Category Archives: Network Security Monitoring

Three Useful SOC Dashboards

I worked in security operation centers for a long time, and I really grew to hate dashboards. Most of them were specially designed pages by vendors meant to impress folks who don’t know any better when they stroll through the SOC and glance at the wall of low-end plasmas. They didn’t really help me catch bad guys any better, and worse yet, my bosses made my ensure they were always functional. Fast forward a few years, and I end up working for a vendor who builds security products. Much to my dismay, while planning for features we end up having to build these same dashboards because, despite my best efforts to persuade otherwise, CISO’s consistently ask for eye candy, even while admitting that it doesn’t have anything to do with the goal of the product. Some of them even tell us, straight up, that they won’t purchase our product if it doesn’t have eye catching visuals.

I provide that backstory to provide some insight into my long, tortuous relationship with useless dashboards. I talk about this enough at work that I feel like I’ve almost created a support group for people who have stress triggers associated with dashboards. If you’ve ever attended a conference talk from my good friend Martin Holste, you may know he hates dashboards even more than me. Alas, I’m not here just to rant. I actually believe that dashboards can be useful if they focus less on looking like video games and they help analysts do their job better. So, in this post I’m going to talk about three dashboard metrics you can collect right now that are actually useful. They won’t look pretty, but they will be effective.

Data Availability

The foundation of any investigation is rooted in asking questions, making hypotheses, and seeking answers that either disprove or prove your educated guesses. Your questioning and answer seeking with both be driven, in part, based on the data you have available. If you have PCAP data then you know you can seek answers about the context within network communication, and if you have Sysmon configured on your Windows infrastructure, you know you can look for file hashes in process execution logs.

While the existence of a data source is half the battle, the other half is retention. Some sources might have a specific time window. You might store PCAP for 3 day and flow data for 90 days, for example. Other data sources will probably use a rolling window, like most logs on Windows endpoints that are given a disk quota and roll over when that quota is met. In both cases, the ability to quickly ascertain the availability of data you have to work with is critical for an analyst. In short, if the data isn’t there, you don’t want to waste time trying to look for it. I contend that any time spent gathering data is wasted time, because the analyst should spend most of their time in the question and answer process or drawing conclusions based on data they’ve already retrieved.

A data availability section on a live dashboard helps optimize this part of the analyst workflow by providing a list of every data source and the earliest available data.

dashboard-dataavailability

In the example above I’ve created a series of tiles representing five different data types common to a lot of SOCs. Each tile boldly displays the name of the data source, and the earliest available date and time of data for it. In this example, I’ve also chosen to color code certain tiles. Data sources with a fixed retention period are green, sources with a rolling retention period based on a disk quota are yellow and red. I’ve chosen to highlight endpoint logs in red because those are not centralized and are more susceptible to a security event causing the logs to roll faster. The idea here is to relay some form of urgency into the analyst if they need to gather data from a particular source. While PCAP, flow, and firewall logs are likely to be there a few hours later, things can happen that will purge domain auth and Windows endpoint logs.

Ideally, this dashboard component is updated quickly and in an automated fashion. At minimum, someone updating this manually once a day will still save a lot of time for the individual analyst or collective group.

Open Case Status

Most SOCs use some form of case tracking or management system. While there aren’t a lot of really great options that are designed with the SOC in mind, there are things people find a way to make work like RTIR, Remedy, Archer, JIRA, and more. If integrated properly, the case management system can be a powerful tool for facilitating workflow when you assign users to cases and track states properly. This can be a tremendous tool for helping analysts organized, either through self organization or peer accountability.

 

dashboard-casestatus

In this example, I’ve gone with a simple table displaying the open cases. They are sorted and color coded by alive time, which is the time since the case was opened. As you might expect, things that have been pending for quite some time are given the more severe color as they require action. This could, of course, be built around an SLAs or internal guidelines you use for required response and closure times.

The important thing here is that this dashboard component shows the information the analysts needs to know. This provides the ability to determine what is open (case number), who they can talk to about it (owner), how serious it is (status), what it’s waiting on (pending), and how long have we known about the issue (alive).

Unsolved Mysteries

On any given day an analyst will run into things that appear to be suspicious, but for which there is no evidence to confirm that suspicion. These unsolved mysteries are usually tied to a weird external IP address or domain name, or perhaps an internal user or system. In a single analyst SOC this is easily manageable because if that analyst runs across the suspicious thing again it is likely to draw attention. That is a tougher proposition in the larger SOC however, because there is a chance that a completely different analyst is the one who runs across the suspicious entity the second time. In truth, you could have half a dozen analysts who encounter the same suspicious thing in different contexts without any of them knowing about the other persons finding. Each encounter could hold a clue that will unravel the mystery of what’s going on, but without the right way to facilitate that knowledge transfers something could be missed.

As a dashboard component,  using watch lists to spread awareness of suspicious entities is an effective strategy. To use it, analysts must  have a mechanism for adding things to a watch list, which is displayed on a screen for reference. Any time an analyst runs across something that looks suspicious but they can’t quite pin down, they first check the screen and if it’s not on there, they add it. Everything that shows up on this list is auto cycled off of it every 24-48 hours unless someone else puts it back on there.

dashboard-weirdthings

In this component, I’ve once again chosen a simple table. This provides the thing that is weird (item), who to talk to about it (observer), when it was observed in the data (date), and where you can go to find out the context of the scenario in which it was found (case) if there is any.

Conclusion

A Dashboard doesn’t have to use a fancy chart type or have lasers to be useful. In this post I described three types of information that are useful in a SOC when displayed on a shared dashboard. The goal is to use group dashboards to help analysts save time or be more efficient in their investigations. If you have the capacity to display this information, you’ll be well on your way to doing both of those things.

 

Do you have a really useful dashboard idea that you think is relevant in most SOCs? Let me know and I might blog about it down the road in a follow up.

Interested in learning more about the investigation process and how these dashboards fit in? Sign up for my mailing list to get first shot at my upcoming course focused entirely on the human aspect of security investigations.

Video: Building an NSM Lab

Building a security lab is something I get asked about really often. So often, in fact, that I decided to put some of my notes together and record a short training video on the topic. This video is only a small part of a much larger series I’m developing, so if you’re interested in learning more about that when it’s available, sign up for my mailing list.

In this one hour video I discuss the importance of an NSM lab and go through a systematic approach to building your own. I go through the following topics:

  • Analyzing your needs to define your inputs and desired outputs
  • Modeling your lab by building a list of technologies
  • The pros and cons of physical, virtual, and cloud based labs
  • Choosing the right platform for your lab
  • Designing your lab network
  • Sourcing the right hardware for your lab
  • Taking a step by step approach to designing and building the lab

Once you’re done with this video, you should have a system you can follow to build a lab that will help you test and build detection, analyze malware, and create simulations. I also provide a lot of insight to my own personal lab I use for my writing and my day job. I’ve also included some additional resources:

  • Lab planning worksheet
  • An exact parts list from my lab
  • Two example lab network diagrams
  • The network diagram for my personal lab

You can access the additional resources mentioned in the video by signing up here.

Investigations and Prospective Data Collection

confused-winnerOne of the problems we face while trying to detect and respond to adversaries is in the sheer amount of data we have to collect and parse. Twenty years ago it wasn’t as difficult to place multiple sensors in a network, collect packet and log data, and store that data for quite some time. In modern networks, that is becoming less and less feasible. Many others have written about this at length, but I want to highlight two main points.

Attackers play the long game. The average time from breach to discovery is over two hundred days. Despite media jargon about “millions of attacks a day” or attacks happening “at the speed of light”, the true nature of breaches is that they are not speedy endeavors from the attackers side. Gaining a foothold in a network, moving laterally within that network, and strategically locating and retrieving target data can take weeks or months. Structured attackers don’t win when they gain access to a network. They win once they accomplish their objective, which typically comes much later.

Long term storage isn’t economical. While some organizations are able to store PCAP or verbose log data in terms of months, that is typically reserved for incredibly well funded organizations or the gov/mil, and is becoming less common. Even on smaller networks, most can only store this data in terms of hours, or at most a few days. I typically only see long term storage for aggregate data (like flow data) or statistical data. The amount of data we generate has dramatically outgrown our capability to store and parse through that data, and this issue it only going to worsen for security purposes.

Medicine and Prospective Collection

The problem of having far too much data to collect and analyze is not unique to our domain. As I often do, let’s look towards the medical field. While the mechanics are a lot different, medical practitioners rely on a lot of the same cognitive skills to investigate afflictions to the human condition that we do to investigate afflictions to our networks. These are things like fluid ability, working memory, and source monitoring accuracy all work in the same ways to help practitioners get from a disparate set of symptoms to an underlying diagnosis, and hopefully, remediation.

Consider a doctor treating a patient experiencing undesirable symptoms. Most of the time a doctor can’t look back at the evolution of a persons health over time. They can’t take a CAT scan on a brain as it was six months ago. They can’t do an ultrasound on a pancreas as it was two weeks ago. For the most part, they have to take what they have in front of them now or what tests can tell them from very recent history.

If what is available in the short term isn’t enough to make a diagnosis, the physician can determine criteria for what data they want to observe and collect next. They can’t perform constant CAT scans, ultrasounds, or blood tests that look for everything. So, they apply their skills and define the data points they need to make decisions regarding the symptoms and the underlying condition they believe they are dealing with. This might include something like a blood test every day looking at white blood cell counts, continual EKG readings looking for cardiac anomalies, or twice daily neurological response tests. Medical tests are expensive and the amount of data can easily be overwhelming for the diagnostic process. Thus, selectively collecting data needed to support a hypothesis is employed. Physicians call this a clinical test-based approach, but I like to conceptualize it as prospective data collection. While retrospective data looks at things that have previously been collected up until a point in time, prospective data collections rely on specific criteria for what data should be collected moving forward from a fixed point in time, for a set duration. Physicians use a clinical strategy with a predominate lean towards effective use of prospective data collection because they can’t feasibly collect enough retrospective data to meet their needs. Sound familiar?

Investigating Security Incidents Clinically

As security investigators, we typically use a model based solely on past observations and retrospective data analysis. The prospective collection model is rarely leveraged, which is surprising since our field shares many similarities with medicine. We all have the same data problems, and we can all use the same clinical approach.

The symptoms our patients report are alerts. We can’t go back and look at snapshots of a devices health over the retrospective long-term because we can’t feasibly store that data. We can look back in the near term and find certain data points based on those observations, but that is severely time limited. We can also generate a potential diagnosis and observe more symptoms to find and treat the underlying cause of what is happening on our networks.

Let’s look at a scenario using this approach.

Step 1

An alert is generated for a host (System A). The symptom is that multiple failed login attempts where made on the devices administrator account from another internal system (System B). 

Step 2

The examining analyst performs an initial triage and comes up with a list of potential diagnoses. He attempts to validate or invalidate each diagnosis by examining the retrospective data that is on hand, but is unable to find any concrete evidence that a compromise has occurred. The analyst determines that System B was never able to successfully login to System A, and finds no other indication of malicious activity in the logs. More analysis is warranted, but no other data exists yet. In other scenarios, the investigation might stop here barring any other alerting. 

Step 3

The analyst adds his notes to the investigation and prunes his list of diagnoses to a few plausible candidates. Using these hypothesis diagnoses as a guide, the analyst generates a list of prospective collection criteria. These might include:

  • System A: All successful logins, newly created user accounts, flow data to/from System B.
  • System B: File downloads, attempted logins to other internal machines, websites visited, flow data to/from System A.

This is all immensely useful data in the context of the investigation, but it doesn’t break the bank in terms of storage or processing costs if the organization needs to store the data for a while in relation to this small scope. The analyst tasks these collections to the appropriate sensors or log collection devices. 

Step 4

The prospective collections record the identified data points and deliver them exclusively to the investigation container they are assigned to. The analyst collects these data points for several days, and perhaps refines them or adds new collections as data is analyzed.

Step 5

The analyst revisits and reviews the details of the investigation and the returned data, and either defines additional or refined collections, or makes a decision regarding a final diagnosis. This could be one of the following:

  • System B appears to be compromised and lateral movement to System A was being attempted.
  • No other signs of malicious activity were detected, and it was likely an anomaly resulting from a user who lost their password. 

In a purely retrospective model the later steps of this investigation might be skipped, and may lead the analyst to miss the ground truth of what is actually occurring. In this case, the analyst plays the long game and is rewarded for it.

Additional Benefits of Prospective Collection

In addition to the benefits of making better use of storage resources, a model that leverages prospective collection has a few other immediate benefits to the investigative process. These include:

Realistic-Time Detection. As I’ve written previously, when the average time from breach to detection is greater than two hundred days, attempting to discover attackers on your network the second they gain access is overly ambitious. For that matter, it doesn’t acknowledge the fact that attackers may already be inside your network. Detection can often its hardest at the time of initial compromise because attackers are typically more stealthy at this point, and because less data exists to indicate they are present on the network. This difficulty can decrease over time as attackers get sloppier and generate more data that can indicate their presence. Catching an attacker +10 days from initial compromise isn’t as sexy as “real time detection”, but it is a lot more realistic. The goal here is to stop them from completing their mission. Prospective collection supports the notion of realistic-time detection.

Cognitive Front-Loading. Research shows us that people are able to solve problems a lot more efficiently when they are aware of concepts surrounding metacognition (thinking about thinking) and are capable of applying that knowledge. This boils down to have an investigative philosophy and a strategy for generating hypotheses and having multiple approaches towards working towards a final conclusion. Using a prospective collection approach forces analysts to form hypotheses early on in the process, promoting the development of metacognition and investigation strategy.

Repeatability and Identified Assumptions. One of the biggest challenges we face is that investigative knowledge is often tacit and great investigators can’t tell others why they are so good at what they do. Defining prospective collection criteria provides insight towards what great investigators are thinking, and that can be codified and shared with less experienced analysts to increase their abilities. This also allows for more clear identification of assumptions so those can be challenged using structured analytic techniques common in both medicine and intelligence analysis. I wrote about this some here, and spoke about it last year here.

Conclusion

The purpose of this post isn’t to go out and tell everyone that they should stop storing data and refocus their entire SOC towards a model of prospective collection. Certainly, more research is needed there. As always, I believe there is value in examining the successes and failures of other fields that require the same level of critical thinking that security investigations also require. In this case, I think we have a lot to learn from how medical practitioners manage to get from symptoms to diagnosis while experiencing data collection problems similar to what we deal with. I’m looking forward to more research in this area.

The Value of Watching Game Tape

coachcalBeing a native Kentuckian, it’s no secret that I bleed blue. As I write this, my Kentucky Wildcats are towards the end of what I hope will continue to be a historic season. All of the prestige that comes with being a tournament favorite also brings copious amounts of media coverage. A recent article by the Wall Street Journal caught my eye. I’ve always known that Head Coach John Calipari isn’t a big fan of exposing his players to game tape, but I’ve never known exactly why until now. The WSJ article addresses this exact topic. The article is worth a read, but this section sums up a lot of Coach Cal’s philosophy:

Kentucky touches on its opponents in the days before a game with a series of walk-throughs in which the Wildcats’ scout team apes the upcoming opponent’s strategy. By the time Kentucky’s players watch film, they have already seen the opponent’s sets on the court, often several times. Even then, though, they aren’t looking for specific plays.

“You see the idea of their offense,” Kentucky guard Aaron Harrison said. “We don’t need to watch every single play. We need to know the options off each set they have. After that we just have to defend.”

The article goes on to mention that assistant coaches responsible for video typically only allow a maximum of eight minute of video review. This is astonishing, because it goes against the grain of what most teams do. The majority of teams in college basketball place extreme focus on film review, often devoting multiple hours a day to it and even sending players home with iPad’s to review game tape away from team facilities. Coach Cal instead makes the players focus heavily on their own strengths and weaknesses, helping them understand that with their talent level, they can beat most anyone if they play as the best version of themselves. In this approach, Kentucky’s losses often have just as much to do with the team beating themselves as it does with them being beaten by their opponent.

Of course, limiting exposure to game tape isn’t a completely new concept. Another coach that practiced this, albeit in an era where obtaining video of teams performances was much harder, was legendary UCLA coach John Wooden. Coach Wooden won an unmatched 10 national championships during his tenure and is widely accepted by many to be the greatest college basketball coach of all time.

Given the audience of my blog, you can probably guess that this post isn’t purely about basketball. This got me thinking about how the Wooden/Calipari approach to limiting game tape applies to using “game tape” in information security. In our case, game tape is more commonly known as threat intelligence. In most cases, this is explicit knowledge about an adversary based derived by researching previous compromises and malware samples. While I can’t possibly argue that threat intelligence should be abandoned, it does make me wonder about the emphasis placed on it in certain environments. In the right situation, might it actually be preferable to decrease focus on threat intelligence and instead focus inward to ones own network to perform effective detection? Perhaps it’s possible that threat intelligence can sometimes be used as a crutch that substitutes for understanding your network as well as you should. That’s a bit radical, but it’s food for thought.

Coach Cal and Wooden both had the benefit of having very good players as their disposal. In the same manner, I think selectively limiting reliance on threat intelligence requires an “A team” of players in your SOC. Having the capability to monitor your network assets and relationships on a very granular basis requires talent and resources, and that simply isn’t something most organizations can do. As information security takes a more mainstream role in our society, this may change as new research and tooling is built to support this line of thought. It might also be positively impacted as the general skill gap between established and amateur defenders narrows.

This approach also requires forward thinking viewpoint on the fundamental nature of breaches. It requires that you accept that prevention eventually fails, and that you don’t consider breaches to exist in a binary state of being. An attacker who breaches your network will have a set mission or series of goals, and the degree to which they succeed and the impact to your business or data determines the nature of the breach. There isn’t simply a breach, there are degrees of breaches. Just like a basketball team can’t expect to keep the opposing team from scoring any points at all, the network defender can’t expect their network to remain forever unbreached. At the end of the day, it’s all about making sure you have more points than your opponent/adversary.

All in all, this might be a bit of a stretch. That said, it does have me thinking quite a bit about the reliance on threat intelligence in defending networks, and what can be done to better understand my own network so that I can focus my defense, detection, and response around where critical data exists and where potential weaknesses exist. Ultimately, having great threat intelligence is not a panacea and there are a lot of ways to think about defending networks that exist independently from detailed knowledge of attacker tools, tactics, and procedures.

Investigating Like a Chef

Whenever I get the chance I like to try and extract lessons from practitioners in other fields. This is important because the discipline of information security is so new, while more established professions have been around, in some cases, for hundreds of years. I’ve always had a keen interest in culinary studies, mostly because I come from an area of the country where people show that they love each other by preparing meals. I’m also a bit of a BBQ connoisseur myself, as those of you who know me can solemnly attest to. While trying to enhance my BBQ craft I’ve had the opportunity to speak with and read about a few professional chefs and study how they operate. In this post I want to talk a little bit about some key lessons I took away from my observations.

If you have ever worked in food service, or have even prepared a meal for a large number of people you know that repetition is often the name of the game. It’s not trimming one rack of ribs, its trimming a dozen of them. It’s not cutting one sweet potato, its cutting a sack of them. Good chefs strive to do these things in large quantities while still maintaining enough attention to detail so that the finished product comes out pristine. There are a lot of things that go into making this happen, but none more important than a chef mastering their environment. This isn’t too different than a security analyst who investigates hundreds of alerts per day while striving to pay an appropriate amount of attention to each individual investigation. Let’s talk about how chefs master their environment and how these concepts can be applied to information security.

Chefs minimize their body movement. If you are going to be standing up in a kitchen all day performing a bunch of repetitive and time sensitive tasks, then you want to make sure every step or movement you make isn’t wasted. This prevents fatigue and increases efficiency.

As an example, take a look at Figure 1. In this image, you will see that everything the chef needs to prepare their dish is readily available without the chef having to take extra steps or turn around too often. Raw products can be moved from the grocery area, rinsed in the sink, sliced or cut on the cutting board, cooked on the stove, and plated without having to turn more than a few times or move more than a couple of feet.

chef_mentalmoves

Figure 1: A Chef’s Workspace is Optimized for Minimal Movement

Chefs learn the French phrase “mise en place” early on in their careers. This statement literally means, “put in place”, but it specifically refers to organizing and arranging all needed ingredients and tools required to prepare menu items during food service. Many culinary instructors will state that proper mise en place, or simply “mise” in shorthand, is the most important characteristic that separates a professional chef from a home cook.

There is a lot of room for mise in security investigations as well. Most analysts already practice this to some degree by making sure that their operating system is configured to their liking. They have their terminal windows configured with a font and colors the make it easy to read, they have common OSINT research sites readily accessible as browser favorites, and they have shortcut icons to all of their commonly used tools. At a higher level, some analysts even have custom scripts and tools they’ve written to minimize repetitive tasks. These things are highly encouraged.

While analysts don’t have to worry about physical movement as much, they do have to work about mental movement. In an ideal situation an analyst can get to the end of an investigation with as few steps as possible, and a strategic organization of their digital workspace can help facilitate that. I’ve seen some organizations that seek to limit the flexibility analysts have in their workspace by enforcing consistent desktop environments or limiting access to additional tools. While policies to enforce good security and analysis practices are great, every analysts learns and processes information in a different way. It isn’t only encouraged that analysts have flexibility to configure their own operating environments, it’s critical to helping them achieve success.

Beyond the individual analysts workstation, the organization can also help out by providing easy access to tool and data, and processes that support it. If an analyst has to connect to five systems to retrieve the same data, that is too much mental movement that could be better spent formulating and answering questions about the investigation. Furthermore, if organizations limit access to raw data it could force the analyst to make additional mental moves that slow down their progress.

Chefs make minimal trips to the fridge/pantry. When you are cooking dinner at home you likely make multiple trips to the fridge to get ingredients or to the pantry to retrieve spices during the course of your meal. That might look something like this:

“I think this soup needs a bit more tarragon, let me go get it. “

or…

“I forgot I need to add an egg to the carbonara at the end, I’ll go get it from the fridge.”

Building on the concept of mise en place, professional chefs minimize their trips to the fridge and pantry so that they always have the ingredients they need with as few trips as possible. This ensures they are focused on their task, and also minimizes prep and clean up time. They also ensure that they get an appropriate amount of each ingredient to minimize space, clean up, and waste.

chef_mise

Figure 2: Chef’s Gather and Lay Out Ingredients for Multiple Dishes – Mise en Place

One of the most common tasks an analyst will perform during an investigation is retrieval of data in an attempt to answering questions. This might include querying a NetFlow database, pulling full packet capture data from a sensor, or querying log data in a SIEM.

Inexperienced analysts often make two mistakes. The first is not retrieving enough data to answer their questions. This means that the analyst must continue to query the data source and retrieve more data until they get the answer they are looking for. This is equivalent to a chef not getting enough flour from the pantry when trying to make bread. On the flip side, another common pitfall is retrieving too much data, which is an even bigger problem. In these situations an analyst may not limit the time range of their query appropriately, or simply may not use enough filtering. The result is a mountain of data that takes a significant amount of time to wade through. This is equivalent to a chef walking back from the fridge with 100 eggs when they only intend to make a 3-egg omelet.

Learning how to efficiently query data sources during an investigation is product of asking the right questions, understanding the data you have available, and having the data in a place that is easily accessible and reasonably consolidated. If you can do these things you should be able to ensure you are making less trips back to the pantry.

Chefs carefully select, maintain, and master their tools. Most chefs spend a great deal of time and money purchasing and maintaining their knives. They sharpen their knives before every use, and have them professionally refinished frequently. They also spend a great deal of time practicing different types of cuts. A dull or improperly used knife can result in inconsistently cut food, which can lead to poor presentation and even cause under or overcooked food if multiple pieces of food are cooked together but are sized differently. Of course, this could also lead to you accidentally cutting yourself. These concepts go well beyond knives; a bent whisk can result in clumped batter, and an unreliable broiler can burn food. Chefs have to select, maintain, and master a variety of tools to perform their job.

chef_tools

Figure 3: A Chef’s Travel Kit Provides Well-Cared For Essential Tools

In a security investigation tools certainly aren’t everything, but they are critically important. In order analyze network communication you have to understand the protocols involved at a fundamental level, but you also need tools to sort through them, generate statistics, and work towards decision points. Whether it is a packet analysis tool like Wireshark, a flow data analysis tool like SiLK, or an IDS like Snort, you have to understand how those tools work with your data. The more ambiguity placed between you and raw data, the greater chance for assumptions that could lead to poor decisions. This is why it is critical to understand how to use tools, and how they work.

Caring for tools goes well beyond purchasing hardware and ensuring you have enough servers to crunch data. At an organization level it requires hiring the right number of people in your SOC to help manage the infrastructure. Some organizations attempt to put that burden on the analysts, but this isn’t always scalable and often results in analysts being taken away from their primary duties. This is also the “piling on” of responsibilities that results in analysts getting frustrated and leaving a job.

Beyond this, proper tool selection is important as well. I won’t delve into this too much here, but careful consideration should be given to free and open source tools, as well as the potential for developing in house tools. Enterprise solutions have their place, but that shouldn’t be the default go-to. The best work in information security in most cases is still done at the free and open source level. You should look for tools that support existing processes, and never let a tool alone dictate how you conduct an investigation.

Chefs can cook in any kitchen. When chefs master all of the previously mentioned concepts, it allows them to apply those concepts in any location. If you watch professional cooking competitions, you will see that most chefs come with only their knife kit and are able to master the environment of the kitchen they are cooking in. For example, try watching “Chopped” sometime on Food Network. These chefs are given short time constraints and challenging random ingredients. They organize their workspace, assess their tools, make very few trips to get ingredients, and are able to produce five star quality meals.

chef_chopped

Figure 4: Professional Chef’s Competing in an Unfamiliar Kitchen on Food Network’s Chopped

In security investigations, this is all about understanding the fundamentals. Yes, tools are important as I mentioned earlier, but you won’t always work in an environment that provides the same tools. If you only learn how to use Arcsight then you will only ever be successful in environments that use Arcsight. This is why understanding higher-level investigative processes that are SIEM-independent is necessary. Even at a lower level, understanding a tool like Wireshark is great, but you also need to understand how to work with packets using more fundamental and universal tools like tcpdump, as you may not always have access to a graphical desktop. Taking that step further, you should also understand TCP/IP and network protocols so that you can make better sense of the network data you are analyzing without relying on protocol dissectors. A chef’s fundamental understanding of food and cooking methods allows them to cook successfully in any kitchen. An analyst’s fundamental understanding of systems and networking allows them to investigate in any SOC.

Conclusion

Humans have been cooking food for thousands of years, and have been doing so professionally for much longer than computers have even existed. While the skills needed to be chef are dramatically different than those needed to investigate network breaches, there are certainly lessons to be learned here. Now, if you’ll excuse me, writing this has made me hungry.

* Figures 1-3 are from “The Four-Hour Chef” by Tim Ferriss. One of my favorite books.