Before diving into cyber security and how the industry is using AI at this point, let’s define the term AI first. Artificial Intelligence (AI), as the term is used today, is the overarching concept covering machine learning (supervised, including Deep Learning, and unsupervised), as well as other algorithmic approaches that are more than just simple statistics. These other algorithms include the fields of natural language processing (NLP), natural language understanding (NLU), reinforcement learning, and knowledge representation. These are the most relevant approaches in cyber security.

Given this definition, how evolved are cyber security products when it comes to using AI and ML?

I do see more and more cyber security companies leverage ML and AI in some way. The question is to what degree. I have written before about the dangers of algorithms. It’s gotten too easy for any software engineer to play a data scientist. It’s as easy as downloading a library and calling the .start() function. The challenge lies in the fact that the engineer often has no idea what just happened within the algorithm and how to correctly use it. Does the algorithm work with non normally distributed data? What about normalizing the data before inputting it into the algorithm? How should the results be interpreted? I gave a talk at BlackHat where I showed what happens when we don’t know what an algorithm is doing.

So, the mere fact that a company is using AI or ML in their product is not a good indicator of the product actually doing something smart. On the contrary, most companies I have looked at that claimed to use AI for some core capability are doing it ‘wrong’ in some way, shape or form. To be fair, there are some companies that stick to the right principles, hire actual data scientists, apply algorithms correctly, and interpret the data correctly.

Generally, I see the correct application of AI in the supervised machine learning camp where there is a lot of labeled data available: malware detection (telling benign binaries from malware), malware classification (attributing malware to some malware family), document and Web site classification, document analysis, and natural language understanding  for phishing and BEC detection. There is some early but promising work being done on graph (or social network) analytics for communication analysis. But you need a lot of data and contextual information that is not easy to get your hands on. Then, there are a couple of companies that are using belief networks to model expert knowledge, for example, for event triage or insider threat detection. But unfortunately, these companies are a dime a dozen.

That leads us into the next question: What are the top use-cases for AI in security?

I am personally excited about a couple of areas that I think are showing quite some promise to advance the cyber security efforts:

1. Using NLP and NLU to understand people’s email habits to then identify malicious activity (BEC, phishing, etc). Initially we have tried to run sentiment analysis on messaging data, but we quickly realized we should leave that to analyzing tweets for brand sentiment and avoid making human (or phishing) behavior judgements. It’s a bit too early for that. But there are some successes in topic modeling, token classification of things like account numbers, and even looking at the use of language.
2. Leveraging graph analytics to map out data movement and data lineage to learn when exfiltration or malicious data modifications are occurring. This topic is not researched well yet and I am not aware of any company or product that does this well just yet. It’s a hard problem on many layers, from data collection to deduplication and interpretation. But that’s also what makes this research interesting.

Given the above it doesn’t look like we have made a lot of progress in AI for security. Why is that? I’d attribute it to a few things:

1. Access to training data. Any hypothesis we come up with, we have to test and validate. Without data that’s hard to do. We need complex data sets that are showing user interactions across applications, their data, and cloud apps, along with contextual information about the users and their data. This kind of data is hard to get, especially with privacy concerns and regulations like GDPR putting more scrutiny on processes around research work.
2. A lack of engineers that understand data science and security. We need security experts with a lot of experience to work on these problems. When I say security experts, these are people that have a deep understand (and hands-on experience) of operating systems and applications, networking and cloud infrastructures. It’s unlikely to find these experts who also have data science chops. Pairing them with data scientists helps, but there is a lot that gets lost in their communications.
3. Research dollars. There are few companies that are doing real security research. Take a larger security firm. They might do malware research, but how many of them have actual data science teams that are researching novel approaches? Microsoft has a few great researchers working on relevant problems. Bank of America has an effort to fund academia to work on pressing problems for them. But that work generally doesn’t see the light of day within your off the shelf security products. Generally, security vendors don’t invest in research that is not directly related to their products. And if they do, they want to see fairly quick turn arounds. That’s where startups can fill the gaps. Their challenge is to make their approaches scalable. Meaning not just scale to a lot of data, but also being relevant in a variety of customer environments with dozens of diverging processes, applications, usage patterns, etc. This then comes full circle with the data problem. You need data from a variety of different environments to establish hypotheses and test your approaches.

Is there anything that the security buyer should be doing differently to incentivize security vendors to do better in AI?

I don’t think the security buyer is to blame for anything. The buyer shouldn’t have to know anything about how security products work. The products should do what they claim they do and do that well. I think that’s one of the mortal sins of the security industry: building products that are too complex. As Ron Rivest said on a panel the other day: “Complexity is the enemy of security”.

Also have a look at the VentureBeat article feating some quotes from me.

This post is about capturing my talking points from the recent conference panel on the “Use of AI for Cybersecurity” at the Intelligence and National Security Alliance (INSA) conference. You can find my musings on the term AI in my previous blog post.

Here is the list of topics I injected into the panel conversation:

• Algorithms (AI) are Dangerous
• Privacy by Design
• Expert Knowledge over algorithms
• The need for a Security Paradigm Shift
• Efficacy in AI is non existent
• The need for learning how to work interdisciplinary

Please not that I am following in the vein of the conference and I won’t define specifically what I mean by “AI”. Have a look at my older blog posts for further opinions. Following are some elaborations on the different topics:

• Algorithms (AI) are Dangerous – We allow software engineers to use algorithms (libraries) for which they do not know what results are produced. There is no oversight demand – imagine the wrong algorithms being used to control any industrial control systems. Also realize that it’s not about using the next innovation in algorithms. When DeepLearning entered the arena, everyone tried to use it for their problems. Guess what; barely any problem could be solved by it. It’s not about the next algorithms. It’s about how these algorithms are used. The process around them. Interestingly enough, one of the most pressing and oldest problems that every CISO today is still wrestling with is ‘visibility’. Visibility into what devices and users are on a network. That has nothing to do with AI. It’s a simple engineering problem and we still haven’t solved it.
• Privacy by Design – The entire conference day didn’t talk enough about this. In a perfect world, our personal data would never leave us. As soon as we give information away it’s exposed and it can / and probably will be abused. How do we build such systems?
• Expert Knowledge – is still more important than algorithms. We have this illusion that AI (whatever that is), will solve our problems by analyzing data with the use of software systems that work with a cloud based database. Instead of using “AI” to augment human capabilities. In addition, we need experts who really understand the problems. Domain experts. Security experts. People with experience to help us build better systems.
• Security Paradigm Shift – We have been doing security the wrong way. For two decade we have engaged in the security cat and mouse game. We need to break out of that. Only an approach of understanding behaviors can get us there.
• Efficacy – There are no approaches to describing how well an AI system works. Is my system better than someone else’s? How do we measure these things?
• Interdisciplinary Collaboration – As highlighted in my ‘expert point’ above; we need to focus on people. And especially on domain experts. We need multi-disciplinary teams. Psychologists, counter intelligence people, security analysts, systems engineers, etc. to collaborate in order to help us come up with solutions to combat security issues. There are dozens of challenges with these teams. Even just something as simple as terminology or a common understanding of the goals pursued. And this is not security specific. Every area has this problem.

The following was a fairly interesting thing that was mentioned during one of the other conference panels. This is a “non verbatum” quote:

AI is one of the poster children of bipartisanship. Ever want to drive bipartisanship? Engage on an initiative with a common economical enemy called China.

Oh, and just so I have written proof when it comes to it: China will win the race on AI! Contrary to some of the other panels. Why? Let me list just four thoughts:

1. No privacy laws or ethical barriers holding back any technology development
2. Availability of lots of cheap, and many of them, very sophisticated resources
3. The already existing vast and incredibly rich amount of data and experiences collected; from facial recognition to human interactions with social currencies
4. A government that controls industry

I am not saying any of the above are good or bad. I am just listing arguments.

:wq

I was just reading an article from Forrester research about “Artificial Intelligence Is Transforming Fraud Management”. Interesting read until about half way through where the authors start talking about supervised and unsupervised learning. That’s when they lost a lot of credibility:

Supervised learning makes decisions directly. Several years ago, Bayesian models, neural networks, decision trees, random forests, and support vector machines were popular fraud management algorithms. (see endnote 8) But they can only handle moderate amounts of training data; fraud pros need more complex models to handle billions of training data points. Supervised learning algorithms are good for predicting whether a transaction is fraudulent or not."

Aside from the ambiguity of what it means for an algorithm to make ‘direct’ decisions, SML can only take limited amounts of training data? Have you seen our malware deep learners? In turn, if SML is good at predicting fraudulent transaction, what’s the problem with training data?

What do they say about unsupervised approaches?

Unsupervised learning discovers patterns. Fraud management pros employ unsupervised learning to discover anomalies across raw data sets and use self-organizing maps and hierarchical and multimodal clustering algorithms to detect swindlers. (see endnote 10) The downside of unsupervised learning is that it is usually not explainable. To overcome this, fraud pros often use locally interpretable, model-agnostic explanations to process results; to improve accuracy, they can also train supervised learning with labels discovered by unsupervised learning. Unsupervised learning models are good at visualizing patterns for human investigators.

And here it comes: “The downside of UML is that it is usually not explainable”. SML is much more prone to that problem than UML. Please get the fundamentals right. Reading something like this makes me question pretty much the entire article on its accuracy. There are some challenges with explainability and UML, but they are far less involed.

As a further nuance: “UML is not itself good at visualizing patterns. Some of the algorithms lend themselves to visualize the output. But there is more to turning a clustering algo into a good visual. I mention t-sne in one of my older blog posts. That algorithm actually follows an underlying visualization paradigm (projection of multiple dimensions into two or three dimensions).

Reading on in the article, it says:

As this use case requires exceptional performance and accuracy, supervised learning dominates.

I thought SML doesn’t scale? Turns out, it actually does quite well, not least because you can run a learner offline.

:q!

In my last blog post I highlighted some challenges with a research approach from a paper that was published at IEEE S&P, the sub conference on “Deep Learning and Security Workshop (DLS 2019)“. The same conference featured another paper that spiked my interest: Exploring Adversarial Examples in Malware Detection.

This paper highlights the problem of needing domain experts to build machine learning approaches for security. You cannot rely on pure data scientists without a solid security background or at least a very solid understanding of the domain, to build solutions. What a breath of fresh air. I hole heartedly agree with this. But let’s look at how the authors went about their work.

The example that is used in the paper is in the area of malware detection; a problem that is a couple of decades old. The authors looked at binaries as byte streams and initially argued that we might be able to get away without feature engineering by just feeding the byte sequences into a deep learning classifier – which is one of the premises of deep learning, not having to define features for it to operate. The authors then looked at some adversarial scenarios that would circumvent their approach. (Side bar: I wish Cylance had read this paper a couple years ago). The paper goes through some ROC curves and arguments to end up with some lessons learned:

• Training sets matter when testing robustness against adversarial examples
• Architectural decisions should consider effects of adversarial examples
• Semantics is important for improving effectiveness [meaning that instead of just pushing a binary stream into the deep learner, carefully crafting features is going to increase the efficacy of the algorithm]

Please tell me which of these three are non obvious? I don’t know that we can set the bar any lower for security data science.

I want to specifically highlight the last point. You might argue that’s the one statement that’s not obvious. The authors basically found that, instead of feeding simple byte sequences into a classifier, there is a lift in precision if you feed additional, higher-level features. Anyone who has looked at byte code before or knows a little about assembly should know that you can achieve the same program flow in many ways. We must stop comparing security problems to image or speech recognition. Binary files, executables, are not independent sequences of bytes. There is program flow, different ‘segments’, dynamic changes, etc.

We should look to other disciplines (like image recognition) for inspiration, but we need different approaches in security. Get inspiration from other fields, but understand the nuances and differences in cyber security. We need to add security experts to our data science teams!

Over the weekend I was catching up on some reading and came about the “Deep Learning and Security Workshop (DLS 2019)“. With great interest I browsed through the agenda and read some of the papers / talks, just to find myself quite disappointed.

It seems like not much has changed since I launched this blog. In 2005, I found myself constantly disappointed with security articles and decided to outline my frustrations on this blog. That was the very initial focus of this blog. Over time it morphed more into a platform to talk about security visualization and then artificial intelligence. Today I am coming back to some of the early work of providing, hopefully constructive, feedback to some of the work out there.

The researcher paper I am looking at is about building a deep learning based malware classifier. I won’t comment on the fact that every AV company has been doing this for awhile (but learned from their early mistakes of not engineering ‘intelligent’ features). I also won’t discuss the machine learning architecture that is introduced. What I will argue is the approach that was taken and the conclusions that were drawn:

• The paper uses a data set that has no ground truth. Which, in network security is very normal. But it needs to be taken into account. Any conclusion that is made is only relative to the traffic that the algorithm was tested, at the time of testing and under the used configuration (IDS signatures). The paper doesn’t discuss adoption or changes over time. It’s a bias that needs to be clearly taken into account.
• The paper uses a supervised approach leveraging a deep learner. One of the consequences is that this system will have a hard time detecting zero days. It will have to be retrained regularly. Interestingly enough, we are in the same world as the anti virus industry when they do binary classification.
• Next issue. How do we know what the system actually captures and what it does not?
  • This is where my recent rants on ‘measuring the efficacy‘ of ML algorithms comes into play. How do you measure the false negative rates of your algorithms in a real-world setting? And even worse, how do you guarantee those rates in the future?
  • If we don’t know what the system can detect (true positives), how can we make any comparative statements between algorithms? We can make a statement about this very setup and this very data set that was used, but again, we’d have to quantify the biases better.
• In contrast to the supervised approach, the domain expert approach has a non-zero chance of finding future zero days due to the characterization of bad ‘behavior’. That isn’t discussed in the paper, but is a crucial fact.
• The paper claims a 97% detection rate with a false positive rate of less than 1% for the domain expert approach. But that’s with domain expert “Joe”. What about if I wrote the domain knowledge? Wouldn’t that completely skew the system? You have to somehow characterize the domain knowledge. Or quantify its accuracy. How would you do that?

Especially the last two points make the paper almost irrelevant. The fact that this wasn’t validated in a larger, real-world environment is another fallacy I keep seeing in research papers. Who says this environment was representative of every environment? Overall, I think this research is dangerous and is actually portraying wrong information. We cannot make a statement that deep learning is better than domain knowledge. The numbers for detection rates are dangerous and biased, but the bias isn’t discussed in the paper.

:q!

Before even diving into the topic of Causality Research, I need to clarify my use of the term #AI. I am getting sloppy in my definitions and am using AI like everyone else is using it, as a synonym for analytics. In the following, I’ll even use it as a synonym for supervised machine learning. Excuse my sloppiness …

Causality Research is a topic that has emerged from the shortcomings of supervised machine learning (SML) approaches. You train an algorithm with training data and it learns certain properties of that data to make decisions. For some problems that works really well and we don’t even care about what exactly the algorithm has learned. But in certain cases, we really would like to know what the system just learned. Your self-driving car, for example. Wouldn’t it be nice if we actually knew how the car makes decisions? Not just for our own peace of mind, but also to enable verifyability and testing.

Here are some thoughts about what is happening in the area of causality for AI:

• This topic is drawing attention because people are having their blinders on when defining what AI is. AI is more than supervised machine learning, and a number of the algorithms in the field, like belief networks, are beautifully explainable.
• We need to get away from using specific algorithms as the focal point of our approaches. We need to look at the problem itself and determine what the right solution to the problem is. Some of the very old methods like belief networks (I sound like a broken record) are fabulous and have deep explainability. In the grand scheme of things, only few problems require supervised machine learning. 
• We are finding ourselves in a world where some people believe that data can explain everything. It cannot. History is not a predictor of the future. Even in experimental physics, we are getting to our limits and have to start understanding the fundamentals to get to explainability. We need to build systems that help experts encode their knowledge and augments human cognition by automating tasks that machines are good at.

The recent Cylance faux pas is a great example why supervised machine learning and AI can be really really dangerous. And it brings up a different topic that we need to start exploring more, which is how we measure the efficacy or precision of AI algorithms. How do we assess the things a given AI or machine learning approach misses and what are the things it classifies wrong? How does one compute these metrics for AI algorithms? How do we determine whether one algorithm is better than another. For example, the algorithm that drives your car. How do you know how good it is? Does a software update make it better? How much? That’s a huge problem in AI and ‘causality research’ might be able to help develop methods to quantify efficacy.

Join me for my talk about AI and ML in cyber security at BlackHat on Thursday the 9th of August in Las Vegas. I’ll be exploring the topics of artificial intelligence (AI) and machine learning (ML) to show some of the ‘dangerous’ mistakes that the industry (vendors and practitioners alike) are making in applying these concepts in security.

We don’t have artificial intelligence (yet). Machine learning is not the answer to your security problems. And downloading the ‘random’ analytic library to identify security anomalies is going to do you more harm than it helps.

We will explore these accusations and walk away with the following learnings from the talk:

I am exploring these items throughout three sections in my talk: 1) A very quick set of definitions for machine learning, artificial intelligence, and data mining with a few examples of where ML has worked really well in cyber security. Check cybersecuritycourses.com here for an overview of the best cyber security courses available. 2) A closer and more technical view on why algorithms are dangerous. Why it is not a solution to download a library from the Internet to find security anomalies in your data. 3) An example scenario where we talk through supervised and unsupervised machine learning for network traffic analysis to show the difficulties with those approaches and finally explore a concept called belief networks that bear a lot of promise to enhance our detection capabilities in security by leveraging export knowledge more closely. And if you plan to test the the vulnerability of your network, make use of Wifi Pineapple testing tool.

I keep mentioning that algorithms are dangerous. Dangerous in the sense that they might give you a false sense of security or in the worst case even decrease your security quite significantly. Here are some questions you can use to self-assess whether you are ready and ‘qualified’ to use data science or ‘advanced’ algorithms like machine learning or clustering to find anomalies in your data:

• Do you know what the difference is between supervised and unsupervised machine learning?
• Can you describe what a distance function is?
• In data science we often look at two types of data: categorical and numerical. What are port numbers? What are user names? And what are IP sequence numbers?
• In your data set you see traffic from port 0. Can you explain that?
• You see traffic from port 80. What’s a likely explanation of that? Bonus points if you can come up with two answers.
• How do you go about selecting a clustering algorithm?
• What’s the explainability problem in deep learning?
• How do you acquire labeled network data sets (netflows or pcaps)?
• Name three data cleanliness problems that you need to account for before running any algorithms?
• When running k-means, do you have to normalize your numerical inputs?
• Does k-means support categorical features?
• What is the difference between a feature, data field, and a log record?

If you can’t answer the above questions, you might want to rethink your data science aspirations and come to my talk on Thursday to hopefully walk away with answers to the above questions.

Update 8/13/18: Added presentation slides

Late June, my alma mater organized an event in Brooklyn with the title: “ETH Meets New York”. The topic of the evening was “Security Technologies Enabling the Future: From Blockchain to IoT”. I was one of the speakers talking about “AI in Practice – What We Learned in Cyber Security”. The video of the talk is available online. It’s a short 10 minutes where I discuss some of the problems with AI in cyber, and outline how expert knowledge is more important than algorithms when it comes to detecting malicious actors in our systems.

Spark your interest? Don’t miss my talk at BlackHat next month where we will have an hour to explore the topics of analytics, machine learning, and artificial intelligence in cyber. I recorded a brief teaser video to help you understand what I will be covering.

A quick summary of the talks can be found in this summary blog post.

Another year, another Security Analytics Summit. This year Kaspersky gathered an amazing set of speakers in Cancun, Mexico. I presented on AI & ML in Cyber Security – Why Algorithms Are Dangerous. I was really pleased how well the talk was received and it was super fun to see the storm that emerged on Twitter where people started discussing AI and ML.

Here are a couple of tweets that attendees of my talk tweeted out (thanks everyone!):

The following are some more impressions from the conference:

And here are the slides: