Speaker 1: Welcome to the deep dive. You know, trying to keep
Speaker 1: up with cybersecurity and forensics. It can feel like information
Speaker 1: overload sometimes it just moves.
Speaker 2: So fast, it really does. And we've got this great
Speaker 2: collection of sources mostly based around the book Advanced Smart
Speaker 2: Computing Technologies and Cybersecurity and.
Speaker 1: Forensics, right, and it dives into how tech like AI, IoT, blockchain,
Speaker 1: machine learning, how they're not just for defense but really
Speaker 1: changing how investigations happen exactly.
Speaker 2: It's about securing data and investigating digital crime in totally
Speaker 2: new ways.
Speaker 1: So our mission here and we're sticking just to what
Speaker 1: these sources say, is to pull out the really key insights,
Speaker 1: the surprising stuff too.
Speaker 2: Yeah, kind of cut through the noise for you. Well,
Speaker 2: hit several different areas, but you'll see how they all connect.
Speaker 1: Absolutely, Like have you ever really thought about how much
Speaker 1: your phone knows and how that could be evidence? Or
Speaker 1: how a tiny hardware glitch might completely wreck strong encryption.
Speaker 2: We'll get into all of that based purely on this material.
Speaker 1: Okay, so where should we start? AI seems like the
Speaker 1: obvious place.
Speaker 2: It's everywhere, definitely, Let's start with AI, specifically machine learning
Speaker 2: mL and deep learning.
Speaker 1: DL Okay, the sources jump right in calling mL and
Speaker 1: DL well revolutionary for information security.
Speaker 2: They really frame it that way. I mean, think about
Speaker 2: the sheer volume of security logs, network traffic. It's immense.
Speaker 2: These technologies are brilliant at spotting patterns and all.
Speaker 1: That patterns like what specifically.
Speaker 2: Things like identifying commands used by cyber criminals, predicting potential attacks,
Speaker 2: or even flagging malicious programs just by looking at the
Speaker 2: structure of the code itself.
Speaker 1: That pattern recognition does seem crucial, especially with threats changing constantly.
Speaker 1: The sources mentioned processing millions of daily logins filtering threats
Speaker 1: much better.
Speaker 2: Yeah, higher detection rates and fewer false alarms, fewer false positives.
Speaker 2: That's a big deal.
Speaker 1: Saves a lot of time and effort, I.
Speaker 2: Imagine absolutely, And there are concrete examples, like using it
Speaker 2: to spot malware signatures or finding crime patterns hidden in
Speaker 2: huge data sets.
Speaker 1: The sources mentioned some of the specific tech names too,
Speaker 1: like convolutional neural networks RNNs gns.
Speaker 2: Yeah, convents where current neural networks generative adversarial networks. We
Speaker 2: don't need to deep dive on how they work, but
Speaker 2: just knowing the terms helps you know.
Speaker 1: Right, it gives you a sense of the complexity.
Speaker 2: But here's where it gets really, really interesting and maybe
Speaker 2: a bit unsettling. Even these super powerful DL models, the
Speaker 2: ones built for security, they could be attacked too.
Speaker 1: Wait, you can attack the AI model itself, not just
Speaker 1: the system it's protecting. How does that even work?
Speaker 2: Well, the sources lay out a few ways. One is
Speaker 2: called data poisoning. Basically, you sneak bad data into the
Speaker 2: training set.
Speaker 1: To bad data like fake information.
Speaker 2: Exactly inaccurate stuff, so the model learns the wrong lessons
Speaker 2: or its decision lines get shifted, making it misclassify things later.
Speaker 1: Huh, So you're corrupting it from the inside during training precisely.
Speaker 2: Then there are backdoor attacks. This is clever. An attacker
Speaker 2: in beds a kind of hidden trigger into the model.
Speaker 2: A trigger, yeah, like a secret pattern or connection. The
Speaker 2: model works normally most of the time, but if the
Speaker 2: attacker sends an input with that specific trigger, boom, the
Speaker 2: model does something malicious.
Speaker 1: Wow, like a sleeper agent inside the AI. That's kind
Speaker 1: of scary, it is.
Speaker 2: And the third one is adversarial examples. The source calls
Speaker 2: them visual illusions. For computers.
Speaker 1: Visual illusion think of.
Speaker 2: An image or some input that looks perfectly fine to
Speaker 2: us humans, but it has tiny, almost invisible changes deliberately
Speaker 2: crafted to fool the algorithm.
Speaker 1: So you slightly tweak something and the AI suddenly thinks
Speaker 1: a stop sign is a I don't know a cat.
Speaker 2: Sort of yeah, it causes the AI to make a
Speaker 2: mistake of misclassification, even though the change is minuscule to us.
Speaker 1: So the tools we build for defense create their own unique,
Speaker 1: subtle weak spots. That feels like a constant theme here.
Speaker 2: It absolutely is. Yeah, you can't just build the AI.
Speaker 2: You have to constantly think about securing the AI itself.
Speaker 1: It's arms, and it's not just these abstract models, right,
Speaker 1: It's about securing the actual devices we use every day,
Speaker 1: our phones, the systems they connect to.
Speaker 2: Exactly, which brings us nicely to well, the devices in
Speaker 2: our pockets. Mobile forensics.
Speaker 1: Ah yeah. Chapter four calls phones treasure troves of evidence,
Speaker 1: and it makes perfect sense. Smartphones log everything, apps, web history,
Speaker 1: purchases where you've been.
Speaker 2: So much more than old feature phones. The sources listed out, contacts, messages,
Speaker 2: call logs, web history, social media activity. It's an incredibly
Speaker 2: detailed digital diary.
Speaker 1: And the AI the MLDL stuff we just talked about
Speaker 1: that comes back here in the forensics part.
Speaker 2: Right, analyzing all that mobile data, you can use mL
Speaker 2: to trace crime patterns, maybe figure out someone's sentiment from
Speaker 2: their texts, or analyze images found on the device.
Speaker 1: There's that example with the word clouds from group chats,
Speaker 1: visualizing common keywords. Could be drugs, could be anything illicit.
Speaker 2: Yeah, it's a neat way to quickly spot potential leads
Speaker 2: in huge volumes of text messages.
Speaker 1: And there's case study about the fraudster using hacked email
Speaker 1: details shows how critical these digital trails on phones and
Speaker 1: email really are.
Speaker 2: Definitely, but phones don't live in isolation anymore, so much
Speaker 2: happens in the cloud, right.
Speaker 1: Mobile cloud computing MCC, where the storage and the heavy
Speaker 1: processing happens off the device.
Speaker 2: YEP. Chapter twelve explains it just like that, your phone
Speaker 2: becomes more of an interface to cloud services.
Speaker 1: But that creates its own set of problems, doesn't it.
Speaker 1: The source mentions MCC environments are really distributed, always changing.
Speaker 2: Exactly, so a simple centralized security design just doesn't work well.
Speaker 2: You need something scalable, adaptable.
Speaker 1: And other challenges pop up too, like a battery life
Speaker 1: on the phone, keeping data secure and private when it's
Speaker 1: bouncing around, keeping users happy with performance.
Speaker 2: It's a long list. Quality of service is a big one, yea.
Speaker 2: The source talks about using dynamic device info, mobile agents,
Speaker 2: things like that to try and tighten up access control
Speaker 2: and keep data confidential.
Speaker 1: Okay, so from the single phone we move up. Let's
Speaker 1: scale up even more smart cities.
Speaker 2: Right. Chapter seven defines a smart city as more than
Speaker 2: just connected. It's about using technology ICT for better administration,
Speaker 2: managing resources. Safety like those intelligent traffic lights that adapt
Speaker 2: to traffic.
Speaker 1: Flow and sensors are key here, right. IoT devices everywhere
Speaker 1: collecting data absolutely fundamental.
Speaker 2: Now that's the huge security challenge securing this massive network
Speaker 2: of often quite simple, low power IoT devices. How do
Speaker 2: you stop malware spreading? How do you protect all the
Speaker 2: data they generate.
Speaker 1: Especially when those devices might not have much processing power
Speaker 1: for complex security themselves.
Speaker 2: Precisely so, privacy protection often needs to happen efficiently, maybe
Speaker 2: closer to the device or user you need, things like
Speaker 2: data minimization, only collect what you need, and really tight
Speaker 2: access controls.
Speaker 1: The source also mentions that trend away from passwords towards
Speaker 1: biometrics like facial recognition, which has its own security implications
Speaker 1: of course for sure.
Speaker 2: And even within the smart city infrastructure, the cloud systems
Speaker 2: they rely on have vulnerabilities, misconfigurations, weak access management. These
Speaker 2: are common problems.
Speaker 1: Mentioned, and it connects back to recent events too, like
Speaker 1: the pandemic pushing faster digital transformation and cloud use forcing
Speaker 1: a rethink of security policies.
Speaker 2: It's all interconnected, the device, the network, the cloud, the
Speaker 2: city infrastructure.
Speaker 1: Okay, so we've gone from the phone to the city.
Speaker 1: What about digging right down into the computer itself, like
Speaker 1: the absolute core.
Speaker 2: Ah, you mean the kernel. Chapter five calls it the
Speaker 2: heart of the operating system. It's that crucial piece of
Speaker 2: software connecting the hardware and all the other software always
Speaker 2: running the.
Speaker 1: Bit we don't directly interact with. We just see the logs.
Speaker 1: And they're different types, right, micro monolithic.
Speaker 2: YEP, micro monolithic, XO, hybrid nanokernels are mentioned, different design philosophies.
Speaker 1: So how do you assess risk down at that level?
Speaker 1: If there's a vulnerability in the kernel that seems really bad,
Speaker 1: it can be very serious.
Speaker 2: That's where the sources bring in the Common Vulnerability Scoring
Speaker 2: System CBSS.
Speaker 1: CBSS. Okay, what's that? Do just give it a score
Speaker 1: out of ten.
Speaker 2: It's a bit more nuanced than that, but yeah, it
Speaker 2: provides a standardized score. It helps prioritize. It looks at
Speaker 2: things called base metrics, characteristics of the vulnerability itself, like.
Speaker 1: How easy it is to exploit exactly.
Speaker 2: Like the attack vector. Does the attacker need to be
Speaker 2: local or can they do it remotely? Attack complexity, how
Speaker 2: hard is it? Privileges require? Do they need admin rights already?
Speaker 2: User interaction? Does the user need to click something? Scope?
Speaker 2: Does exploiting this affect other parts of the system?
Speaker 1: Okay, that makes sense? And then there's the impact.
Speaker 2: Right, the impact metrics? Yeah, what gets damaged if it
Speaker 2: is exploited? Confidentiality? Is secret data exposed? Integrity? Can data
Speaker 2: be changed? Availability? Does a crash the system?
Speaker 1: So CBSS weighs all those factors to give a score.
Speaker 1: Helps defenders figure out what to.
Speaker 2: Patch first, precisely and guess what comes up again? Here?
Speaker 1: Let me guess? Machine learning?
Speaker 2: You got it. Chapter five mentions using mL, specifically an
Speaker 2: algorithm called random forest to analyze kernel vulnerabilities. They can
Speaker 2: even build a model to predict the potential score or risk.
Speaker 1: Wow, using AI to analyze the core OS code for weaknesses.
Speaker 2: Yeah, and they talk about tuning the algorithm, adjusting parameters
Speaker 2: like the number of decision trees to make the prediction better.
Speaker 2: It shows how deep these computational techniques go.
Speaker 1: It really does, from AI attacking AI, to AI analyzing
Speaker 1: kernels to something completely different DNA cryptography using DNA. That
Speaker 1: sounds like.
Speaker 2: Sci fi It does, doesn't it, But it's right there
Speaker 2: in chapter three, it's fascinating. They start by explaining standard
Speaker 2: crypto first though.
Speaker 1: Okay, yeah, symmetric versus asymmetric.
Speaker 2: Right, Symmetric uses the same key for locking and unlocking
Speaker 2: like AES. Simple, but if that one key gets out,
Speaker 2: you're exposed.
Speaker 1: That's the man in the middle attack risk if someone
Speaker 1: intercepts the key being shared exactly.
Speaker 2: Asymmetric is different. I think RISA two keys, a public
Speaker 2: one everyone can use to encrypt messages to you, like
Speaker 2: a mailbox slot and a private one, only you have
Speaker 2: to decrypt them.
Speaker 1: Mailbox key much safer for key sharing because the private
Speaker 1: key never needs to be te transmitted. So DNA how
Speaker 1: does that fit in?
Speaker 2: Well? Chapter three mentions Leonard Adelman's work back in ninety
Speaker 2: four using actual DNA strands to solve complex math problems.
Speaker 1: Really actual biological DNA.
Speaker 2: Yeah, and DNA cryptography bills on that idea. It proposes
Speaker 2: using the structure of DNA, the ACGT bases to encode data.
Speaker 1: So instead of zeros and ones, you're using combinations of
Speaker 1: ACG and T. Maybe mapping binary to base pairs like
Speaker 1: A is zero zero, C is zero one.
Speaker 2: That's one method they described. Yes, the y is interesting too.
Speaker 2: DNA offers potentially massive data storage in a tiny space,
Speaker 2: ultra compact, the source says, and huge potential for parallel processing.
Speaker 1: Parallel processing.
Speaker 2: Yeah, I imagine billions or trillions of DNA strands processing
Speaker 2: information simultaneously compared to transistors on a chip. It's a
Speaker 2: different scale.
Speaker 1: Okay, huge storage, massive parallelism sounds good.
Speaker 2: What about security, The sources argue it could be very robust,
Speaker 2: especially against things like frequency analysis.
Speaker 1: Where attackers guess key is based on how often letters appear.
Speaker 2: Right. The proposed DNA system uses compression first, like Hoffman coding,
Speaker 2: then transforms the text into these DNA sequences or binary strings.
Speaker 2: The example they use makes the final distribution of ACEGT
Speaker 2: bases almost perfectly uniform.
Speaker 1: Ah, So if all the letters appear equally often. The
Speaker 1: attacker can't learn anything from frequency. It hides the original
Speaker 1: pattern exactly.
Speaker 2: It makes guessing much harder.
Speaker 1: Now. The detail that really stuck with me from that
Speaker 1: chapter the encryption key example.
Speaker 2: Oh yeah, left slightly the finic fox.
Speaker 1: The key was based on the genes of a finic fox. Volpaserta.
Speaker 1: Why a finic fox is their a reason the.
Speaker 2: Source just uses as the example key. It ties into
Speaker 2: the biological theme obviously. But yeah, the specific choice seems
Speaker 2: a bit random, but memorable.
Speaker 1: Definitely memorable. And they didn't just leave it as a theory,
Speaker 1: right They tried building something.
Speaker 2: Yeah, they mentioned trying a hardware implementation using artweno, some
Speaker 2: wireless stuff sensors, even visualizing it with node red for
Speaker 2: IoT monitoring. So they were exploring if it could.
Speaker 1: Be practical from biological theory to our Dueno boards. That's
Speaker 1: quite a leap.
Speaker 2: It really covers a lot of ground.
Speaker 1: So let's maybe touch on a couple more areas where
Speaker 1: this attack defense dynamic plays out, maybe back at the
Speaker 1: hardware level.
Speaker 2: Okay, yeah, back to IoT devices. Chapter fourteen brings up
Speaker 2: fault analysis attacks or FA fault analysis.
Speaker 1: What's it sounds it often is.
Speaker 2: It's not about network breaches, but about messing with the
Speaker 2: device's operation. You deliberately inject faults maybe voltage glitches, clock
Speaker 2: manipulation lasers, even while it's doing cryptography.
Speaker 1: Why would you do that?
Speaker 2: Because causing calculation errors can sometimes make the device leak
Speaker 2: information about the secret keys it's using. The faulty output
Speaker 2: gives clues.
Speaker 1: And for IoT devices, especially ones out in the open,
Speaker 1: like you said, cameras or sensors, physical access might be easier,
Speaker 1: making fault injections simpler.
Speaker 2: That's a major concern raised in the source. Yes, even
Speaker 2: if they use lightweight crypto designed for low power, fa
Speaker 2: can bypass it. If you can physically mess with the device.
Speaker 1: So how do you stop that? Put everything in a
Speaker 1: tamper proof box.
Speaker 2: That helps. But the source also talks about detecting the
Speaker 2: faults electronically techniques like concurrent Error Detection ced CED. It
Speaker 2: relies on redundancy. Maybe you do the calculation twice and
Speaker 2: compare results, or use redundant hardware or special data encoding.
Speaker 2: If a fault occurs, the redundancy lets you spot the error.
Speaker 1: Ah okay, so you build in checks to see if
Speaker 1: someone's glitching the system.
Speaker 2: Basically, yes, Now let's make a really big shift from
Speaker 2: hardware faults to people. The human side of investigations.
Speaker 1: You mean interrogating suspects. That feels miles away from DNA crypto.
Speaker 2: It is, but Chapter thirteen argues it's crucial. You can
Speaker 2: have all the fancy digital forensics in the world, but
Speaker 2: if the human investigation is flawed or the evidence isn't
Speaker 2: handled right legally, it falls apart. The source calls interrogation
Speaker 2: proceed to.
Speaker 1: Disputable, highlighting how tricky it is. It mentioned stages right,
Speaker 1: initial investigators, then maybe a review panel, a disciplinary committee
Speaker 1: for insiders.
Speaker 2: Yeah, there's a flow chart showing a process, but the
Speaker 2: huge emphasis is on independence, following local laws, following un standards,
Speaker 2: no intimidation, no harassment, definitely, no torture or public humiliation,
Speaker 2: basic human rights.
Speaker 1: But even with rules, it sounds like there are massive challenges.
Speaker 2: Oh. Absolutely. The source list things like suspects just walking
Speaker 2: out or causing disruptions, investigators resigning because cases get bungled politically,
Speaker 2: and just finding witnesses. People are scared, they get threatened,
Speaker 2: or they just can't afford to keep showing up for court.
Speaker 1: If they're low income, really practical difficult problems.
Speaker 2: Very much so. And there's a really stark line in
Speaker 2: there too about how the death of a key suspect
Speaker 2: can just end the whole case.
Speaker 1: Boof wow, that just stops everything.
Speaker 2: Yeah. So the chapter ends by calling for basically better
Speaker 2: ways to conduct interrogations that are effective but still fully
Speaker 2: legal and ethical under un standards.
Speaker 1: It's a sobering room that technology is only one part
Speaker 1: of the puzzle, the human element, the legal framework.
Speaker 2: Just as critical couldn't agree more. This deep dive has
Speaker 2: really covered a lot of ground, hasn't it.
Speaker 1: It really has when you pull it all together. We've
Speaker 1: seen advanced computing as this powerful force for both attack
Speaker 1: and defense, from AI fighting AI down to kernel code
Speaker 1: and tiny IoT hardware flaws, looked.
Speaker 2: At uncovering digital lives on phones, the complexities of smart cities,
Speaker 2: and even futuristic ideas like DNA encryption, and briefly touched
Speaker 2: on quantum computing's potential role too.
Speaker 1: And then contrasted all that tech with the very human,
Speaker 1: very challenging process of actually investigating the people behind the screens.
Speaker 2: It really highlights that cybersecurity and forensics are these incredibly
Speaker 2: dynamic fields. Every advance creates new opportunities, but also new vulnerabilities.
Speaker 2: It never stands still.
Speaker 1: Yeah, and understanding all these layers, from the AI down
Speaker 1: to the hardware, the code, the legal stuff, it feels
Speaker 1: essential just to navigate the world to which is what
Speaker 1: these sources really help unpack.
Speaker 2: It definitely leaves you with a lot to think about
Speaker 2: regarding the future trajectory, It really does.
Speaker 1: Yeah, you know, thinking about how fast AI is moving,
Speaker 1: quantum computing on the horizon, the absolute explosion of data
Speaker 1: from smart devices, smart cities. Are we heading for a
Speaker 1: future where cyber defense is less about people looking at
Speaker 1: logs and more about AIS battling other ais at machine speed?
Speaker 1: And if so, what does that future look like for privacy,
Speaker 1: for trusting these systems, for accountability when things go wrong
Speaker 1: at that speed and scale. Well, it's a lot to
Speaker 1: chew on.