Speaker 1: Welcome to the Deep Dive, where the show that bigs
Speaker 1: through stacks of sources to give you the key takeaways,
Speaker 1: making sure you're well informed. And today, Wow, we are
Speaker 1: plunging into a pretty intense digital battlefield. The stakes are
Speaker 1: incredibly high. We're talking about malware, you know, that nasty
Speaker 1: software designed purely to disrupt damage steel and the scale,
Speaker 1: the sheer scale of this problem, it's just staggering. Get this.
Speaker 1: Every single day, something like three hundred and fifty thousand
Speaker 1: new instances of malicious software pop up detected. Just think
Speaker 1: about that number for a second. And back in twenty eighteen,
Speaker 1: over six hundred and sixty nine million new variants were
Speaker 1: spotted in that year alone. This isn't just annoying pop ups.
Speaker 1: It's a huge financial hit businesses. We're spending on average
Speaker 1: two point four million US dollars back in twenty eighteen
Speaker 1: twenty nineteen just fighting malware and web attacks. So our
Speaker 1: mission for this deep dive is to really get into
Speaker 1: how cutting edge artificial intelligence, specifically deep learning, is being
Speaker 1: used as well a crucial line of defense. We want
Speaker 1: to explore how these intelligence systems are learning, adapting, maybe
Speaker 1: even predicting threats that the old way just can't catch.
Speaker 1: It's not just about spotting the known bad guys anymore, right,
Speaker 1: it's about anticipating the unknown, the brand new stuff.
Speaker 2: That's precisely it. You know, for years cybersecurity really leaned
Speaker 2: heavily on what's called signature based detection. You could think
Speaker 2: of it like having a huge photo album of known criminals.
Speaker 2: It's great for recognizing malware we've already seen and fingerprinted,
Speaker 2: very efficient for that, but it's big weakness. It's achilles heel.
Speaker 2: Really is the zero day attack, ah.
Speaker 1: The infamous zero days Exactly?
Speaker 2: These are completely new malware variants never seen before. They
Speaker 2: don't have a signature, no photo in the album to match.
Speaker 2: And that's exactly where AI and deep learning are stepping in.
Speaker 2: They use much more sophisticated methods like looking at dynamic
Speaker 2: behavior to spot malicious intent, even if the code itself
Speaker 2: is brand new.
Speaker 1: Okay, let's unpack that a bit, Starting with like the
Speaker 1: raw materials, how do we even study malware? I gather
Speaker 1: there are two main ways, static and dynamic analysis.
Speaker 3: That's right.
Speaker 2: Static analysis is well like examining a suspicious package without
Speaker 2: actually opening it. You're looking at the code itself without
Speaker 2: running it, things like library calls that might make tech
Speaker 2: strings inside it, byte sequences, maybe the sequence of API calls.
Speaker 2: It seems designed to make signature based detection that mostly
Speaker 2: uses this static data, but as we said, it totally
Speaker 2: misses new malware because there's no existing signature, right, no
Speaker 2: mugshot exactly. And then you have dynamic analysis. This is
Speaker 2: where you actually detonate the malware so to speak.
Speaker 1: You run it sounds risky.
Speaker 2: Well, you run it or emulate it in a very
Speaker 2: controlled environment a sandbox usually, and you watch what it does.
Speaker 2: So you track the actual API calls it makes, how
Speaker 2: it interacts with the system, maybe even low level hardware
Speaker 2: events for unknown malware. Seeing its behavior what it actually
Speaker 2: does is absolutely critical. It's not just about its blueprint.
Speaker 1: But it's actions makes sense, and I heard some people
Speaker 1: are even combining them like a hybrid approach.
Speaker 2: Yes, absolutely. Hybrid analysis tries to get the best of
Speaker 2: both worlds, looking at both the static structure and the
Speaker 2: dynamic bee behavior to build more complete picture.
Speaker 3: Things like mal DNA try to do this.
Speaker 1: So you mentioned API calls and other things you look for.
Speaker 1: These are the features, right, The specific clues precisely.
Speaker 2: Features are the specific characteristics we extract. And API call
Speaker 2: sequences are incredibly valuable. Why because they directly show what
Speaker 2: a program is trying to do. Interact with files, connect
Speaker 2: to the network, modify the system. API calls reveal.
Speaker 1: That ah okay, And the key.
Speaker 2: Insight here is that the order of these calls often
Speaker 2: screams malicious intent. Think about it opening a file, encrypting it,
Speaker 2: then deleting the original. That sequence tells a very different
Speaker 2: story than just opening and reading a file.
Speaker 1: Yeah, it definitely sounds like ransomware exactly.
Speaker 2: So researchers use techniques like n grams, which is just
Speaker 2: a fancy way of saying they look at short ordered
Speaker 2: sequences of calls, like pairs or triplets to capture this
Speaker 2: vital order information. Opcode sequences are another important feature too.
Speaker 2: Those are the really low level machine instructions giving insight
Speaker 2: into the program's core functions.
Speaker 1: So how do analysts actually get this data? What tools
Speaker 1: are they using?
Speaker 2: Ah, there's a whole toolkit for static analysis. You have
Speaker 2: dissemblers and debuggers like ida pro or allidobig. They let
Speaker 2: you peek inside the compiled code. See the assembly instructions
Speaker 2: extract op codes, potential API calls, and for.
Speaker 1: The dynamic side, the sandbox stuff right.
Speaker 2: Tools like API monitor are used to track those API
Speaker 2: calls live, but you usually need to run the malware
Speaker 2: inside a virtual machine or sandbox to contain it. Buster
Speaker 2: Sandbox Analyzer BSA and similar tools like CW sandbox are
Speaker 2: designed for exactly that. They run the malware safely and
Speaker 2: log everything it does, file changes, network connections, API calls.
Speaker 2: They're even more advanced tools like ether, which use hardware virtualization.
Speaker 2: They kind of sit outside the operating system the malware
Speaker 2: is running in, making them much harder for the malware
Speaker 2: to detect.
Speaker 1: Okay, this is fascinating. So you've got all this raw data,
Speaker 1: API sequences, op codes, behaviors. Now how do you actually
Speaker 1: feed this into an AI? How does the machine see
Speaker 1: the malwa?
Speaker 3: Well, this is.
Speaker 2: Where some really creative approaches come in. One of the
Speaker 2: most surprising ones is malware visualization.
Speaker 1: Visualization you mean like charts and graphs.
Speaker 2: No, literally turning the malware code the binary file itself
Speaker 2: into an image, usually a grayscale image.
Speaker 1: Wait, what turning code into a picture? How does that
Speaker 1: even work?
Speaker 2: Or why it sounds bizarre? I know, but researchers found
Speaker 2: that malware samples from the same family, even if they
Speaker 2: look different in code, often end up having similar textures
Speaker 2: and structural patterns when you represent their binary data as pixels.
Speaker 1: In an image like a visual fingerprint.
Speaker 2: Kind of yeah, kindred attributes as some call it. And
Speaker 2: the brilliant part is this lets us use incredibly powerful
Speaker 2: deep learning models that were originally designed for image recognition.
Speaker 1: You mean, like the AI that recognizes cats and photos.
Speaker 2: Exactly, Convolutional neural networks or CNNs. They're designed to find
Speaker 2: patterns in images, edges, textures, shapes, increasingly complex features. So
Speaker 2: by turning malware into an image, we can train as
Speaker 2: c N to spot the visual hallmarks of malicious code,
Speaker 2: even if it has no obvious image component itself. It's
Speaker 2: surprisingly effective.
Speaker 1: Wow. Okay, that's pretty cool. So CNN's for the image approach.
Speaker 1: What other AI tools are in the box?
Speaker 2: Well, for data that's sequential where the order is crucial,
Speaker 2: like those API call sequences or op code sequences we
Speaker 2: talked about. With these different architectures, recurrent neural networks or
Speaker 2: RNNs are designed specifically for sequential data, Okay, and within
Speaker 2: RNNs variants like lstm's long short term memory networks are
Speaker 2: really powerful. They have mechanisms to remember information over longer sequences,
Speaker 2: which is perfect for tracking complex behaviors that unfold over.
Speaker 1: Time, so they can connect an early action with.
Speaker 3: A later one precisely.
Speaker 2: LSTMs are actually quite successful commercially. Another popular variation is
Speaker 2: the GRU or gated recurrent unit, which is a bit
Speaker 2: simpler than LSTM but often performs just as well. Both
Speaker 2: LSTMs and grus have shown really significant improvements in detecting malware,
Speaker 2: even things like spotting cybersecurity events based on say, patterns
Speaker 2: and social media messages over time.
Speaker 1: Interesting any other architectures.
Speaker 2: Definitely there are residual networks or resonants. Their key innovation
Speaker 2: is allowing the network to learn identity mappings, basically letting
Speaker 2: the signal skip layers if needed. This helps train much
Speaker 2: deeper networks without running into problems like vanishing gradients where
Speaker 2: the signal gets too weak to train the.
Speaker 3: Early layers effectively.
Speaker 2: It's kind of inspired by how neurons connect in the brain.
Speaker 1: Deeper networks mean potentially learning more complex patterns.
Speaker 3: I guess that's the idea.
Speaker 2: And then there are jans generative adversarial networks.
Speaker 1: These are fascinating adversarial sounds intense.
Speaker 2: It is in a way you have two networks competing.
Speaker 2: A generator tries to create fake data like fake malware samples,
Speaker 2: and a discriminator tries to tell the generator's fakes apart
Speaker 2: from real.
Speaker 1: Dat like a game of cat and mouse.
Speaker 2: Exactly a mini max game. The generator gets better at
Speaker 2: fooling the discriminator, and the discriminator gets better at spotting fakes.
Speaker 2: The really exciting part about cans is their potential for
Speaker 2: things like zero day malware detection, because the generator might
Speaker 2: create novel malicious patterns or even we can use them
Speaker 2: in the lab to generate challenging new threats to test
Speaker 2: our defenses before similar things appear in the wild. It's
Speaker 2: like a digital.
Speaker 1: Sparring partner proactive defense. I like that. What about understanding
Speaker 1: the words of malware like op codes or API calls?
Speaker 3: Ah?
Speaker 2: Yes, that's where word embedding techniques come in, like word
Speaker 2: two vec, or even approaches based on hidden Markov models
Speaker 2: like HMM two vec. The core idea is similar to
Speaker 2: how language models understand words and sentences. You treat op
Speaker 2: codes or API calls as words. These techniques learn to
Speaker 2: represent these words as numerical vectors in a high dimensional.
Speaker 1: Space, vectors like points on a map.
Speaker 2: Sort of yes, And the key is that words used
Speaker 2: in similar contexts, like API calls that often appear together
Speaker 2: in malicious sequences and then closer together in this vector space.
Speaker 2: Word two vec, for example, trained on just a shallow
Speaker 2: neural network, can capture really meaningful relationships. It learns the
Speaker 2: meaning or function of an op code from how it's
Speaker 2: used alongside others, so.
Speaker 1: It groups similar functions together automatically.
Speaker 2: Essentially, yes, it captures semantic relationships. There are others too, briefly,
Speaker 2: like extreme learning machines or elms. These are super fast
Speaker 2: because they don't use the typical backpropagation training method solving
Speaker 2: linear equations instead.
Speaker 1: Wow, okay, so it's a really diverse AI toolkit. CNNs
Speaker 1: for images, RNNs for sequences, jans for generating challenges, embeddings
Speaker 1: for meaning.
Speaker 2: Exactly, they're not just generic algorithms, they're specific tools honed
Speaker 2: for different facets of the malware problem. Each has its
Speaker 2: strengths depending on the data and the goal.
Speaker 1: Right, It's like having different kinds of sensors. And analyzers.
Speaker 1: So let's talk about where this is actually being deployed.
Speaker 1: Where are these AI techniques making a real difference on
Speaker 1: the front lines?
Speaker 2: Good question. A huge area is Android malware detection. Think
Speaker 2: about it, billions of smartphones out there. It's a massive target.
Speaker 1: Yeah, my phone feels like my life sometimes, right.
Speaker 2: So AI system analyze Android apps using static, dynamic or
Speaker 2: hybrid methods. They look for suspicious API calls and app
Speaker 2: shouldn't need like pt trace for debugging other processes, or
Speaker 2: mkdr to create directories unexpectedly or connect for unusual network activity.
Speaker 2: They also flag risky permission requests. Does that simple game
Speaker 2: really need send SS permission or read contacts or system
Speaker 2: milert window to draw over other apps. AI learns the
Speaker 2: patterns of legitimate apps versus malware.
Speaker 1: That makes sense. What about newer areas. I keep hearing
Speaker 1: about smart cars and potential hacking.
Speaker 2: That's a critical emerging frontier. Connected vehicle security part of
Speaker 2: intelligent transportation systems or rights. Modern cars are basically computers
Speaker 2: on wheels, packed with sensors embedded devices, communicating wirelessly V
Speaker 2: two V vehicle to vehicle, V two I vehicle to.
Speaker 1: Infrastructure, which means more tax surfaces.
Speaker 2: Exactly, and the risks are serious. Denial of service DOSS
Speaker 2: or distributed denial of service DAS attacks could cripple communication.
Speaker 2: Imagine jamming traffic safety messages or preventing cars from coordinating
Speaker 2: at intersections.
Speaker 1: That sounds potentially catastrophic.
Speaker 3: It could be so.
Speaker 2: AI is being developed to monitor the complex network traffic
Speaker 2: in and around vehicles, looking for anomalies communication patterns that
Speaker 2: indicate jamming, spoofing, or attempts to compromise vehicle systems.
Speaker 1: Okay, cars, phones, What about the cloud? So much runs
Speaker 1: there now?
Speaker 2: Absolutely, cloud infrastructure protection is vital. A major threat is
Speaker 2: malware injection into virtual machines vms, because cloud platforms often
Speaker 2: automatically provision lots of similar vms. If one type gets compromised,
Speaker 2: malware can potentially spread very easily to others configured the same.
Speaker 1: Way, like an infection spreading through identical twins.
Speaker 2: A good analogy. AI techniques, sometimes even simpler machine learning
Speaker 2: like keeneurest neighbors or local outlier factor can monitor the
Speaker 2: hypervisor the software managing the vms. They look at performance metrics,
Speaker 2: CPU load, memory usage, network IO. Anomalies in these patterns
Speaker 2: can indicate a VM has been compromised and is doing
Speaker 2: something malicious.
Speaker 1: Like a fever chart for the VM.
Speaker 2: Kind of yeah, though it can be less effective against
Speaker 2: low and slow malware that tries very hard to hide
Speaker 2: its activity and not cause obvious performance spikes.
Speaker 1: Right stealthy attacks. What about just general network defense like
Speaker 1: intrusion detection systems.
Speaker 2: Yes, IDs are a classic battleground where AI is making inroads.
Speaker 2: Instead of just relying on known attack signatures, AI can
Speaker 2: perform anomaly detection on system of ventlogs I think database logs,
Speaker 2: operating system logs. AI models, particularly auto encoders, can learn
Speaker 2: what normal activity looks like for a specific user or.
Speaker 1: System, establishing a baseline exactly.
Speaker 2: Then any significant deviation from that learned normality gets flagged
Speaker 2: as suspicious. It might be an attacker trying to escalate
Speaker 2: privileges or moving laterally through the network. Some systems even
Speaker 2: use hybrid approaches, maybe combining deep learning like auto encoders
Speaker 2: for complex dependent data with traditional machine learning like support
Speaker 2: vector machines for simpler independent data like timestamps.
Speaker 1: In different angles. And what about something seemingly simpler like spam?
Speaker 2: Ah, but spam gets clever too. Image spam is a
Speaker 2: big one. Spammers embed their malicious messages or links inside images,
Speaker 2: specifically to bypass text based filters.
Speaker 1: Oh right, so the filter doesn't see the text correct.
Speaker 2: But AI, especially CNN's again often combined with transfer learning
Speaker 2: models like VGG nineteen, which are pre trained on millions
Speaker 2: of images, can fight back effectively. They don't just read text.
Speaker 2: They analyze the image itself. It's metadata like height, with
Speaker 2: color statistics, mean color skewness, texture patterns, even shapes detected
Speaker 2: using edge filters. They learn the visual characteristics of spam.
Speaker 1: Images, so the AI sees the spamminess in the image itself.
Speaker 1: That's clever.
Speaker 2: It shows how AI can tackle threats designed to evade
Speaker 2: older methods.
Speaker 1: It really does feel like a constant arms race, though,
Speaker 1: as our AI gets better at spotting malware.
Speaker 2: The attackers start using AI themselves to create better malware.
Speaker 3: It's an unavoidable cycle.
Speaker 1: Which leads to this concept I've read about adversarial examples
Speaker 1: sounds ominous.
Speaker 2: It's a major challenge. Adversarial examples or aes or inputs
Speaker 2: could be an image, could be a data file, could
Speaker 2: be a software binary that are intentionally but very slightly modified.
Speaker 2: The modification is often tiny, maybe even imperceptible to a human,
Speaker 2: but it's specifically crafted to fool an AI classification.
Speaker 4: Model to make the AI misjudge it exactly in the
Speaker 4: malware context, attacker could take a genuinely malicious file, tweak
Speaker 4: it just a little bit, maybe adding some junk code,
Speaker 4: changing a few bytes so that our AI detector now
Speaker 4: classifies it as benign.
Speaker 1: But it still does the bad stuff.
Speaker 2: Crucially, yes, it preserves its original malicious functionality while wearing
Speaker 2: this AI fooling camouflage. It highlights that even powerful AI
Speaker 2: models can have these exploitable blind spots. There were even
Speaker 2: techniques to create universal perturbations that can fool a model
Speaker 2: across many different inputs.
Speaker 1: That's worrying. So the malware itself is also evolving, partly
Speaker 1: in respect through our defenses.
Speaker 2: Constantly, and machine learning is actually being used to track
Speaker 2: this evolution. Researchers analyze malware families over time, perhaps looking
Speaker 2: at op code sequences within specific time windows. They use techniques,
Speaker 2: maybe even simpler ones like linear SVMs, to detect points
Speaker 2: where a malware family significantly changed its characteristics.
Speaker 1: Like finding evolutionary branches in the malware family tree.
Speaker 2: Precisely understanding how threats adapt helps us anticipate future shifts
Speaker 2: in their tactics or structure.
Speaker 1: There must be practical challenges in just studying all this malware,
Speaker 1: especially older stuff.
Speaker 2: For live threats, oh absolutely, Handling live malware is inherently risky,
Speaker 2: and for older samples, the infrastructure they relied on, especially
Speaker 2: their command and control server C two servers, is often
Speaker 2: long gone, so you.
Speaker 1: Can't see their full behavior, not easily.
Speaker 2: That's where C two server emulators become really useful. These
Speaker 2: are tools researchers build to mimic the original C two server.
Speaker 2: This allows them to run the malware, even historical samples,
Speaker 2: in an isolated lab network and observe its full range
Speaker 2: of capability. Because the malware thinks it's talking to its
Speaker 2: real controller, you can extract features, understand its entire life cycle.
Speaker 1: You trick the malware into showing its hand.
Speaker 3: Essentially.
Speaker 2: Yes, sometimes you might even need to slightly patch the
Speaker 2: malware itself, maybe to bypass some anti analysis checks it has,
Speaker 2: or if say an encryption key needed for its C
Speaker 2: two communication was lost to time, like with some old
Speaker 2: cryptol locker variants.
Speaker 1: It's a complex process. Now, with all this focus on AI,
Speaker 1: this AI mania, almost are their downsides things we need
Speaker 1: to be cautious about.
Speaker 3: That's a very important point.
Speaker 2: Yes, while AI is powerful, we need perspective. Machine learning
Speaker 2: is data driven, but it's not magic. Humans still make
Speaker 2: crucial decisions, things like choosing the right model architecture, setting
Speaker 2: parameters like the number of hidden states in an HMM,
Speaker 2: selecting the kernel function for an SVM. These aren't automatic.
Speaker 2: They require human expertise and significantly impact performance.
Speaker 1: Right. The human element is still key in setting it.
Speaker 2: Up, definitely, and there are practical constraints. More data is
Speaker 2: often better, but it needs more computing power, more storage,
Speaker 2: longer training times. That's a real bottleneck. Plus, some highly
Speaker 2: tuned models can become.
Speaker 3: Very specific to the data set they were trained on.
Speaker 2: They might not generalize well to new, slightly different data,
Speaker 2: which is a constant issue with evolving malware. There's a
Speaker 2: real need for more robust, more generic deep learning approaches.
Speaker 1: Adaptability is crucial and.
Speaker 2: Another big challenge, maybe less technical, but just as important,
Speaker 2: is the lack of a unified standard for malware taxonomy.
Speaker 2: Different anti virus vendors often label the same threat differently,
Speaker 2: even with tools like virus Total that aggregate results. Correlating
Speaker 2: threats globally and building truly comprehensive data sets is harder
Speaker 2: than it should be because we don't always speak the
Speaker 2: same language when naming things.
Speaker 1: That makes collaborative defense tricky.
Speaker 2: It does, and one final sort of intriguing point. Researchers
Speaker 2: have found that different methods for selecting the most important features,
Speaker 2: like those API calls or op codes, can sometimes pick
Speaker 2: vastly different sets of features.
Speaker 1: But they still work.
Speaker 2: But they still end up achieving similar classification accuracy, which
Speaker 2: raises a fascinating question. Are these methods truly finding the
Speaker 2: single best set of features or are there potentially multiple
Speaker 2: different sets of features that are almost equally good at
Speaker 2: identifying malware. It makes you wonder about what the AI
Speaker 2: is really learning.
Speaker 1: That is interesting. It suggests maybe there isn't one perfect
Speaker 1: way to see the malware.
Speaker 2: Okay, we have definitely covered a lot of ground in
Speaker 2: this deep dive. We've seen how AI and deep learning
Speaker 2: are genuinely transforming the fight against malware. From visualizing code
Speaker 2: as images, which is still kind of blowing my mind yea,
Speaker 2: to understanding behavior through sequences and protecting everything from our
Speaker 2: phones and cars to the cloud. It's clearly a super dynamic,
Speaker 2: constantly evolving field.
Speaker 1: It absolutely is, and I think the key takeaway is
Speaker 1: the sheer complexity of this ongoing cybersecurity arms race. AI
Speaker 1: gives us incredibly powerful new tools, yes, but the ingenuity
Speaker 1: attackers means it's never solved. Critical thinking, human oversight, asking
Speaker 1: the right questions, understanding the limitations of the AI, these
Speaker 1: remain completely indispensable. It's very much a human machine partnership,
Speaker 1: absolutely a partnership against an ever adapting adversary. So maybe
Speaker 1: the thought to leave you, our listener with, is this,
Speaker 1: As AI gets better and better at spotting the hidden patterns,
Speaker 1: the secret signatures of malicious code, what new forms of
Speaker 1: digital camouflage will the attackers invent next? And will our
Speaker 1: intelligent defenses always find the optimal way to adapt or
Speaker 1: just one of many good enough ways. Constantly pushing the
Speaker 1: very boundaries of what these intelligent systems can even perceive
Speaker 1: is definitely something to think about.