Speaker 1: Hey, everyone, welcome to another deep dive. This time we're
Speaker 1: tackling intelligent Mobile malware detection. Okay, you sent over some
Speaker 1: really interesting excerpts from the book Intelligent Mobile Malware Detection. Yes,
Speaker 1: and we're ready to unpack those, correct. But before we
Speaker 1: jump in, yeah, we should probably mention that we'll be
Speaker 1: focusing specifically on Android malware, right, and that makes sense, right,
Speaker 1: I mean, Android is like the dominant mobile platform globally. Shoot,
Speaker 1: so this is a topic that affects a ton of people.
Speaker 2: Totally.
Speaker 1: We're gonna look at how malware has made that jump
Speaker 1: from PCs to our smartphones. Yeah, we're even going to
Speaker 1: try to get inside the heads of these malware developers
Speaker 1: and figure out the tricks they use to bypass security.
Speaker 2: That's a good one.
Speaker 1: I'm pretty excited to dig into this me too. One
Speaker 1: thing that really stood out to me when I was
Speaker 1: reading the book excerpts you sent over was how the
Speaker 1: author emphasizes the unique challenges posed by Android's open source nature.
Speaker 2: That's a huge factor.
Speaker 1: It's that openness that makes Android so versatile. Absolutely, yeah,
Speaker 1: but it also creates these opportunities for uh, well, for exploitation. Yeah, exactly,
Speaker 1: So how do we even start wrapping our heads around
Speaker 1: this complex world of mobile malware.
Speaker 2: Well, the book starts with a pretty good overview of
Speaker 2: Android's architecture. Okay, it's kind of like a layered cake.
Speaker 1: Right, I like cake analogies.
Speaker 2: Each layer has a specific purpose, gotcha. And we're going
Speaker 2: to be zeroing in on the application layer because that's
Speaker 2: where all those apps you download and use actually live.
Speaker 1: So that's where all the action happens pretty much. Yeah, Now,
Speaker 1: remind me how are those acts created?
Speaker 2: So it all begins with the source code, which is
Speaker 2: like the recipe for the app. Okay, developers use that
Speaker 2: code to build the app's functionality, and then it gets
Speaker 2: packaged up into a nice, neat little file. Okay, call
Speaker 2: it dot appk file, right, that's what you download from
Speaker 2: the app store.
Speaker 1: Oh, the dot app k file. That's what brings us
Speaker 1: all those games, productivity tools, all this social media stuff.
Speaker 2: It does it all.
Speaker 1: It's like a little bundle of digital joy, you know.
Speaker 1: But those files, I mean they can also be used
Speaker 1: to carry malware.
Speaker 2: Right, it's the donside. Yeah.
Speaker 1: Even though Google play Store has things like what is
Speaker 1: it the bouncer system, it's not like one hundred percent
Speaker 1: fool proof. These malware developers, they're finding new ways to
Speaker 1: slip through the cracks all the time. Oh yeah, it
Speaker 1: really makes you think twice about downloading apps from those
Speaker 1: third party app stores, for sure. You know, one thing
Speaker 1: that really stuck with me from the reading was how
Speaker 1: much those malware infection methods have changed since the PC
Speaker 1: virus days. Oh yeah, remember those spam emails with the
Speaker 1: attachments like you've won a million dollars click here? Yeah, totally,
Speaker 1: but those seem almost quaint compared to what's going on
Speaker 1: now right. Definitely, malware has gotten way more sophisticated on Android,
Speaker 1: oh for sure, And it seems like it's taking advantage
Speaker 1: of those unique features of mobile devices. So it's not
Speaker 1: just about downloading a bad app anymore.
Speaker 2: No, No, it's way more than that.
Speaker 1: So like, what else is there?
Speaker 2: Okay, So the book talks about these drive.
Speaker 1: By download drive by download.
Speaker 2: Yeah, basically, you visit a malicious website and bam, okay,
Speaker 2: malware gets downloaded without you even knowing it.
Speaker 1: Wow, that's scary.
Speaker 2: It is pretty sneaky.
Speaker 1: Like, are there any examples of that?
Speaker 2: Um, you know, off the top of my head, I
Speaker 2: can't think of a specific one, no worries, but it's
Speaker 2: definitely a common tactic.
Speaker 1: And then there's malvertising, right where those ads online are
Speaker 1: actually infected.
Speaker 2: Yeah, exactly.
Speaker 1: It's like, can you trust anything you see online anymore?
Speaker 2: It's tough.
Speaker 1: I mean even those app updates. The book talked.
Speaker 2: About that too, Yeah, the update attacks.
Speaker 1: An app might start out totally fine and then.
Speaker 2: Boom, it turns malicious.
Speaker 1: Yeah, after an update, talk about a betrayal. It's rough,
Speaker 1: you know, it makes you think twice about just hitting
Speaker 1: accept on those update permissions.
Speaker 2: Definitely, you gotta read those carefully.
Speaker 1: Yeah, even for apps you've been using for a while, exactly.
Speaker 1: And then remember it talked about repacking attacks.
Speaker 2: Well, yeah, those are sneaky where.
Speaker 1: They take a good app and in it with some
Speaker 1: bad code.
Speaker 2: It's like a wolf in sheep's clothing.
Speaker 1: It's like someone taking your favorite recipe and adding a secret,
Speaker 1: harmful ingredient.
Speaker 2: Right, you'd never know until it's too late.
Speaker 1: And it seems like malware has gotten sneakier in other
Speaker 1: ways too. It's not just these broad attacks anymore. They're
Speaker 1: going after specific organizations.
Speaker 2: Now, right, targeted attacks, Yeah.
Speaker 1: Like businesses or even government agencies.
Speaker 2: Exactly. They're getting more strategic.
Speaker 1: And they're using all these advanced techniques to stay hidden.
Speaker 2: They're getting smarter, for sure.
Speaker 1: The book mentioned polymorphic malware. Oh yeah, I thought that
Speaker 1: was fascinating.
Speaker 2: To change their appearance, you know, like chameleons.
Speaker 1: Exactly. They're constantly changing to avoid detection.
Speaker 2: It makes it much harder to catch them, like.
Speaker 1: Trying to hit a moving target exactly. And then there
Speaker 1: was that plug x malware. Oh yeah, with those plug
Speaker 1: in interface.
Speaker 2: Yeah, like add ons, right, but for bad stuff.
Speaker 1: Yeah, like for a web browser. But they're constantly updating.
Speaker 2: It's like they're always one step ahead.
Speaker 1: It's like they have this whole tool box of ways
Speaker 1: to make their malware even more powerful.
Speaker 2: Exactly. They're very adaptable.
Speaker 1: It's not just Android either, right.
Speaker 2: Nope, not at all.
Speaker 1: The book even mentioned how Windows malware has gotten pretty
Speaker 1: sophisticated too.
Speaker 2: Definitely.
Speaker 1: They talked about fileless malware.
Speaker 2: That's a tricky one, yeah.
Speaker 1: Which uses legitimate programs on your computer to do bad things.
Speaker 2: It's like they're hiding in plain sight.
Speaker 1: So it's not enough to just scan for the bad
Speaker 1: files anymore, right.
Speaker 2: We need to look deeper at the behavior of programs.
Speaker 2: You have to be like a detective exactly, look for
Speaker 2: anything that seems out of place.
Speaker 1: And then, of course there's ransomware.
Speaker 2: Ugh, don't even get me started.
Speaker 1: It seems like everyone's talking about it these days.
Speaker 2: It's a huge problem.
Speaker 1: It's scary stuff.
Speaker 2: It's really impacting both individuals and organizations.
Speaker 1: And the book even mentioned how academic institutions were hit
Speaker 1: hard during the pandemic.
Speaker 2: Oh yeah, especially when everyone went online for.
Speaker 1: Classes, because they were probably more vulnerable.
Speaker 2: Exactly, they weren't prepared.
Speaker 1: It's almost like they wait for us to be at
Speaker 1: our weakest.
Speaker 2: It's pretty ruthless.
Speaker 1: What's more unsettling is that these ransomware kits are popping
Speaker 1: up on the dark web, right, the dark web? Yeah,
Speaker 1: like ransomware is a service.
Speaker 2: Yeah, it's like they're making it easier for anyone to
Speaker 2: launch attacks.
Speaker 1: So you don't even need to be a tech genius
Speaker 1: to do it anymore.
Speaker 2: That's the scary part.
Speaker 1: Okay, so we've got this whole army of malware threats
Speaker 1: out there.
Speaker 2: It's like a digital battlefield.
Speaker 1: But how do we actually detect these sneaky apps before
Speaker 1: they can do any damage?
Speaker 2: That's the million dollar question.
Speaker 1: Well, that's exactly what we're going to tackle next. Stay
Speaker 1: tuned as we continue our deep dive into intelligent mobile
Speaker 1: malware detection.
Speaker 2: All right, so now that we've met some of those
Speaker 2: bad guys in the world of Android malware, Yeah, let's
Speaker 2: look at how we can fight back.
Speaker 1: Let's do it.
Speaker 2: The book dives into something called static malware detection.
Speaker 1: Okay, static malware detection, what is that?
Speaker 2: It's basically analyzing the source code of an app without
Speaker 2: actually running it.
Speaker 1: Gotcha.
Speaker 2: It's like you're checking the blueprints of a building for weaknesses.
Speaker 2: Interesting before you even start construt So.
Speaker 1: You're looking for those red flags early on. Exactly proactive.
Speaker 1: I like it.
Speaker 2: But to do that, we need the source.
Speaker 1: Code, right, how do we get that?
Speaker 2: Well, there are tools like app tool and dex two jar.
Speaker 2: They let security researchers reverse engineer the app and get
Speaker 2: that source code.
Speaker 1: Sounds pretty high tech it is, So what exactly were
Speaker 1: they looking for in that code?
Speaker 2: They're examining various parts of the app, like detectives at
Speaker 2: a crime scene. I like that analogy. They might look
Speaker 2: at the java files, the core logic of the app,
Speaker 2: or the resource files, things like images, and text okay,
Speaker 2: But one of the most important things is the Android
Speaker 2: manifest dot XML file.
Speaker 1: Android Manifest dot xml. Yeah, that sounds familiar, But it's.
Speaker 2: Like the app sid card.
Speaker 1: Okay.
Speaker 2: It tells you the permissions the app wants, right, the
Speaker 2: intense it uses intense. Yeah, we'll get to those, and
Speaker 2: the different parts of the app itself.
Speaker 1: So it's basically a roadmap of what the app can
Speaker 1: potentially do. Exactly, and I bet you can find some
Speaker 1: clues about malicious intent in there. Absolutely, you mentioned intents.
Speaker 1: What are those?
Speaker 2: Intents? Are messages that allow apps to talk to each other, okay,
Speaker 2: and to the Android system itself. Gotcha, like messengers carrying instructions?
Speaker 1: Interesting.
Speaker 2: For example, an intent might be used to open a
Speaker 2: web page okay, or send an email, or even launch
Speaker 2: another app.
Speaker 1: So they're kind of like the glue that holds everything
Speaker 1: together in a way. Yeah, but I'm guessing malware can
Speaker 1: take advantage of these intents too, unfortunately.
Speaker 2: Yes, how so they can trigger malicious actions or try
Speaker 2: to get sensitive information.
Speaker 1: Oh no.
Speaker 2: For example, there's an intent called action power Connected.
Speaker 1: What does that do?
Speaker 2: It gets triggered when your device is plugged into charge. Okay,
Speaker 2: Some malware will use that to launch updates or steal
Speaker 2: data while you're not looking.
Speaker 1: Sneaky Rrey, that's like when burglars wait for you to
Speaker 1: go on vacation, right to break into your house. What
Speaker 1: other examples are there?
Speaker 2: There's the SMPS received intent.
Speaker 1: What's that one for?
Speaker 2: Triggered when you get a text message? Okay. Malware can
Speaker 2: use it to intercept your messages. Oh no, and steal
Speaker 2: things like bank codes.
Speaker 1: So it's like they're spying on your conversations essentially. Yes,
Speaker 1: And there's more, right, there's.
Speaker 2: User present What does that mean? Triggered when you unlock
Speaker 2: your device. Oh, malware might use it to launch bad
Speaker 2: stuff when you're actively using your phone.
Speaker 1: So you're less likely to notice exactly. It's like they're
Speaker 1: watching our every move.
Speaker 2: They're trying to be as sneaky as possible.
Speaker 1: This is all pretty scary stuff. It is so understanding
Speaker 1: these permissions and intense is really important, absolutely protecting ourselves.
Speaker 1: But static analysis that's not the only way to detect malware,
Speaker 1: right right, What else is there?
Speaker 2: There's dynamic analysis.
Speaker 1: Okay, tell me about that.
Speaker 2: It involves actually running the app ah okay, but in
Speaker 2: a controlled environment like a sandbox. A sandbox, yeah, so
Speaker 2: it can't actually harm your device, gotcha, And then you
Speaker 2: watch how it behaves.
Speaker 1: But it's like a controlled experiment to see what the
Speaker 1: malware does.
Speaker 2: That's the ideas, and this is.
Speaker 1: Good for catching stuff that hides its true nature.
Speaker 2: Yes, things like dynamic code loading.
Speaker 1: Dynamic code loading.
Speaker 2: It's where the bad code is hidden until the app
Speaker 2: is running.
Speaker 1: A tricky but I bet those malware developers have some
Speaker 1: tricks up their sleeves.
Speaker 2: Oh they always do, like what things like anti emulation.
Speaker 1: Methods anti emulation.
Speaker 2: Yeah, they try to detect if they're in a sandbox.
Speaker 2: Oh wow, and then they change your behavior.
Speaker 1: So they're like, oh, we're being watched, let's act nice.
Speaker 1: That's so sneaky.
Speaker 2: It is a constant cat and mouse game, so.
Speaker 1: What can we do to stay ahead.
Speaker 2: That's where hybrid analysis comes in.
Speaker 1: Hybrid analysis it.
Speaker 2: Combined static and dynamic analysis.
Speaker 1: So the best of both worlds. You get a much
Speaker 1: more complete picture. There's something else, right, system call monitoring,
Speaker 1: System call monitoring?
Speaker 2: What's that? System calls are low level requests and app
Speaker 2: makes to the operating system, things like accessing a file,
Speaker 2: setting data over the network, or allocating memory.
Speaker 1: So it's like the app is asking permission from the
Speaker 1: operating system exactly to do certain things.
Speaker 2: And by monitoring those calls, yeah, we can get a
Speaker 2: better idea of what the app is really doing.
Speaker 1: It's like eavesdropping on their conversation exactly to see if
Speaker 1: they're up to no good.
Speaker 2: And there are a few ways to analyze those calls,
Speaker 2: like what there's frequency analysis okay, looking at how often
Speaker 2: certain calls are made. Right, If an app makes a
Speaker 2: lot of calls related to sensitive data, that's a red flag.
Speaker 1: It's like someone making way too many trips to the
Speaker 1: bank vault exactly. What else?
Speaker 2: Sequence analysis, what's that? It looks at the order of
Speaker 2: the system calls. Okay, Certain sequences can point to bad behavior, even.
Speaker 1: If the individual calls seem normal.
Speaker 2: Right.
Speaker 1: It's like noticing someone always enters the same code on
Speaker 1: a keypad. Good analogy makes you think they're trying to
Speaker 1: break in.
Speaker 2: And then there's graph based analysis, Okay. Graphs we represent
Speaker 2: the system calls as a graph, okay, to see the
Speaker 2: relationships between them.
Speaker 1: So like dots and lines showing connections, and that helps
Speaker 1: you spot patterns, right.
Speaker 2: Patterns in a nonmas that you wouldn't see otherwise.
Speaker 1: It's like mapping out the app's network, yeah, to see
Speaker 1: if there are any suspicious links.
Speaker 2: And this brings us to some of the more advanced
Speaker 2: techniques in the book.
Speaker 1: Ooh, like what.
Speaker 2: Things like graph centrality measures.
Speaker 1: Graph centrality measures.
Speaker 2: Yeah, it sounds complicated, it does, but it's basically math
Speaker 2: that helps us find the most important calls.
Speaker 1: The most important system calls right within that graph. Okay,
Speaker 1: so you're ranking them based on how influential they are, exactly,
Speaker 1: like the key players and a network.
Speaker 2: And the book talks about a few different types of
Speaker 2: centrality measures, like what there's eigenvector centrality. Eigenvector centrality it
Speaker 2: measures influence based on connections to other influential calls.
Speaker 1: So if a system call has a high eigenvector centrality score,
Speaker 1: it means it's a big deal in that network exactly,
Speaker 1: and if it's doing something suspicious, that's a huge red flag.
Speaker 2: Big problem.
Speaker 1: What other types are there?
Speaker 2: There's between maldness central Okay, what's that one? It measures
Speaker 2: how often a call lies on the shortest path between
Speaker 2: two other.
Speaker 1: Calls, so it's like a busy interception.
Speaker 2: Yeah, connecting different parts of the network.
Speaker 1: If that call is malicious, it can cause.
Speaker 2: A lot of disruption, a lot of damage.
Speaker 1: And what's the last one?
Speaker 2: Closeness centrality.
Speaker 1: Closeness centrality, What's that?
Speaker 2: It measures how close a call is to all the
Speaker 2: other calls. Okay, like a central hub ah, gotcha, easy
Speaker 2: access to the whole network.
Speaker 1: So if it's malicious, it can spread quickly. And these
Speaker 1: centrality measures, yeah, they become even more powerful when you
Speaker 1: combine them with machine learning. That's right, Okay, machine learning,
Speaker 1: that's where things get really futuristic.
Speaker 2: It's definitely changing the game.
Speaker 1: But how does it work for malware detection?
Speaker 2: We feed a machine learning algorithm tons of data system
Speaker 2: called graphs from both malware and good apps, gotcha, and
Speaker 2: it learns to recognize patterns.
Speaker 1: It's like showing a detective thousands of crime scene.
Speaker 2: Photos, right, so they can spot the clues exactly. And
Speaker 2: the more data it sees, the better it gets it
Speaker 2: recognizing those patterns like.
Speaker 1: A digital detective, getting better with experience.
Speaker 2: And there are some cutting edge techniques using machine learning,
Speaker 2: like what graph convolutional networks GCNS for shorts and graph
Speaker 2: signal processing GSP GSP.
Speaker 1: Okay, those sounds super high tech.
Speaker 2: They are but they're also really effective.
Speaker 1: Break those down for me.
Speaker 2: Okay, so imagine a huge system called graph hundreds maybe
Speaker 2: thousands of nodes. A GCN can look at that whole graph,
Speaker 2: not just individual calls, but the relationships between them.
Speaker 1: So it's like a superpowered version of those centrality measures.
Speaker 2: You could say that it's.
Speaker 1: Finding those hidden connections and.
Speaker 2: That helps it spot even the sneakiest malware.
Speaker 1: What about GSP.
Speaker 2: GSP transforms the data. It's like turning that graph into
Speaker 2: a series of signals.
Speaker 1: Signals like what like an.
Speaker 2: Audio wave form, and then we can use signal processing
Speaker 2: techniques to analyze those signals in and find hidden patterns.
Speaker 1: It's like using a special lens to see things we.
Speaker 2: Couldn't before exactly.
Speaker 1: And you can combine GSP with machine learning too.
Speaker 2: Yeah, to create even more powerful systems.
Speaker 1: So we've got all these high tech ways to find malware.
Speaker 2: We do.
Speaker 1: But the book also mentioned something called system call pattern detection, oh,
Speaker 1: which sounds a bit simpler.
Speaker 2: It's based on the idea that malware often uses specific
Speaker 2: patterns of system calls, okay, when it's trying to do
Speaker 2: bad stuff.
Speaker 1: So it's like those telltale signs.
Speaker 2: Yeah, like a fingerpres give away a criminal exactly.
Speaker 1: But how do you compare those patterns?
Speaker 2: We use something called the Jerro Winkler.
Speaker 1: Similarity Narrowinkler similarity.
Speaker 2: It measures how similar two sequences of characters are.
Speaker 1: So you're comparing the patterns of an unknown app to
Speaker 1: a database of known malware patterns exactly, and if the
Speaker 1: score is high enough.
Speaker 2: Yeah, red flag, big red flag.
Speaker 1: It's amazing how all these different techniques are being used
Speaker 1: to fight malware.
Speaker 2: It's a fascinating field, it really is.
Speaker 1: It's like a constant battle between good and evil, and
Speaker 1: the stakes are higher than ever, especially with how much
Speaker 1: we rely on our smartphones these days. It's our connection
Speaker 1: to the world, and that's why this whole topic of
Speaker 1: intelligent mobile malware detection is so important.
Speaker 2: Absolutely, we need to be aware of the threats and
Speaker 2: the ways to protect ourselves, and that's.
Speaker 1: What we're trying to do here today. Knowledge is power, right,
Speaker 1: and the more we know, yeah, the better equipped we
Speaker 1: are to stay safe in this digital world.
Speaker 2: Couldn't have said it better myself.
Speaker 1: It's a wild world out there in the digital frontier,
Speaker 1: it really is. As we wrap up our deep dive here,
Speaker 1: any final thoughts for our.
Speaker 2: Listener, I think the biggest thing is just awareness.
Speaker 1: Awareness.
Speaker 2: The more you know about how this malware works and
Speaker 2: how it gets on your phone, the better you'll be
Speaker 2: able to protect yourself.
Speaker 1: So pay attention to those app permissions, absolutely, be careful
Speaker 1: about suspicious links or downloads, and keep your software updated.
Speaker 2: All the basics.
Speaker 1: Yeah, it's like that old saying knowledge is power.
Speaker 2: It really is.
Speaker 1: But it's not just about what we do as individuals, right,
Speaker 1: What about the role technology plays in all this?
Speaker 2: Oh? Technology is huge.
Speaker 1: Yeah.
Speaker 2: I mean we've talked about static and dynamic analysis, right,
Speaker 2: and those are constantly improving. M But what's really cool
Speaker 2: is how machine learning is being used. Machine learning, Yeah,
Speaker 2: it feels like graph convolutional networks GSS, signal process GSP.
Speaker 1: They're really changing the game.
Speaker 2: It's like we're building a digital immune system for our devices.
Speaker 1: That's a great way to put it.
Speaker 2: But even with all that, there are still some big challenges, right.
Speaker 2: Oh yeah, what are some of the things that keep
Speaker 2: you up at night?
Speaker 1: Well, the book talked about adversarial attacks, and that's pretty
Speaker 1: scary stuff.
Speaker 2: Adversarial attacks remind me of what those are.
Speaker 1: So it's when those malware developers Uh huh actually design their.
Speaker 2: Code specifically to fool the detection systems.
Speaker 1: Oh wow.
Speaker 2: Yeah, it's like a constant chess match.
Speaker 1: So they're always trying to outsmart us basically, and we
Speaker 1: have to keep developing new ways to stay ahead exactly.
Speaker 1: That's that's a great point. I hadn't really thought about
Speaker 1: it like that.
Speaker 2: It's a never ending battle.
Speaker 1: Another challenge I can imagine is just the sheer amount
Speaker 1: of data right now.
Speaker 2: It's overwhelming, millions of.
Speaker 1: New apps coming out.
Speaker 2: All the time, and they all need to be checked.
Speaker 1: That's where I bet automation comes.
Speaker 2: In absolutely, and machine learning. It's the only way to
Speaker 2: keep up.
Speaker 1: It's like having a whole army of digital detectives working
Speaker 1: around the.
Speaker 2: Clock trying to keep us safe.
Speaker 1: And as those threats keep changing, evolving, yeah, we're going
Speaker 1: to need even more creative solutions for sure.
Speaker 2: Well.
Speaker 1: I think we've given everyone a lot to think about today.
Speaker 2: We covered a lot of ground.
Speaker 1: We talked about Android's architecture, all those sneaky ways malware
Speaker 1: gets onto our phones.
Speaker 2: System call analysis, machine learning.
Speaker 1: It's been a wild ride it Hopefully our listener is
Speaker 1: walking away with a new appreciation for mobile security and.
Speaker 2: All the people who are working to keep us safe.
Speaker 1: That's what this deep dive is all about, sparking that curiosity,
Speaker 1: exploring these new.
Speaker 2: Ideas, empowering people with knowledge.
Speaker 1: Exactly so, for our listener out there who's eager to
Speaker 1: learn even more about Android malware and cybersecurity, where should
Speaker 1: they go?
Speaker 2: There are so many great resources online. Ye, the Sands
Speaker 2: Institute has a ton of information.
Speaker 1: Okay, Sands Institute.
Speaker 2: And organizations like NIST. They publish guidelines and best practices NIST.
Speaker 1: I'll have to check that out.
Speaker 2: And of course there's the book.
Speaker 1: Itself, Intelligent Mobile Malware.
Speaker 2: Detection, a great starting point for anyone who wants to
Speaker 2: dive deeper.
Speaker 1: Awesome, and don't forget just staying informed is huge. Definitely,
Speaker 1: keep up with the security news, pay attention to those permissions,
Speaker 1: be careful what you click on, and remember knowledge is power.
Speaker 2: Couldn't agree more Well.
Speaker 1: On that note, we'll wrap up this deep dive. Thanks
Speaker 1: for joining us.
Speaker 2: It's been a pleasure.
Speaker 1: Until next time, Stay curious, stay informed, and stay safe
Speaker 1: out there.