Category Archives: Psychology

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.

On the Importance of Questions in an Investigation

questionsI spend a large part of my day studying cognition related to security investigations, which can ultimately be boiled down to thinking about how we learn and process information during and around our investigative processes. As part of my research, one of my professors recently pointed me towards a TEDx video by Dan Rothstein entitled “Did Socrates Get it Wrong?”. In this fourteen minute talk Rothstein questions whether Socrates approach of expert led questioning, commonly referred to as the Socratic method, was wrong. He brings up quite a few fascinating points, but ultimately concludes that Socrates was right and wrong, and that strategic questioning is of the utmost importance, but that it can also be an entirely student lead exercise. The key here is that asking the right question is critical for exploration, and of course, getting to the right answer.

This has quite a few implications to security investigations. Strategic questioning as a means towards finding and eliminating bias is something that immediately comes to mind, but not what I want to talk about here.

At a more fundamental level is questioning as the essence of the investigation process. I tend to believe that an investigation itself is simply a question. Usually something like this:

  • What happened here?
  • Did we get compromised?
  • Did APT[x] access any of our information assets?

Going one step further, I would also hypothesize that every action we take during the course of an investigation can be distilled down into a question, like these:

  • Does the activity identified in this alert match what the signature was trying to detect?
  • Did internal Host A communicate with external Host B?
  • Did the device download and execute the stage two payload of this malware family?
  • Is there a log indicating that a specific file was accessed?

Most of the time these questions don’t materialize in this form. Typically, they develop in our subconscious and analysts go forth looking for answers before they’ve articulated the question fully. I may not actually ask myself “Does the data in this PCAP match what the signature was looking for in the appropriate context?” before I go look at the signature to see what it was attempting to detect, but subconsciously that is exactly what I’m doing. Research suggests that a lot of this can be attributed to the formulation of habits or intuition (potentially in a brain structure known as the precuneus) that help us be more cognitively efficient. While this type of intuition can help us get things done faster, there is immense value in ripping these things from our subconscious into our conscious thought so that they can be articulated.

A couple things come to mind immediately when assessing the value of articulating questions consciously. First, if all of an investigation can be based on questions, we must ensure we are asking the right questions. This requires us to be consciously aware of those questions before we seek to solve them. Second, if we hope to successfully train the next generation of analysts then we have to teach them to ask the right questions, again requiring us to be consciously aware of what they are.

If you are a security investigator or are responsible for training them, consider creating a culture of articulated questions in your SOC. Before acting, attempt to determine what question you are trying to answer and share that information with your peers. I would bet that you will find this type of strategic questioning will help you ask better questions and more effectively guide your investigation towards an appropriate goal.

References:

Dan Rothstein, “Did Socrates Get it Wrong”, TEDx Somerville – https://www.youtube.com/watch?v=_JdczdsYBNA 

Working Memory and the Visual Investigative Hypothesis

Late last year I wrote a blog post focused what I have perceived as a coming evolution of focus for security investigations. This evolution will push us into an era where the human analyst takes center stage in a security investigation, and where tools and processes will shift to augment human cognitive ability. In this article, I want to expand on some of those thoughts and describe my research on how human analysts solve investigations. This is summarized as a concept I refer to as visual investigative theory.

I want to begin by revisiting the KSU ethnographic study I called out in a previous article. When several KSU sociologists spent time performing an ethnographic study of a security operations center they had some very interesting findings. Based on those findings, I drew the following conclusions:

Investigative process knowledge is tacit. While experienced analysts have the ability to quickly solve investigations, they almost never have the ability to accurately describe what makes them so effective.

Fundamental skills and domains aren’t well established. We have an inability to identify the fundamental cognitive (not platform or technology specific) skills that are required to successfully detect and response to compromises. Further, we have not clearly identified subdomains of the broader security investigation domain, and differentiated the cognitive skills necessary to define and excel at each of them.

Knowledge transfer is limited. Without identified skills and domains, or adequate explicit process knowledge, our ability to train less experienced analysts is hampered. Most SOC’s rely exclusively on “over the shoulder” training where less experienced investigators simply watch experienced investigators work. While this has its place, a training program founded exclusively in this type of instruction is fundamentally flawed and lacks proper fundamental building blocks.

Investigations rely on intuition. The aforementioned findings lead to the conclusion that the investigative process relies heavily on intuition. Beyond tool and technology specific processes, investigators rely almost exclusively on what they might refer to as “gut feeling” to determine which steps they should take to connect the dots and solve the investigation at hand.

Examining Intuition

Intuition typically refers to the ability to understand something immediately without the need for conscious reasoning. The concept of intuition isn’t new, but its acceptance in the world of psychological research is. Psychology itself is a fairly young field, having only existed since the late 1800s and becoming exponentially more popular around the mid 1900s. Most founding fathers of psychology dismissed intuition. Even Sigmund Freud was famous for saying that “it is an illusion to expect anything from intuition.” However, that has changed in recent years with the development of more sophisticated brain imaging techniques.

If you’ve ever had a head injury where you’ve scrambled your eggs a bit, then there is a chance that you’ve been the beneficiary of an MRI scan. A newer and more advanced form of this is something called an fMRI scan, which allows doctors and researchers to measure the response level from certain areas of the brain when specific stimuli are introduced.

A group of researchers recently wanted to better understand the science behind intuition. To do this, they utilized fMRI technology to measure the response of different areas of the brain while presenting chess of varying degrees of expertise with match scenarios designed to draw upon their sense of intuition. While chess is very different from investigating security incidents, participants in each of these tasks claim to be successful thanks in part to unexplainable, tacit intuition.

In this scenario, researches selected two groups of chess players. The first group consisted of journeyman chess players who were familiar with the game, but would not be considered professionals or experts. The second group consisted of professional chess players with high global rankings. Both groups were presented with an image of a chessboard showing a game in progress for a short period of time. They were then asked questions relating to what moves they thought would be best next, while their neural response was measured using fMRI technology.

The results of this experiment were exciting because they identified a specific area of the brain where the chess experts showed significantly more activity than the inexperienced players. This area, called the precuneus, showed 2.1x more activity in the chess experts. This indicates that there is a biological basis for the unconscious thought that we’ve previously only been able to refer to as intuition. Because of this, many psychologists have begun to shift their beliefs such that they recognize the existence of intuition.

WorkingMemory-Fig1

Figure 1: The precuneus is related to what we think of as intuition

This gets really interesting when you consider that the precuneus is also known to be responsible for portions of our working memory, and our capacity to form and manipulate mental images. Before we dive into that, let’s have a quick primer on how human memory works.

Modeling Memory

There are multiple theories and models related to how memory is organized, but the most widely accepted model breaks it down into three distinct categories.

Sensory Information Store (SIS) is the most volatile form of memory, and is associated with the lingering sensations that follow a stimulus. For instance, if you are starting at an object and close your eyes, you may still “see” the object for a brief period as though its printed on to the inside of your eyelids. This is an example of SIS.

Short-term Memory (STM) is volatile memory that exists in conscious thought. When you are actively thinking about something, you are using STM to do so. This is why STM is often referred to as working memory (WM). Things that we perceive and only contemplate for a short period of time that aren’t worthy of storing permanently are processed by STM. In computing terms, STM is akin to RAM.

Long-term Memory (LTM) is our most resilient form of memory. Once something gets encoded into LTM it is stored for a very long time. For input into LTM, some theorize that we only encode certain things into LTM while others propose that we encode most everything. For output from LTM, some propose that we store everything but simply can’t recall it all, while others propose that some things that are encoded eventually decay out over time. In computer terms, LTM is similar to the concept of disk storage.

For the purposes of this article, we are most concerned with short term / working memory. As with memory in general, there are multiple models for how STM is organized, but one of the most widely accepted is Baddeley’s Model of Working Memory.

WorkingMemory-Fig2

Figure 2: Badelley’s model of working memory

In Badelley’s model, there are three components of WM that are all controlled by a central executive services.

The Phonological Loop stores audible information and prevents it from decaying by continuously repeating its contents. For example, it allows you to use working memory to remember a phone number by repeating it over and over again in your head.

The Episodic Buffer holds representations that integrate multiple types of information to form a single unified representation of memory. It was a more later and more recent addition to the model.

The Visuospatial Sketchpad (VSSP) allows us to mentally picture and manipulate visual information about objects. For example, if you picture a multi-colored cube rotating so that different colors face you as time advances, you are using the VSSP. It is this portion of working memory we are most concerned about for the purpose of this discussion.

Visual Investigative Hypothesis

We can apply what we just learned about working memory to the earlier discussion about intuition. As we discovered, intuition is strong related to the precuneus. Examination of other psychology and neurology research tells us that the precuneus is involved in several different things, including (surprise!) working memory and visuospatial processing. While not definitive, this does lead us to believe that the visuospatial sketchpad and the mental visualization and manipulation of objects may be related to intuition and how humans solve complex problems.

Of course, I’m not a neuroscientist and there is still quite a bit of ongoing research here. However, I think there are many cases when this theory makes sense. For example, prolific and prodigal musicians have been known to say that they can literally “see” the music as they are composing or playing it. Individuals who practice stock trading will also speak about how they can see trends forming before they actually happen, allowing them to execute smart orders and make a sizable profit. Even going back to our earlier discussion of chess, expert chess players will state that a reason they excel at competition is their ability to “see” the board and picture future situations better than their opponents.

It would truly appear that humans excel at processing information when it’s possible for them to visualize it, so why wouldn’t the same apply to security investigations? I’ve been an analyst myself for quite some time, and I’ve also had the pleasure of working with and speaking to a lot of other analysts, and I think this does apply. It’s important to realize that in a lot of cases, people may visualize things like this subconsciously without actually realizing that they are solving problems visually. I believe that individuals who excel at solving information security investigations also solve problems visually. In fact, I think that many subconsciously see a data or attackers moving thorough a network as they assimilate various data points from system logs, packet captures, and IDS alerts. I’ve summed this theory up into something I call visual investigative hypothesis.

In short, the visual investigative hypothesis states that security analysts are more efficient, and more likely to arrive at a conclusion based on an accurate representation of events that occurred when they are able to visualize the relationships that represent a network compromise and build a mental picture of an attacker moving through a network.

In psychology, most principals exist as either hypothesis or theories because our understanding of the brain, while advancing, is still very limited. Many highly probable concepts and others considerably less probable will likely never advance to being considered confirmed truths, so while I do expect to mold my research into a more sound theory, I certainly don’t expect to ever definitively and quantifiably prove it as a ground truth. A great deal of my doctoral coursework will be geared towards development of visual investigative hypothesis into more formal theory, which will involve continued efforts interviewing security analyst and conducting case studies regarding their investigative habits, failures, and successes.

Maximizing Working Memory Effectiveness

While there is still much work to do, if you subscribe to the visual investigative hypothesis there are a few ways you can begin shifting your investigative technique towards something that is much more visual. When considering working memory, its important to understand that it is a finite resource. Humans only have so much capacity in working memory, just like computers have only so much RAM. Some people have a larger WM capacity while some have less. In addition, external factors like tiredness and stress can negatively affect the situational capacity of WM. Knowing WM is a finite resource can guide us towards ideas for optimizing our investigative habits and the tools we use to perform our work.

As an example, consider the magic number seven, a theory developed by Princeton psychologist George Miller. This theory states that an average person can hold seven objects in working memory, plus or minus two. This means that if I were to list twenty random objects, you are likely to only remember five to nine of them. This is the result of biology, and most likely something that can’t really be changed person to person.

This applies to the investigative process when you think about all of the various pieces of information that an analyst has to store in WM when attempting to describe an anomalous event or breach. At any given point an analyst might need to consider a pair of IP addresses, a port number, protocol, two system roles, a detection signature, a file name, a portion of a file hash, a system name, a start time, and an end time. No wonder investigations push the limits of WM capacity.

Overcoming magic number seven and limitations of working memory is all about making the right information available at the right time, and in the right way. Some ways that we can do this during an investigation include:

Data Scoping: Analyst should only retrieve the information they need for the time duration required. Have too little data is a bad thing, but having too much data can be just as bad. This can be achieved by formulating concise questions before seeking data, and making sure your data sources can be queried flexibly.

Focusing on Relationships: Humans remember things better if they can associate them with existing schemas in long-term memory. If I were to tell you ten random objects and ask you to recall them an hour later, you would have trouble doing so. If I repeated the same experiment with related items like breakfast foods, your recall would be much better. We can force objects in an investigation into similar schemas by describing entities as nouns and their interactions as verb, building graph/link representations that help us conceptualize a potential attackers movement through a network this way. One of the bigger gaps between network attackers and defenders is that attackers often think in this type of relationship-centric manner, and defenders don’t.

Rethinking Search: Searching through data itself should be less of an iterative process of querying a data source, viewing a response, and repeating. It should be more of an exploration where the analyst anchors themselves to a point in the data and they explore outward from there. This supports a relationship-centric view of security.

Visualizing Events over Time: The activities of a suspected adversary typically lend themselves well to groupings of major and minor events occurring at specific times. Using timelines to represent these groupings of events with pointers back to the source data can provide a visual construct that is useful for easing pressure on WM.

Easy to Remember Names: Long strings of characters like MD5/SHA1 hashes or even IP addresses take up valuable space in WM. Often times analysts will try to remember sections of these objects just as the last octet of an IP address or the last few characters of a file has. Another strategy here is to assign common names to various unique hosts and files for quick reference during the investigation. I’ve done this with animals or food in the past. Thus, f527fe6879ae8bf31cbb1e5c32d0fc33 becomes Fennel, and 123.1.2.3 becomes Puma. This is made easier when the tools used facilitate it. Of course, protocols like DNS can make this easier too, but its important to remember that a DNS name simply represents a point to a host, and not a host in itself.

Conclusion

The concepts surrounding the visual investigative hypothesis aren’t new. Most of us know that the right visualizations can help us find evil better, but beyond that we don’t collectively have a lot of solid science that we can use to apply it to security investigations or how we train analysts. While I think there are some practical takeaways we can draw from this immediately, there is still much work to be done. I’m looking forward to continuing my research here and applying cognitive psychology concepts to the security investigation process.

Teaching Good Investigation Habits Through Reinforcement

Press_for_food-fullThe biggest responsibility that leaders and senior analysts in a SOC have is to ensure that they are providing an appropriate level of training and mentoring to younger and inexperienced analysts. This is how we better our SOC’s, our profession, and ourselves. One problem that I’ve written about previously relates to the prevalence of tacit knowledge in our industry. The analysts who are really good at performing investigations often can’t describe what makes them so good at it, or what processes they use to achieve their goals. This lack of clarity and repeatability makes it exceedingly difficult to use any teaching method other than having inexperienced analysts learning through direct observation of those who are more experienced. While observation is useful, a training program that relies on it too much is flawed.

In this blog post I want to share some thoughts related to recent research I’ve done on learning methods as part of my study in cognitive psychology. More specifically, I want to talk a bit about one specific way that humans learn and how we might be able to better frame our investigative processes to better the investigation skills of our fellow analysts and ourselves.

Operant Conditioning

When most people think of conditioning they think of Pavlov and how he trained his dogs to learn to salivate at the sound of a tone. That is what is referred to as learning by classical conditioning, but that isn’t what I want to talk about here. In this post, I want to instead focus on a different form of learning called operant conditioning. While classical conditioning is learning that is focused on a stimulus that occurs prior to a response and is associated with involuntary response, operant conditioning is learning that is related to voluntary responses and is achieved through reinforcement or punishment.

An easy example of operant conditioning would be to picture a rat in a box. This box contains a button the rat can push with its body weight, and doing so releases a treat. This is an example of positive reinforcement that allows to rat to learn the associated that pressing the button results in a treat. The relationship is positively reinforced because a positive stimulus is used.

Another type of operant conditioning reinforcement is negative reinforcement. Consider the same rat in a different box with a button. In this box, a mild electrical charge is passed to the rat through the floor of the box. When the rat presses the button, the electrical charge stops for several minutes. In this case, negative reinforcement is being used because it teaches the rat a behavior by removing a negative stimulus. The key takeaway here is that negative reinforcement is still reinforcing a behavior, but in a different way. Some people confuse negative reinforcement with punishment.

Punishment is the opposite of reinforcement because it reduces the probability of a behavior being expressed. Consider the previous scenario with the rat in the electrified room, but instead, the room is only electrified when the rat presses the button. This is an example of a punishment that decreases the likelihood of the rat pressing the button.

Application to Security Investigation

I promise that all of this talk about electrifying rats is going somewhere other than the BBQ pit (I live in the deep south, what did you expect?). Earlier I spoke about the challenge we have because of tacit knowledge. This is made worse in many environments where you have access a mountain of data but have an ambiguous workflow that can allow an input (alert) to be taken down hundreds of potential paths. I believe that you can take advantage of a fundamental construct like operant conditioning to help better your analysts. In order to make this happen, I believe there are three key tasks that must occur.

Identify Unique Investigative Domains

First, you must designate domains that lend themselves to specific cognitive functions and specializations. For instance, triage requires different skills sets and cognitive processes than hunting. Thus, those are two separate domains with different workflows. Furthermore, incident response requires yet another set of skills and cognitive processes, making it a third domain of investigation. Some organizations don’t really distinguish between these domains, but they certainly should. I think there is work to be done to fully establish investigative domains (I expect lots of continued research here on my part), and more importantly, criteria for defining these domains. But at a minimum you can easily pick out a few domains relevant to your SOC, like I’ve mentioned above.

Define Key Workflow Characteristics and Approaches

Once you’ve established domains you can attempt to define their characteristics. This isn’t something you do in an afternoon, but there are a few clear wins. For instance, triage is heavily suited to divergent thinking and differential diagnosis techniques. On the other hand, hunting is equally reliant on convergent and divergent thinking and is well suited to relational (link) analysis. These are characteristics you can key on in your workflows moving on to the next step.

Apply Positive and Negative Reinforcement in Tools and Processes

Once you know what paths you want analysts to take, how do you reinforce their learning so that they are compelled to do so? While some of us would like to consider a mechanism that provides punishment via electrified keyboards, positive and negative reinforcement are a bit more appropriate. Of course, you can’t give an analyst a treat when they make good decisions, but you can provide reinforcement in other ways.

For an investigation, there is no better positive stimulus than providing easy and immediate access to relevant data. When training analysts, you want to ensure they are smart about what data they gather to support their questioning. Ideally, an analyst only gathers the amount of information the need to get the answer they want. More skilled analysts are able to do this quickly without spending too much time re-querying data sources for more data or whittling excess away from data sets that are too large. Whenever an analyst has a questions and your tool or process helps them answer it in a timely manner, you are positively reinforcing the use of that tool or process. Furthermore, when the answer to that question helps them solve an investigation, you are reinforcing the questions the analyst is putting forth, which helps that analyst learn what questions are most likely to help them achieve results.

Negative reinforcement can be used advantageous here as well. In many cases analysts arrive at points in an investigation where they simply don’t know what questions to ask next. With no questions to ask, the investigation can stall or prematurely end. When chasing a hot lead, this can result in frustration, despair, and hopelessness. If the tools and processes used in your SOC can help facilitate the investigation by helping the analysts determine their next logical set of questions, then that can serve as negative reinforcement by removing the negative stimuli of frustration, despair, and hopelessness. At this point you aren’t only help the analyst further a single investigation, you are once again reinforcing questions that help them learn how to further every subsequent investigation they will conduct.

Other Thoughts

While the previous sections identified some structured approaches you can take towards bettering your analysts, I had a few less structured thoughts I wanted to share in bullet points. These are ways that I think SOC’s can help achieve teaching goals in every day decisions:

  • How can you continually provide positive reinforcement to help analysts learn to make good decisions?
  • If you are making a decision for analysts, let them know. Little things like data normalization and timestamp assumptions can make a difference. Analyst knowledge of these things further help them understand their own data and how we manipulate it for their (hopeful) betterment. Less abstraction from data is critical to understanding the intricacies of complex systems.
  • You must be aware of when you punish your analysts. This occurs when a tool or process prevents the user from getting data they need, takes liberties with data, fails to produce consistent results, etc. If a process or tool is frustrating for a user, then that punishment decreases the likelihood that they will use it, even if it represents a good step in the investigation. You want to at all costs avoid tools and processes that steer your analysts away from good analytic practices.

Conclusion

This is another post that is pretty heavy in theory, but it isn’t so far away from reality that it doesn’t’ have the potential for real impact in the way you make decisions about the processes and tools used in your SOC, and how you train your analysts. As our industry continues to work on developing workflows and technologies we have to think beyond what looks good and what feels right and grasp the underlying cognitive processes that are occurring and the mental challenges we want to help solve. One method for doing this is a thoughtful use of operating condition as a teaching tool.

Theory of Multiple Intelligences for Security Analysts – Initial Thoughts

Obrainicon_bluene of the more interesting concepts I’ve come to study recently is the theory of multiple intelligences, which was originally proposed in the 1980s by Dr. Howard Gardner, a developmental psychologist. The Theory of Multiple Intelligence (MI) simply states that rather than humans having a singular intelligence, we have a set of different intelligences that are independent and entirely unique. While his theory does have some detractors and competing schools of thought, it has generally been met with great intrigue and is a popular area of study for developmental, cognitive, and industrial psychology scholars alike. In this post I want to discuss the theory of MI and how I think it relates to security investigations. While you might be expecting concise post full of conclusions with a nice bow on it, this article is more about raising questions and getting some of my notes on paper for further research.

Multiple Intelligences

We often think of intelligence as a measure of how much someone knows about something, but that more accurately describes aptitude than intelligence. An intelligence is actually a computation capacity. This is why true intelligence tests that result in intelligence quotient (IQ) scores are much more about measuring someone’s ability to learn than what they have learned. Traditionally, intelligence was viewed as a single biological construct. The theory of MI pluralizes this concept of computational capacity such that more than one of them exists, and that they exist as independent intelligences.

There are several criteria surrounding what the core intelligences are. This includes the intelligence being universal to the entire human species, an identifiable set of core operations, and a susceptibility to encoding in a symbol system where meaning can be captured. I don’t want to delve too far into these criteria here, but if you are interested in this you can read more in Dr. Gardner’s books mentioned at the end of the post. The result of Gardner’s study into MI resulted in the formulation of seven intelligences, with the assertion that all humans have the full range of these intelligences. I’ll give a basic outline of those now.

  • Musical-Rhythmic: Has to do with sensitivity to sounds, rhythms, tones, and music. People with high musical intelligence can often recognize and match pitch well, and are able to sing, play instruments, and compose music. These people usually excel careers as musicians, composers, singers, or producers.
  • Bodily-Kinesthetic: Relates to control of one’s bodily motions and the capacity to handle objets skillfully, to include a sense of timing and muscle memory. People with high bodily-kinesthetic intelligence are generally good at physical activities like working out, sports, dancing, or craftsmen activities. These people usually excel in careers as athletes, dancers, and various types of builders.
  • Logical-Mathematical: Has to do with logic, reasoning, numbers, and critical thinking. People with high logical-mathematical intelligence excel at problem solving, thinking about abstract ideas, solving complex computations, and conducting scientific experiments. These people usually excel in careers as scientists, programmers, engineers, and accountants.
  • Verbal-Linguistic: Deals with the ability to process, interpret, and form words. People with high verbal-linguistic intelligence are good at reading, writing, telling stories, and memorizing words and dates. These people usually excel in careers as writers, lawyers, journalists, and teachers.
  • Visual-Spatial: Has to do with the ability to visualize things in the mind. People with high visual-spatial intelligence excel at navigating, doing jigsaw puzzles, reading maps, recognizing patterns, interpreting graphs and charts, and daydreaming. These people usually excel in careers as architects, artists, and engineers.
  • Interpersonal: Focused on interaction with others and the ability to recognize and be sensitive to others moods, feelings, temperaments, and motivations. People with high interpersonal intelligence communicate effectively and empathize well with others. They often enjoy debates and excel at verbal and nonverbal communication. These people usually excel in careers as psychologists, counselors, politicians, and sales.
  • Intrapersonal: Focused on introspective and self-reflective capacities. People with high intrapersonal intelligence have a strong ability to assess their own strengths and weaknesses and predict their own reactions and emotions. These people usually excel in careers as writers, scientists, and philosophers.

A key takeaway under MI theory is that every human is born with each of these intelligences, but no two people have the same level of every intelligence. Even identical twins will have varying levels of each intelligence because we know that intelligence is shaped by nature and nurture. Additionally, we know that just because someone has a high level of a particular intelligence doesn’t mean that they will use that intelligence in a smart manner. For instance, someone with high logical-mathematical intelligence might choose to use their intelligence to guess lottery numbers for a living instead of applying it to one of the sciences, accounting, etc.

Intelligence and Security Investigations

Whether or not you subscribe to MI theory, it does provide an interesting approach towards viewing how and why certain people excel in different types of security investigations. There are multiple types of security investigation domains, including event-driven (triage) analysis, NSM hunting, malware analysis, and forensic response. I hold that each of these domains requires a specific balance and emphasis of abilities and computational capacity. With that in mind, it brings about an interesting question of which intelligences are most suited to particular types of security investigations.

The first thing that must be considered is whether each investigative domains is more suited to a laser or search light intellectual profile. These terms define the manner in which people typically excel in certain intelligences. A laser is a person who generally has a high elevation in one or two intelligences. A search light is a person who has an equal level of moderately elevated intelligence in three to four intelligences, but does not have a very high elevation in any one intelligence. Lasers tend to focus on one specific focus area or task, where as search lights tend to work in areas that require a constant surveying of multiple elements to form a bigger picture. Once each investigative domain is tied to a laser or search light profile, the individual intelligences that are most applicable can be determined.

I don’t have a lot of concrete thoughts yet related to which intellectual profiles and intelligences are suited to each investigative domain, and I certainly don’t have a thorough accounting for every relevant domain for information security. However, I do have some initial thoughts that warrant more research. I could postulate on this for quite some time, but a few things that initially come to mind including the following:

  • Most traditional computer scientists would probably think that security investigations are almost exclusively related to logical-mathematical intelligence. I’d challenge this for some investigative domains. In a  lot of cases I believe visual-spatial intelligence is much more important.
  • Malware analysis tends to lean more towards a laser profile. It also requires a great deal of logical-mathematical intelligence due to the need to interpret and reverse engineer source code during static analysis.
  • Triage analysis and forensic response requires visual-spatial intelligence because of all the moving parts that must be assimilated into a bigger picture. These are a product of the reliance on divergent thinking during these processes, and the need to rapidly shift to convergent thinking one a critical mass of ideas and knowledge has been reached.
  • Forensic response requires a greater deal of interpersonal intelligence due to the reliance on communication with various new and unfamiliar stakeholders. The ability to empathize and gauge moods is critical. I would guess that a search light profile would be most desired here.
  • Intelligence analysis requires an elevated interpersonal intelligence due to the need to assess motivations.
  • Analysis across most domains in a team setting requires some level of intrapersonal intelligence so that practitioners can identify their own deficiencies along the lines of alternative analysis methods.

If we can identify investigative domains and determine which intelligences are most suited to those, we can be a lot more successful in identifying the right people for those roles and educating them appropriately so that they are successful.  This is another step along the way towards converting tacit knowledge to explicit knowledge and gaining a better advantage in security analysis scenarios.

References:

Multiple Intelligences: New Horizons (2008), Howard Gardner

Frames of Mind: The Theory of Multiple Intelligences (1983), Howard Gardner