Chris Sanders

I’ve always thought that the concept of a honeypot was one of the most fascinating things in information security. If you aren’t familiar with honeypots, they are basically traps used to detect or deter attackers on a network. They typically come in two forms; low interaction and high interaction. A low interaction honeypot is software that emulates a set number of services that may run on a computer. When an attacker connects to a low interaction honeypot, he/she will be able to interact with that service on a limited basis, and that interaction will be logged. A high interaction honeypot is more robust and emulates all aspects of an operating system. This is most often a deployed operating system running a number of legitimate services with an extensively level of logging enabled. The thing both of these implementation methods have in common is that the honeypot doesn’t actually contain real data. Should an attacker compromise either type of honeypot, there is no real direct risk of critical data being exposed when deployed properly.

Almost every single honeypot implementation I’ve seen deployed is for research purposes. There isn’t anything wrong with a research honeypot, after all, I run a couple myself (at home) and have learned a lot from it. However, I think there is a lot of operational value that can be gained from deploying honeypots in production environments. I wanted to discuss, at a high level, a few of these strategies and the benefit that can be gained from them.

Honeypots for Prevention

There has been a fair amount of talk recently about security mechanisms designed to drive up the cost of exploiting a network by increasing the time it takes to do so. As a matter of fact, Adobe’s Senior Directory of Product Security and Privacy, Brad Arkin, even recently said that “My goal isn’t to find and fix every security bug. It’s to drive up the cost of writing exploits. We invest a lot of time in building up mitigations that increase the cost and complexity of writing exploits that will become reliable.” Of course, Arkin was referring to the exploitation of software, but the concept still applies to the network side of the house. I’m still a firm believer that your detection capability is still the most important because prevention eventually fails, but if you can drive up the cost of exploiting a network this has the potential to deter some attackers. At a minimum deterring attacks of opportunity can be achieved if you can increase the time cost of exploiting a network, and this may even work to deter attacks of choice as well.

Honeypots can do this by adding to the frustration factor. I see a couple of ways this can be done. The first of which is to utilize a  large number of low interaction honeypots with varying configurations. The important thing here is to vary their configurations as much as possible in order to prevent an attacker from characterizing them and automating them out of their window of visibility. For instance, if you deploy twenty honeypots and they all have ports 22, 80, and 3306 open and all provide the same responses to banner grabs, an attacker is going to be able to correlate this pretty quickly and will simply scan and exclude those hosts from his list of potential targets. The other method for preventive use is to deploy a significant number of high interaction honeypots. This requires a significant time investment, but the right configuration can cause an attacker to waste a significant amount of time in the right places. Again, this strategy isn’t going to prevent aggressive adversaries from reaching their goal, but it will drive up the time cost of lesser determined foes.

Honeypots for Attack Sense and Warning

This is the sacrificial lamb approach to honeypot deployment. In this scenario, honeypots are deployed based upon trust zones within your network. There are different strategies for outlining trust barriers but on a simple network you might define a low trust zone within a wireless or user space network segment, a medium trust zone in a DMZ, and a high trust zone within a server farm network segment. In that sort of topology, all three zones would contain honeypots configured with security comparable to the next step lower. The idea here is that the honeypot should be slightly more vulnerable to attack than everything else in the zone that it is currently in. This configuration provides value in a couple of ways. First, if a honeypot gets compromised, it will likely serve as a warning that other assets within that trust zone may be compromised soon as well, if they aren’t already. Taking this one step further, it is often logical to assume that if a lower trust zone honeypot becomes compromised, the next highest trust zone may be the next target. Depending on how the network is setup, if a higher trust zone includes a honeypot that gets compromised, it could mean that all of the trust zones below it could also have fallen victim to the adversary. This whole model relies on a lot of assumption, but that is the space AS&W operates in.

Honeypots for Detection Related to Critical Assets

I’m a big fan of target-based IDS deployment where instead of deploying a single IDS to your network perimeter, you user more focused IDS’s with finer tuned rule sets and place them closer to organizationally critical assets. This allows for better use of resources across the board as it usually requires less beefy hardware and ensures your analysts won’t see nearly as many false positives. For instance, if your critical data is housed in SQL servers on a single network segment, then deploy an additional IDS to that segment and only utilize SQL focused signatures there rather than on the perimeter IDS. This also allows you to prioritize IDS sensors so that alerts generated by sensors in high priority areas are given priority when it comes to investigations.

I think the same concept of target-based deployment can be tied to Honeypot deployments in the protection of critical assets. If your organization has prioritized their assets (and they should have), then the general idea behind target based honeypot deployment for the purpose of detection would be to configure and deploy honeypots that are virtually identical to the critical servers. This means that they should be running the same services,  talking to the same hosts, and vulnerable to the same types of attacks. The thought here is that if the critical server gets compromised, then so should the honeypot, and vice verse. This is valuable because it isn’t always feasible to log everything on a production server based upon its volume of traffic. This applies to both host based and network based logging. Utilizing an identically configured honeypot that doesn’t see the same amount of utilization allows you to use more aggressive logging, which may allow you to gain more visibility into an attackers movements. This can provide value in helping you determine exactly how an attacker has compromised a system, what they are utilizing the same for, if there is particular data they may be after, and if they have compromised any other systems on the network.

Reverse Honeypots for Intelligence Collection

Although the concept of a reverse honeypot is a bit radical, it really appeals to me considering the industry I work in. The concept of a traditional honeypot is that in which you fill a pot with honey and hope the attacker gets attracted to the honey and sticks his hand in the pot. A reverse honeypot is where you throw some honey in the direction of a target in such a manner as to leave a trail back to the source. The idea being that the target will notice, follow the trail, see the pot, and stick his hand in. In more practical terms, this means that you would attempt to attack a target elsewhere on the Internet. This attack doesn’t necessarily have to be successful and it may just constitute something as simple as a port scan or something as overt as a DoS attempt. During these attacks, no masking of your source IP address should occur and no third party hop points would be used, thus meaning that the target would see your true IP address when reviewing logs of your attack on his network. Given the nature of your target, this may result in his curiosity being peaking and him reciprocating your attack back at you in another form. Of course, within your network you have several vulnerable honeypots of varying interaction levels waiting for the target.

This type of honeypot is solely for the purpose of target based intelligence gathering, but has the potential to be very effective. First and foremost, should the target scan or attack your network you should be able to capture some of the tools, techniques, and procedures (TTPs) that he is using. This type of intelligence can help in recognizing, characterizing, or attributing other computer network exploitation activity to this attacker and may also lend to better detection techniques in the future. One more added value which is incredibly attractive in the modern threat landscape is the identification of hops points. Although you very purposefully did not mask your true source IP address, the attacker may choose to do so. It’s incredibly common for attackers to compromise other hosts elsewhere on the Internet to launch their attacks from, but it’s also common that they will reuse these same hop points for an extended amount of time. If you can identify these hop points then you can use that information to attribute the attacks to a particular operator or group. This is extremely valuable. Of course, this type of activity should be done from non-production networks, because it’s very possible that you might lure an attacker into launching a large scale DDoS attack on your network 10,000 bots strong.

Conclusion

I think there is a lot of room for operationalizing honeypots in production environments. The major factors prohibiting this are a lack of research in this area and a lack of production-grade tools for implementing these techniques. Unfortunately, we are still in a time that IDS is having trouble gaining traction because of the cost it entails, so a future where honeypots can be deployed for the purpose of enhancing network security seems far off. Don’t be surprised however, if you seen a job posting five years down the road for “Honeypot Administrator”. I know I’d have one if I could.

I was going back through some old bookmarks when I stumbled upon on a post by Richard Bejtlich from 2007 entitled “NSM and Intrusion Detection Differences“. In this article, Richard discussed the concept of ‘immaculate collection’ versus ‘immaculate detection’. Richard’s article references IDS developers desiring immaculate detection while NSM practitioners typically vie for immaculate collection. Given this, I posed the following question to several of my colleagues: Which is more important, collection or detection?

 

The question itself is open to a bit of interpretation, but my group was split about 60/40 favoring collection over detection. I tend to agree with that majority, although the minority had some valid points as well.

 

Those favoring detection argued that a mountain of data, no matter how eloquently collected, is useless without some level of detection capability. Additionally, most in this camp agreed that your detection capability shapes how you perform collection. A few even made the point that they considered collection to be a function of network operations, and not NSM. I can’t disagree with the first of these arguments, but I’m opposed to the other two. I’ll address the argument of whether or not detection shapes collection here.

 

When I think about NSM, I typically think of it in three phases: collection, detection, and analysis. Collection is the gathering and parsing of relevant network security data, and it often performed by a combination of hardware and software. Detection is the process of finding anomalies in collected data that may represent a potential intrusion. Detection is most often done by software, but can be done by humans to a lesser extent. Analysis is the review and investigation of alert data generated during detection. Analysis is typically (and most effectively) done by humans.

 

 

Phases of Network Security Monitoring

 

 

The key takeaway from these three phases is that they form a cycle rather than a beginning to end process. Collected data feeds the detection capability, and the alert data generated from detection feeds the analysis process. What makes this process cyclical is that the investigation and research performed during the analysis process is used to define and shape what data you are collecting.

 

That said, I argue that collection is the most important phase of network security monitoring for a couple of reasons:

 

Detection Depends on Collection

Abraham Lincoln was quoted in saying that if you were to give him six hours to chop down a tree, he would spend the first four hours sharpening his ax. This analogy fits perfect here, because no matter how much thought you put into your detection tools, they are utterly useless if they aren’t digesting the right data. That nice beefy Snort sensor might just be wasting cycles if you’ve placed it on the wrong side of your firewall. Detection fails if collection isn’t done well.

 

Analysis Also Depends on Collection

I hate using the needle in the haystack analogy, but if the hay is covered in manure then you sure aren’t going to want to  spend all of that time digging through it. A human analyst interprets alert data provided by a detection mechanism and then goes out and collects more data in an effort to support his/her investigation. If this data isn’t being collected in an easily retrievable and digestible format then analysis fails. An IDS signature might tell me that a potential attacker is attempting SQL injection on my public facing web server but if I’m not collecting PCAP data and my web server/database logs aren’t accessible then I’m going to have a really hard time finding out if the attack is actually successful.

 

Analysis Feeds Collection Moreso than Detection

I’ve served in the role where I’m the guy creating the detection tools and also in the role where I’m the guy analyzing the alerts generated by the detection tools. It is absolutely true that in some cases collection software/hardware is designed and configured in such a way that it provides data in the appropriate format to a detection tool. This might lead someone to the conclusion that it is detection shaping the collection, but that argument is only made seeing a narrow view of the entire thought process. It is actually the analysis of previous alert data that typically has identified the need for the detection tool that is being created. Remember that detection is most often a task performed by software and it is analysis that is performed by individuals. Software doesn’t identify needs, people do.

 

 

Again, I think this is one of those questions that may or may not have a right answer, but for my two cents, if you gave me six hours to find the bad guys, I’d spend the first four making sure I collected the right data.

 

 

There are a lot of things that the industry does well when it comes to network security monitoring (NSM). For instance, I tend to think that we have data collection figured out reasonably well. I also think that signature-based intrusion detection is a really well developed science. However, with NSM having only existed for a short period of time there are several facets of it that aren’t too well defined. One such aspect is the actual diagnostic method that people use to analyze NSM events. That is, the process an analyst uses to connect the dots between the initial alert and the final diagnosis. In this article I’m going to discuss the use of a common medical diagnostic method called differential diagnosis and how it can be applied to NSM.

 

Understanding Normal

The first thing that was ever taught to me when I started my career as an NSM analyst was that if you know what normal looks like, then you can determine what is bad. I trusted in this concept for many years and even taught it to others. As true as this statement may be, I believe it is relied on entirely too much. This is primarily due to a failure in separating the collection, detection, and analysis processes.

 

Collection centers on the hardware and software used to collect NSM related data. Consider the collection of full content packet capture (PCAP) data. The use a network tap and DaemonLogger allow you to store this data on disk so that it may be used for the identification and analysis of network security related events. Collection occurs with a combination of hardware and software.

Detection is the process by which collected data is examined and anomalies are identified, typically through some form of signature, anomaly, or statistically based detection. Snort is software that is an example of signature-based intrusion detection that compares collected network traffic to signatures of known malicious activity in an effort to perform pattern matching to determine if something bad has occurred. Detection is typically software focused.

Analysis is what occurs when a human interprets the results of the output of an identification tool. Although Snort may detect a pattern match in a communication sequence and generate an alert, it is a human who is ultimately responsible for reviewing the alert and investigating it to an end determination on its validity. The key concept here is that analysis is human focused.

 

With those three terms more clearly defined and distinctions drawn, it would stand to reason that the concept of knowing what normal looks like in order to determine what is bad is actually more relevant to detection than analysis. Realistically speaking, it’s not feasible in the modern state of network computing to be well versed in every aspect of normal communications. Although some traffic patterns may remain fairly static, the open nature and loose standards that govern network communication protocols result in a constant evolution of traffic patterns. Don’t be mistaken, this is still an important concept that must be incorporated into the analytic approach, it’s just not strong enough to stand on its own as the singular concept new analysts should be taught. Knowing what normal looks like is best used when analyzing specific facets of a potential breach rather than as a holistic method to classify all network traffic you may be capturing.

 

A Differential Approach

The general goal of an NSM analyst is to digest the alerts generated by various detection tools and investigate multiple data sources and perform relevant tests and research to see if their findings represent a network security breach. This is very similar to that of a physician, whose goal is to digest the symptoms a human presents and investigate multiple data sources and perform relevant tests and research to see if their findings represent a breach in the person’s immune system.  Both practitioners share a similar of goal of connecting the dots to find out if something bad has happened and/or is still happening.

Although NSM has only been around a short while, medicine has been around for centuries. This means that they’ve got a head start on us when it comes to developing their diagnostic method. One of the most common diagnostic methods used in clinical medicine is one called differential diagnosis. If you’ve ever seen an episode of “House” then chances are you’ve seen this process in action. The group of doctors will be presented with a set of symptoms and they will create a list of potential diagnosis on a whiteboard. The remainder of the show is spent doing research and performing various tests to eliminate each of these potential conclusions until only one is left. Although the methods used in the show are often a bit unconventional they still fit the bill as a part of the differential diagnosis process.

The differential method is one based upon a process of elimination. It consists of five distinct steps, although in some cases only two will be necessary. The differential process exists as follows:

1. Identify and list the symptoms
   
   In medicine, symptoms are typically initially conveyed verbally by the individual experiencing them. In NSM, a symptom is most commonly in the form of an alert generated by some form of intrusion detection system or other detection software. Although this step focuses primarily on the initial symptoms, more symptoms may be added to this list as additional tests or investigations are conducted.

 

2. Consider and evaluate the most common diagnosis first
   
   A statement every medical student is taught in their first year is “If you hear hoof beats, look for horses…not zebras.” This is to state to that the most common diagnosis is likely the correct one. As a result, this diagnosis should be evaluated first. The analyst should focus his investigation on doing what is necessary to quickly confirm this diagnosis. If this common diagnosis cannot be determined to be true during this initial step then the analyst should proceed to the next step.

 

3. List all possible diagnosis for the given symptoms
   
   The next step in the differential process is to list every possible diagnosis based upon the information currently available with the initially assessed symptoms. This step requires some creative thinking is often most successful when multiple analysts participate in generating ideas. Although you may not have been able to completely confirm the most common diagnosis in the previous step, if you weren’t able to rule it out completely then it should be carried over into the list generated in this step. Each potential diagnosis on this list is referred to as a candidate condition.

 

4. Prioritize the list of candidate conditions by their severity
   
   Once a list of candidate conditions is created a physician will prioritize these listing the condition that is the largest threat to human life at the top. In the case of an NSM analyst you should also prioritize this list, but the prioritization should focus on which condition is the biggest threat to your organizations network security. This will be highly dependent upon the nature of your organization. For instance, if “MySQL Database Root Compromise” is a candidate condition then a company whose databases contains social security numbers would prioritize this condition much higher than a company who uses a simple database to store a list of its sales staffs on-call schedule.

 

5. Eliminate the candidate condition, starting with the most severe
   
   The final step is where the majority of the action occurs. Based upon the prioritized list created in the previous step the analyst should begin doing what is necessary to eliminate candidate conditions, starting with the condition that poses the greatest threat to network security. This process of elimination requires considering each candidate condition and performing tests, conducting research, and investigating other data sources in an effort to rule them out as a possibility. In some cases investigation on one candidate condition may effectively rule out multiple candidate condition, speeding up this process. Alternatively, investigation of other candidate conditions may prove inconclusive leaving one or two conditions that are unable to be definitively eliminated as possibilities. This is acceptable however as sometimes in network security monitoring (as in medicine) there are anomalies that can’t be explained that require more observation before determining a diagnosis. Ultimately, the goal of this final step is to be left with one diagnosis so that either the incident handling process may begin or the alert can be dismissed as a false positive. It’s very important to remember that “Normal Communication” is a perfectly acceptable diagnosis, and will be the most common diagnosis an NSM analyst arrives at. I also find that remembering that all packets are good unless you can prove they are bad is an important concept to remember during this step.

 

 

Let’s consider this process with a couple of broad case scenarios.

 

Scenario 1

Step 1: Identify and List the Symptoms

Symptoms:

• Internal host appears to be sending outbound traffic to a Russian IP address
• The traffic is occurring at regular intervals, every 10 minutes
• The traffic is HTTPS over port 443, and as such is encrypted and unreadable

Step 2: Consider and Evaluate the Most Common Diagnosis First

It’s been my experience that most entry level analysts will see these symptoms and automatically think that this machine is infected with some form of malware and is phoning home for further instructions. Those analysts tend to key in on that fact that the traffic is going to a Russian IP address and that it is occurring at regular 10 minute intervals. Although those things are worth noting (I wouldn’t have listed them if they weren’t), I don’t buy into the malware theory so easily. I believe entirely too much emphasis is placed on the geographic location of IP addresses, so the fact that the remote IP address is Russian means little to me. Additionally, there are a whole variety of normal communication mechanisms that talk on regular periodic intervals. This includes things like web-based chat, RSS feeds, web-based e-mail, stock tickers, software update processes, and more. Operating on the principal that all packets are good unless you can prove they are bad, I think the most common diagnosis here is that this is normal traffic.

That said, how we can confirm this potential diagnosis? Confirming something is normal can be hard. In this particular instance we could start with some open source research on the Russian IP. Although it’s located in Russia it still may be owned by a legitimate company. If we were to look up the host and find that it was registered to a popular AV vendor we might be able to use that information to conclude that this was an AV application checking for updates. I didn’t mention the URL that the HTTPS traffic is going to, but quickly Googling it may yield some useful information that will help you determine if it is a legitimate site or something that might be hosting malware or some type of botnet C2. Another technique would be to examine the host physically if you have ready access to it in an effort to see if any processes are launched on the machine at the same intervals the traffic is occurring at.

Let’s assume that we weren’t able to make a final determination on whether or not this was normal communication.

Step 3: List all Possible Diagnosis for the Given Symptoms

*There are obviously more candidate conditions in the realm of possibility, but for this and the other scenario I’ve kept it to some of the more common ones for the sake of brevity.

Candidate Conditions:

• Normal Communication
  We weren’t able to rule this out completely in the previous step so we carry it over to this step.

 

• Malware Infection / Installed Malicious Logic
  This is used as a broad category. We typically don’t care about the specific strain until we determine that malware may actually exist. If you are concerned about a specific strain then it can be listed separately. Think of this category as a doctor listing “bacterial infection” as a candidate condition knowing that they can further narrow it down later.

 

• Data Exfiltration from Compromised Host
  Potential that the host could be sending proprietary or confidential information out. This sort of thing would likely be part of a coordinated or targeted attack.

 

• Misconfiguration
  It’s well within the realm of possibilities that a system administrator fat-fingered an IP address and a piece of software that should be trying to communicate periodically with an internal IP is now trying to do so with a Russian IP. This is really quite common.

 

Step 4: Prioritize the List of Candidate Conditions by their Severity

These priorities are fairly generalized since they are dependent upon your organization.

Priority 1: Data Exfiltration from Compromised Host

Priority 2: Malware Infection / Installed Malicious Logic

Priority 3: Misconfiguration

Priority 4: Normal Communication

Step 5: Eliminate the Candidate Conditions, Starting with the Most Severe

Priority 1: Data Exfiltration from Compromised Host

This one can be a bit tricky to eliminate as a possibility. Full packet capture won’t be of the most assistance here since the traffic is encrypted, but if you can create some statistics from this traffic, or better yet, if you have netflow available, you should be able to determine the amount of data going out. If only a few bytes are going out every then minutes than it’s likely that this is not data exfiltration. The host based research you did earlier on the Russian IP address may also provide some value here in determining the reputation of this host. It would also be of value to determine if any other hosts on your network are talking to this IP address or any other IPs in the same address space. Finally, baselining normal communication for your internal host and comparing it with the potentially malicious traffic may provide some useful insight.

Priority 2: Malware Infection / Installed Malicious Logic

At this point the research you’ve already done should give you a really good idea on whether or not this condition is true. It will be likely that by examining the potential for data exfiltration you will rule this condition out as a result, or will have already been able to confirm it to be true.

Priority 3: Misconfiguration

This condition can best be approached by comparing the traffic of this host against the traffic of one or more hosts with a similar role on the network. If every other workstation on that same subnet has the same traffic pattern, but to a different IP address, then it’s likely that the wrong IP address was entered into a piece of software somewhere proving that a misconfiguration exists. Having access to host-based logs can also be useful in figuring out if a misconfiguration exists since they might exist in Windows or Unix system logs.

Priority 4: Normal Communication

If you’ve gotten this far, then the diagnosis of normal communication should be all that remains on your list of candidate conditions.

Concluding a Diagnosis

At this point you have to use your experience as an analyst and your intuition to decide if you think something malicious is really occurring. If you were able to complete the previous analysis thoroughly, then operating on the assumption that all packets are good unless you can prove they are bad would mean your final diagnosis here should be that this is normal communication. If you still have a hunch something quirky is happening though, there is no shame in monitoring the host further and reassessing once more data has been collected.

 

Scenario 2

Step 1: Identify and List the Symptoms

Symptoms:

• A web server in our DMZ is receiving massive amounts of inbound traffic
• The inbound traffic is unreadable and potentially encrypted or obfuscated
• The inbound traffic is coming to multiple destination ports on the internal host
• The inbound traffic is UDP based

Step 2: Consider and Evaluate the Most Common Diagnosis First

With the amount of traffic being received by the internal host being very large and the packets using the UDP protocol with random destination ports, my inclination would be that this is some form of denial of service attack.

The quickest way to determine whether something is a denial of service is to assess the amount of traffic being received compared with the normal amount of traffic received on that host. This is something that is really easy to do with netflow data if you have it available. If the host is only receiving 20% more traffic than it normally would then I would consider other alternatives to a DoS. However, if the host is receiving ten or one hundred times its normal amount of traffic then DoS is very likely and almost a certainty.  It’s important to remember that a DoS is still a DoS even if it is unintentional.

Once again, for the sake of this scenario we will continue as though we weren’t able to make a clear determination on whether or not a DoS condition exists.

Step 3: List all Possible Diagnosis for the Given Symptoms

Candidate Conditions:

• Denial of Service
  We weren’t able to rule this out completely in the previous step so we carry it over to this step.

 

• Normal Communication
  It doesn’t seem incredibly likely, but there is potential for this to be normal.

 

• Misdirected Attacks
  When a third party chooses to attack another they will often spoof their source address for the sake of anonymity and to prevent getting DoS’d themselves. This will result in the owner of the spoofed IP they are using seeing that traffic. This web server could be seeing the effects of this.

 

• Misconfigured External Host
  A misconfiguration can happen on somebody else’s network just as easily as it could on yours. This misconfiguration could result in an external host generating any number of types of traffic and sending them to the web server.

 

• SPAM Mail Relay
  The server could be misconfigured or compromised in a manner that allows it to be used for relaying SPAM across the Internet.

 

Step 4: Prioritize the List of Candidate Conditions by their Severity

Priority 1: Denial of Service

Priority 2: SPAM Mail Relay

Priority 3: Misconfigured External Host

Priority 4: Misdirected Attacks

Priority 5: Normal Communication

Step 5: Eliminate the Candidate Conditions, Starting with the Most Severe

Priority 1: Denial of Service

We’ve already gone through the paces on this one without being able to identify that it is the definitive diagnosis. Even though this is the most severe we would have to proceed to attempt to eliminate other candidate conditions to help in figuring out if a DoS is occurring. Of course, depending on the effect of the attack it may make the most sense to contain the issue by blocking the traffic before spending more time investigating the root cause.

Priority 2: SPAM Mail Relay

This one is relatively easy to eliminate. If the server was being used as a mail relay then you would have a proportionate amount of traffic going out as you do going in. If that’s not the case and you don’t see any abnormal traffic leaving the server then it is likely that it is not relaying SPAM. If the web server is also running mail services then you can examine the appropriate logs here as well. If it is not supposed to be running mail services you can examine the host to see if it is doing so in an unauthorized manner.

Priority 3: Misconfigured External Host

This one is typically pretty tricky. Unless you can identify the owner of the IP address and communicate with them directly then the most you can hope to do is block the traffic locally and/or report abuse at their ISP level.

Priority 4: Misdirected Attacks

This is another tricky one along the same lines as the previous candidate condition. If it’s an attacker somewhere else whose antics are causing traffic redirection to your server then the most you can do is to report the issue to the ISP responsible for the IP address and block the traffic locally.

Priority 5: Normal Communication

This doesn’t seem likely, but you can’t say this for sure without baselining the normal traffic for the host. Compare its traffic at similar times on previous days to see if you can draw any conclusions. Is the pattern normal and it’s just the amount of traffic that anomalous? Is it both the pattern and the amount that’s anomalous? Does the server ever talk to the offending IP prior to this?

 

Concluding a Diagnosis

In this scenario, it’s very possible that you are left with as many as three candidate conditions that you cannot rule out. The good thing here is that even though you can’t rule these out, the containment and remediation methods would be the same for all of them so you still have gotten to a state of diagnosis that allows the network to recover from whatever is occurring. If the amount of traffic isn’t too great then you may not need to block the activity and you may be able to monitor it further in order to attempt to collect more symptoms that may be useful in providing a more accurate diagnosis.

 

Conclusion

I’ve spent quite a bit of time doing analysis with this differential approach and also reviewing previous investigations post-mortem while applying these concepts and I’ve been really pleased with my findings. I think that if you are struggling with being able to grasp a firm analytical method then this may be a great one to start with. I’m not entirely sure that the differential method is appropriate for all organizations, but just as with medicine, there are competing approaches and I hope to examine more of those in the future so that I can draw more comparisons between the medical field and NSM. If you have any scenarios in which you’ve used this differential approach (for better or for worse), I’d love to hear about them.

In the realm of network security monitoring and intrusion analysis we are all slaves to our data. Typically speaking, we rely on two different types of data at the network layer; full content data (PCAP) and session data (Netflow). Both are pretty easy to generate given the right sensor placement, and there are a lot of great resources out there for learning how to get good value out of the data. That said, they do each have their own shortcomings as well.

 

Session Data (Netflow)

Netflow is a standard form of session data that details the ‘who, what, when, and where’ of network traffic. I tend to equate this to the call records you’ll see on your monthly cell phone bill.

 

 

 

 

Figure 1: Partial Netflow Records Exported from SiLK

 

The best thing about netflow is that it provides a lot of value with minimal disk storage overhead. It’s really a lot of bang for your buck. Most commercial grade routers and firewalls will generate netflow, and there are a lot of free and open source tools, such as SiLK, that can be used to generate and analyze netflow as well. There is even a yearly conference called FloCon where people get together and talk about cool things you can do with netflow. The only real downside to netflow data is that it doesn’t paint a complete picture, so it’s often best used as a complement to full content data.

 

Full Content Data (PCAP)

If netflow session data is equivalent to a call log, then full content data in the form of PCAP is just like having a full recording of all of your calls.

 

 

 

Figure 2: PCAP Data Investigation with Wireshark

 

 

The PCAP format has become very universal and can be collected and analyzed with a variety of free and open source applications like Dumpcap, Tcpdump, Wireshark, and more. A lot of the more popular intrusion detection systems, such as Snort, use the PCAP format as well. As an analyst, having PCAP data available tends to make the analytical process a dream come true as it provides the highest level of context when investigating an anomaly. The primary downside to full content data is that it has an incredibly high disk storage overhead, which prevents most organizations from collecting and storing any reasonable amount of it. In my experience, the organizations that are capable of collecting and storing PCAP can only measure the amount stored in hours, rather than days. In addition to this, unless you have an idea of what you are looking for within a reasonable time range, it can be a bit difficult to locate things as well, somewhat impeding flexibility in analysis.

 

Application Layer Metadata

The concept of application layer metadata originally presented itself to me in a discussion regarding additional data types that are useful within the network security monitoring function that were sort of a happy medium in between session data and full content data. It didn’t take a lot of number crunching to find that on most of the networks we monitored, the vast majority of the traffic was the application layer data of a few common protocols. The largest of these was HTTP, followed by the other usual suspects; SSL, DNS, and SMTP.

Starting with a couple of these protocols as a baseline, we quickly realized that we could save ourselves a lot of disk storage overhead by actually eliminating the stuff we didn’t need. There are an unlimited number of ways to do this, but we wanted to go with the keep it simple philosophy, so we started by using tcpdump to read in our PCAP data, outputting the ASCII formatted data to a file. Then, we ran the Unix strings command on that file to get read of any binary data that we couldn’t read anyways. We weeded out a few more things that we didn’t want through a magical combination of SED and AWK, added in the appropriate timestamps, formatted the data a bit prettier, and we had achieved our goal.

The end result of a reasonably small bash script was the ability generate application layer metadata in the form of something we call a Packet String, or PSTR file (pronounced pee-stur).  The script is ideally designed to run as a cron job where it parses continually generated PCAP files in order to generate accompanying PSTR files.

 

 

 

 

 

 

 

 

Figure 3: Sample PSTR Data

 

You can download the bash script that generates this data from PCAP files here. This is provided as a simple proof of concept and takes an input PCAP file and generates an output PSTR file. Now that we’ve got application layer metadata being generated in the form of PSTR files, let’s take a look at a few use cases.

 

Using PSTR as a Data Source

The original goal of generating PSTR files was to provide a data format with a low disk storage overhead that provided value to analysts as a secondary NSM data source. In a typical workflow, analysts would take an input from a detection capability, such as an IDS, and then PSTR would be another data source available to the analyst in order to provide supporting evidence in the analysis of a potential event or incident. I’ve written a few use cases here. Some of these are theoretical, but others are examples of actual things that have happened since implementing the PSTR data type.

 

Malware Infection Use Case

Let’s look at an example in which we’ve just received an alert from our IDS stating that an internal system has been detected as exhibiting symptoms of infection. The signature that fired did so because it saw a malicious GET request associated with a known botnet C2 server. The host was examined, and it appeared as though the GET request matches what is expected as a result of the signature that fired, so were able to determine with a pretty reasonable certainty that this box was infected.

Upon closer examination, we also notice that the infected host was also sending an HTTP POST with a very unique string. This looked like it might be an indicator of malicious activity, but it wasn’t something that any of your existing signatures fired on. In this case, an analyst was very quickly able to use GREP to quickly find other instances of this same string within the HTTP header data of all traffic on our monitored networks. PSTR data proved to be incredibly useful in finding other infected boxes across multiple networks.

 

Targeted Phishing Use Case

As a theoretical example, consider another example where several users have contacted your security team because they’ve received a very suspicious e-mail that seems to be targeted specifically at your company. This e-mail mentions a payroll adjustment and asks the client to access the provided link and log in with their employee ID number and password.

After examining the e-mail, you’ve determined that it has been sent from a spoofed e-mail address and that it uses a slightly modified subject line that is unique to each recipient. You’ve also noticed that based upon the reports you’ve received from users, these e-mails have come in over the past several weeks. One of the things you would want to do in this case would be to find who within your organizations received this e-mail. The purpose of this is to be able to warn the users not to click the link in the e-mail and also in hopes that you might be able to find a pattern as to why the selected recipients were chosen (access to certain systems, high profile employees, etc).

Typically, you might search through Exchange or Postfix logs to see if you can find who the recipients were. This of course relies on your organization having adequate logging and retention of those logs. The unique nature of the string however, makes it difficult to query these data sources. Using PSTR data, you can write a quick regular expression to match the semi unique subject lines and run a very quick query that will give you these results.

 

Using PSTR as a Detection Capability

The one thing we didn’t really anticipate when we created the PSTR file type was its use as a second level detection capability. When I refer to second level analysis and detection, I’m referring to moving past near real-time detection to the point in which analysts start reviewing traffic retrospectively to find things that signatures don’t catch. This often involves statistical and anomaly based detection with large data sets. This is something PSTR is perfect for.

 

User Agent Use Case

The user agent field within an HTTP header is always a good source for catching the low hanging fruit when it comes to malware infections on a network. Lots of malware will use a custom value in this field that deviates from standard browser identifying strings. The detection technique I’ve seen most commonly deployed to catch these types of malware infections at the network level rely on IDS/IPS signatures. As a matter of fact, if you subscribe to the common popular Snort rule sets then probably are using their user agent rules to detect known bad user agents.
The only problem with that detection scenario is that malware is now being generated at a rate much faster than the AV and ISD companies can keep up with. As a result, there are a LOT of malicious user agents out there that aren’t accounted for. In addition to this, some malware uses randomly generated user agent strings, meaning it’s much more difficult to write adequate signatures for detection.

One day, one of our analysts started playing around with PSTR data and wrote a quick script to parse all of the PSTR data for a given site, grab all of the user agent strings, and sort those by uniqueness. As expected, there were thousands of occurrences of the typical Firefox and Internet Explorer user agents, but what was really interesting was that there were several user agent strings only seen a handful of times that didn’t correlate to any particular known browsers. After a bit more analysis, we ended up finding quite a few machines that were infected with malware variants using these custom user agent strings. This one was a home run.

 

E-Mail Subject Use Case

The previous use case got us to thinking about other common fields with application layer metadata that we could do the same types of analysis on. One such field was the e-mail subject line field.  We modified our user agent parsing code to look at all PSTR data related to e-mail subject lines, and again had some very cool results.

Instead of most of the distribution being focused on one or two unique strings like we saw with user-agents, we saw that the distribution was spread very widely across thousands of different subject lines. This was expected, since most e-mails have a unique subject line. What interested us here however, was that we had a few subject lines that were used in excess. The first item of interest we found was that some sites had misconfigured applications that were mailing things to places they shouldn’t go, which was worth pursuing and getting fixed. We also found a user who was e-mailing all of his work documents to himself as a scheduled task every night, which was a policy violation.

This was all found with a very basic level of analysis, and it had some very real and useful results.

 

Additional Analytic Capabilities

The thing I love about this data format is that we can store a lot of it and it’s really quick to search through. With those things being true, there are a ton of things that can be done with it from a detection standpoint. A few immediate ideas include:

• Searching for unique values with HTTP, SMTP, DNS, and SSL headers

This is what we did in most of these examples. You can really quickly sort through the unique values within certain fields and find the outliers that warrant additional investigation.

• Byte entropy of certain fields to locate encrypted data where it shouldn’t be

It’s a common tactic to exfiltrate encrypted data through commonly used channels in an effort to hide in plain sight. Performing entropy calculations on GET and POST requests in an effort to find encrypted data would be a good way to detect where this might be occurring.

• Checking the length of certain fields for anomalies

You can do some statistical analysis and determine that certain fields will often have values that have a length falling in a particular range. Using that, you can flag on outliers that are far too short or too long in order to look for anomalies. I’ve seen good success in doing this with the various HTTP header fields, e-mail subject lines, and SSL certificate exchanges.

• Enumerating Downloads of Particular File Types

There is a great deal of value to being able to list all of the executables or PDF files downloaded within a certain time span. This is pretty easily achievable really quickly with analysis of HTTP header data within PSTR files.

Of course, all of these things CAN be done on PCAP data as well, but it would take significantly more processing power and it’s likely that you can’t store enough PCAP data at a given time to make it worth useful.

 

Conclusions

The concept of collecting and storing application layer metadata isn’t anything revolutionary. As a matter of fact, the idea isn’t even completely original as I’ve encountered other organizations that do similar things. There are even some commercial products that do this as well. However, I do know that nobody is sharing their methods and code with the world, which is the purpose of this post. Analysts live and die by their data feeds, and I think application layer metadata in whatever form it takes has its place amongst the other primary network data types. You can download the proof of concept code to generate and parse PSTR files here. I’m excited to see this data format evolve as we find more and more use for it. Look for more updates on this front as the code base continues to advance.

 

 

* A special thanks to my colleague Jason Smith for doing most of the legwork on writing the POC code.

 

I’m excited to announce that I, along with my good friend and colleague Jason Smith, will be speaking at the DHS US-CERT Government Forum of Incident Response and Security Teams (GFIRST) Conference in August. The conference is being held the week of August 7-12 at the Gaylord Opryland Hotel in Nashville, TN. We will be speaking at 1 PM on Wednesday, August 10th.

Title: Real-World Security Scripting

Abstract:

Scripting serves several purposes within a security operation center (SOC). You can write scripts to automate common tasks, to perform actions on large amounts of data, or to perform calculations and correlation on data sets. Given a bit of time an analyst can do great things with an interpreter and a little bit of elbow grease. Over the past few years our team has found that a lot of incredibly useful analysis tools can be created with only a minimal amount of programming knowledge.

In this presentation, we are going to educate our audience on how to get started with scripting for SOC related functions. Don’t be fooled though, this isn’t your typical scripting lesson. We aren’t going to ramble on about data types, expressions, and syntax formatting. Instead, we are going to look at real scripts we use in the SPAWAR SOC every single day. We will step through as many scripts as time permits while showing effective methods for automatically parsing netflow data for known malicious hosts, extracting payloads from PCAP files for content or entropy analysis, and more. We will go through the process of creating each script from inception to production.

A few specific scripts we will break down include:

• Updating a snort ruleset across multiple sensors
• Automated reporting of known malicious IP addresses and domains with netflow data
• Extracting the data payloads of packets in PCAP files
• Retrieval and concatenation of PCAP data from multiple sources
• Automatic parsing of netflow data into various graphs for visual traffic analysis

 

The tools covered will be a mix of BASH, PERL, and Python. No prior scripting knowledge is required to gain value from this presentation. We will be providing source code for all of the scripts we are discussing as well as a few extras. As a bonus, we will even provide versions of some of these scripts that can be integrated into Arcsight or other SIEM products to extend their capabilities. At the very least, attendees will walk away with scripts they can implement into their production SOC immediately. The real value of this course however, is a real world crash course in scripting for analyst-centric SOC functions.

 

You can read more about the GFIRST conference at http://www.us-cert.gov/GFIRST/.

 

 

Look forward to seeing you there!

 

I recently found myself in a situation where I had a couple dozen Snort sensors deployed in a network with no commercial software for centralized management. Due to the decentralized nature of the sensor management, one of the bigger headaches was adding new custom rules to all of the sensors. New rules had to be added to each sensor manually into a custom rules file. These rules all existed in a single file, so I wrote a bash script that automates this process. Using this script an analyst only has to add the new rules to a single file and run the script to push it out to all of the other sensors. I thought I’d share that here for those that might get some use from it.

FAQ

What does the script do?

The script does a few simple tasks. It will perform the following actions for each IP in its sensor list:

• Create a backup of the existing custom rules file on the sensor.

• Replace the current rule file with the new rule file.

• Performs a ‘diff’ on the new rule file and the old rule file and places the results in a timestamped log file in /var/log/snortrules.

• Restarts snort on the sensor to ensure the new rules are applied.

What are the requirements for running the script?

In order to execute the script, the following conditions must be met:

• A custom rule file named the same as the custom rule file on the sensor must exist in the directory the script is executed from.

• You must have SSH/SCP connectivity to the servers.

• It is necessary to have permissions to perform the actions described above on the appropriate folders.

Additionally, it helps to have certificate based authentication setup for a service account that can handle actions performed by this script. Otherwise you will have to password authenticate to each sensor.

How do I add in the addresses for my sensors?

The first line of code in the script contains the list of sensor IP addresses. Replace the following with addresses for your sensors, delimited by spaces.

What other variables do I need to modify within the script to match my environment?

There are three main variables in addition to the sensor IP addresses. These are:

• rulepath – The path on the remove server where the custom rules file exists

• customrules – The name of the custom rules file

• user – The user name to use for authentication to the sensor

Can I make modifications to the script?

Absolutely. I’m not a programmer. I’m just a guy who saw a need and wrote something to address it quickly. That said, the script could probably be setup a lot better and do a lot of cool neat things (like error checking). If you find some value you in the script and want to make some modifications or additions to it then by all means do so, I just ask that you reciprocate those changes back to me so everyone can benefit.

 

With all that out of the way, you can download the script here.

I’m once again going to be leading a SANS Mentor session. This time however, I’ll be teaching SEC 503, Intrusion Detection In-Depth in my new home of Charleston, South Carolina. The course will be starting on June 22nd, running once a week for two hours, for ten weeks. The course will be held at Honeywell, on Rivers Avenue in North Charleston.

 

An excerpt from the course description:

Learn practical hands-on intrusion detection and traffic analysis from top practitioners/authors in the field. This is the most advanced program in network intrusion detection that has ever been taught. The emphasis of this course is on increasing students’ understanding of the workings of TCP/IP, methods of network traffic analysis, and one specific network intrusion detection system (NIDS) – Snort. This is a fast-paced course, and students are expected to have a basic working knowledge of TCP/IP in order to fully understand the topics that will be discussed. Although others may benefit from this course, it is most appropriate for students who are or who will become intrusion detection analysts. Students generally range from novices with some TCP/IP background all the way to seasoned analysts. The challenging, hands-on exercises are specially designed to be valuable for all experience levels. If you want to learn the ins and outs of TCP/IP as it relates to security analysis, how to dissect packets at their most basic level, and how utilize NIDS effectively then this is the course for you.

 

If you are pursuing DOD 8570 certification, then the certification paired with this course, the GCIA, will satisfy the requirement for the CND-Analyst designation. This is a great course if you are a government employee or contractor pursuing 8570 compliance, or simply someone working in information security looking to strengthen your defensive technology skills and gain a widely accepted certification in the process.

 

If you are interested in learning more then you can visit the SANS website for this course at: http://www.sans.org/mentor/details.php?nid=24684. Also, feel free to pass around the flyer for this course, which can be viewed here. Also, I can provide some discounts to help offset the cost a bit if you contact me directly.

One of the more important skills in intrusion detection and analysis is the ability to evaluate an IP address or domain name in order to build an intelligence profile on that host. Gathering this intelligence can help guide you to making more informed decisions regarding the remote hosts that are communicating with your network in order to determine if they are of a malicious or hostile nature. I recently wrote a two-part article on collecting threat intelligence for WindowsSecurity.com which describe some methods that can be used to collect threat intelligence on a host or network.

Collecting Threat Intelligence (Part 1)

Collecting Threat Intelligence (Part 2)

I’ve been actively involved in the training and development of intrusion detection analysts for a few years now which includes being a SANS Mentor for SEC 503: Intrusion Detection In-Depth. One thing I find myself constantly doing is trying to evolve my philosophy on effective intrusion detection. While doing this, some themes arise that tend to stay consistent no matter how that philosophy changes. Through that, I’ve written up something I call the “10 Commandments of Intrusion Analysis” which highlight some of those themes that seem to be at the core of what I try to instill in the analysts I train and in my own analysis. They don’t really command you to anything, but there are 10 of them, so the name kind of fits. These may not fit you or your organizational goals or personal style, but they work for me!

1. Analysts, Analysts, Analysts!

The most important thing an analyst can have ingrained into them in their importance. An analyst is the first line of defense. The analyst is sitting in the crows nest watching for the icebergs. It is the analyst who can keep attacks from happening and can stop attacks from getting worse. Most security incidents begin with an analyst providing a tip based upon an IDS alert and end with an analyst putting in new signatures and developing new tools based up on intelligence gained from a declared incident. The analyst is the beginning and the end in information security. The alpha and omega. Okay, maybe that’s a bit dramatic, but the importance of an intrusion analyst can’t be understated.

2. Unless you created the packet yourself, there are no absolutes.

Analysis happens in a world of assumptions and its important to remember that. Most of the decisions you will make are centered around a packet or a log entry and then honed based upon intelligence gathered through research. The fact is that the analyst isn’t the one who generated the traffic, so every decision you will make is based upon an assumption. Don’t worry though; there is nothing wrong with that. Ask your friendly neighborhood chemist or physicist. Most of their work is based upon assumptions and they have great success. The takeaway here is that there are no absolutes. Is that IP address REALLY a known legitimate host? Does that domain REALLY belong to XYZ company? Is that DNS server REALLY supposed to be talking to that database server? There are no absolutes, merely assumptions, and because of that remember that assumptions can change. Always question yourself and stay on your toes.

3. Be mindful of how far abstracted from the data you actually are.

An analyst depends on data to perform their function. This data can come in the form of a PCAP file, an IIS log file, or SYSLOG file. Since most of your time will be spent using various tools to interact with data it’s crucial to be mindful of how that tool interacts with the data. Did you know that if you run Tcpdump without specifying otherwise, it will only capture the first 68 bytes of data in a packet? How about that Wireshark displays sequence and acknowledgement numbers within TCP packets in a relative manner by default? Tools are made by people and sometimes “features” can cloud data and prevent proper analysis. I think both of the features I described earlier are great, but I’m also mindful that they exist so I can see all of the packet data available or view the real sequence and acknowledgement numbers when needed. In a job where reliance upon data is critical, you can’t afford to not understand exactly how tools interact with that data.

4. Two sets of eyes are always better than one.

There is a reason authors have editors, policemen have partners, and there are two guys sitting in every nuclear silo. No matter how much experience you have and how good you are you will always miss things. This is to be expected because different people come from different backgrounds. I work with the government so the first thing I look at when examining network traffic is the source and destination country. I’ve worked with people who have systems administration backgrounds and as a result, will look at the port number of the traffic first. I’ve even worked with people who have a number crunching background who will look at the packet size first. This demonstrates that our experiences shape our tactics a bit differently. This means that the numbers guy might see something that the sysadmin didn’t see or that the government guy might have insight that the numbers guy didn’t. Whenever possible it’s always a good idea to have a second set of eyes look at the issue you are facing.

5. Never invite an attacker to dance.

This is something I’ve believed since the first day I ever fired up a Snort sensor, but IDS guru Mike Poor phrased it best while I was attending one of his SANS classes when he said that you should never invite an attacker to dance. As an analyst its very tempting to want to investigate a hostile IP address a bit beyond conventional means. Trust me, there have been many occasions where I’ve been tempted to port scan a hostile that kept sending me painfully obviously crafted UDP packets. Even more so, any time someone attempts to DOS a network I’m responsible for defending, I wish nothing more than to be able to unleash the full fury of a /8 network on their poor unsuspecting DSL connection. The problem with this is that 99% of the time we don’t know who or what we are dealing with. Although you may just be seeing scanning activity, the host that is originating the traffic could be operated by a large group or even a military division of another country. Even something as simple as a ping could tip off an attacker that you know they exist, prompting them to change their tactics, change source hosts, or even amplify their efforts. You don’t know who you are dealing with, what their motivation is, and what there capabilities are, so you should never invite them to dance.

6. Context!

One word can drastically change the dynamic of your monitoring and detection capabilities. In order to be effective you must have context into the network you are defending. Network diagrams, listings of servers and their roles, breakdowns of IP address allocations, and more can be your best friend. Basically any and everything that can be used to document the assets within the network, how they function, and how they relate to other assets are beneficial in running down anomalous events. Depending upon your role in the organization you may not be in a position to obtain these things and if they don’t already exist you are going to have a heck of a time getting the systems folks to put in the leg work to create them. However, as difficult as this may be, its an effort that’s worth pursuing. Whether you have to present your case to the CIO or just buy your network engineers a case of their favorite adult beverage its ultimately worth the effort.

7. Packets, in a word, are good.

The ultimate argument in life is whether or not people are inherently good or inherently evil.  This same argument can be had for packets as well. You can either be the analyst that believes all packets are inherently evil or the analyst that believes all packets are inherently good. I’ve noticed that most analysts typically start their career as for the former and quickly progress the later. That’s because its simply not feasible to approach every single piece of traffic as something that could be a potential root level compromise. If you do this, you’ll eventually get fired because you spent your entire day running down a single alert or you’ll just get burnt out. There is something to be said for being thorough but the fact of the matter is that most of the traffic that occurs on a network isn’t going to be evil, and as such, packets should be treated innocent until proven guilty.

8. Analysis is no more about tcpdump than astronomy is about a telescope.

Whenever I interview someone for any analyst position that’s above entry level I always ask them to describe how they would investigate a typical IDS alert. I get frustrated when someone gives answers along the lines of “I use  Tcpdump, Wireshark, Network Miner, Netwitness, Arcsight, Xeyes, etc” with no further clarification. Although their are processes and sciences in intrusion analysis, intrusion analysis itself is not a process or a science, but rather an art. If this wasn’t the case then it wouldn’t even be necessary to have humans in the loop when it comes to intrusion detection. An effective analyst has to understand that while different tools may be the most important part of the job, those things are merely pieces of the puzzle. Just like an astronomer’s telescope is just another tool in his arsenal that allows him to figure out what makes the planets orbit the sun, Wireshark is just another tool in an analysts arsenal that allows him to figure out what makes a packet bypass a firewall rule. Start with the science, add in a few tools and processes, stay cognizant of the big picture, keep an attention to detail, and eventually the combination of all of those things and the experience you gain over time will help you develop your own analysis philosophy. It’s at that point you have taken your analysis to the level of an art, and made it so that your worthy enough to not be replaced by a machine.

9. Sometimes, we lose.

No matter how hard you try there will come a point in which the network you are defending gets successfully attacked and compromised. In the modern security landscape its inevitable and there isn’t a lot you can do about it. In these times its likely that the analyst will take the heat over the incident. Because of this, you need to be prepared when it happens. An incident won’t be remembered for how an intrusion occurred, but rather how it was responded to, the amount of downtime that occurred, the amount of information that was lost, and ultimately the amount of money it costs the organization. What recommendations can you make to management to ensure a similar incident doesn’t occur again? What can you show your superiors to explain why the attack wasn’t detected? What shortcomings do your tools have? These are questions that can’t fully be answered until an intrusion has occurred and you have the context of an attack, but you can definitely consider the questions now and have a plan for how your information will be presented to key figures. You will get caught off guard and you will be blind sided, but its important that you don’t appear as such and you keep your game face on. This can make the difference between a promotion and a pink slip.

10. Dig deeper.

At the end of the day you have to have something to rest your laurels on and that has to be the fact that you’ve done your due diligence and that you’ve given your best. My “motto” per se when it comes to intrusion analysis is “Dig Deeper”. A defender has to control 65,535 ports. An attacker has to compromise one. A defender has to protect 10,000 users. An attacker has to deceive one. A defender has to examine millions of packets. An attacker has to hide a malicious payload in one. What can you do to increase your visibility into the data? What proficiency can you develop that gives you that edge against the attacker? You have a hunch that there is more than meets the eye, so what can you do to dig deeper?

I’ve been using Snort has a host-based IDS on my laptop for quite a while now and rather than expanding my attack surface by installing a database server for logging, I am simply logging to the standard flat file format. In this format all snort alerts are logged to an alert.ids file in the C:/Snort/log directory. In previous instances I’ve just reviewed this log frequently but I’ve had the desire for quite some time to have something a bit more realtime. I’m not sure if I’ve found the best solution, but I’m currently using RainMeter to display the most recent Snort alerts on my desktop.


I did run into one problem which had a solution I think others might be interested in. Snort logs the newest alerts it recieves at the bottong of the alert.ids file, which makes gathering the most recent alerts via perl regular expressions a bit of a complicated task. I brought this problem to my analysis team at EWA and Jason Smith, who has just started learning Perl,  developed a script that alleviates this problem. The script takes the last alert in the alert.ids file and places it at the top of a new “parsed” file. The second to last alert in the alert.ids file is then placed as the second alert in the parsed file, and so on and so forth. Also, as a bit of expanded functionality the script only grabs the first four lines of every alert which gives the alert name, classification, priority, and basic packet information. This makes for a more condensed and concise output.


You can download the script here: snort_parser.zip


As for implementation, I have setup a scheduled task that runs the script every 5 minutes so that the RainMeter on my desktop is updated very frequently. One small issue I noticed was that when this task ran it would pop up a command prompt window momentarily which was quite annoying. In order to combat this I created a VBS script that runs the perl script in the background. Rather than running the perl script, the scheduled task runs the VBS script which calls the perl script as an argument so that the process is invisible to me.


You can download the VBS script here: silent_launcher.vbs


Feel free to download, use, and distribute these files as you see fit.

Do you have a story about a time when you used packet analysis to solve a problem on your network? If so, I want to hear that story. E-Mail me at chris@chrissanders.org and your story could be featured on this site or even in the next edition of Practical Packet Analysis.