Join us for State of Cybercrime, where experts discuss the latest trends and developments in the world of cybercrime and provide insights into how organizations can protect themselves from potential threats.
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Join us for State of Cybercrime, where experts discuss the latest trends and developments in the world of cybercrime and provide insights into how organizations can protect themselves from potential threats.
Sponsored by Varonis
Check out Maltego: https://www.maltego.com/
Maltego Twitter: https://twitter.com/maltegohq
Check out Maltego use cases: https://docs.maltego.com/support/solutions/articles/15000012022-use-cases
This podcast is brought to you by Varonis, if you'd like to learn more check out the Cyber Attack Lab at https://www.varonis.com/cyber-workshop/
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Check out Objective-See: https://objective-see.com/
Objective-See Twitter: https://twitter.com/objective_see
Objective-See Patreon: https://www.patreon.com/objective_see
While In Russia: Patrick's RSA talk on hacking journalists -
Patrick's Twitter: https://twitter.com/patrickwardle
This podcast is brought to you by Varonis, if you'd like to learn more check out the Cyber Attack Lab at https://www.varonis.com/cyber-workshop/
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Stefan's Site with links to all of his projects: https://spacehuhn.io/
Twitter: https://twitter.com/spacehuhn
YouTube: https://www.youtube.com/channel/UCFmjA6dnjv-phqrFACyI8tw
An overview of the ESP8266 https://www.espressif.com/en/products/hardware/esp8266ex/overview
Stefan's Github https://github.com/spacehuhn
ESP8266 Deauther 2.0 https://github.com/spacehuhn/esp8266_deauther
WiFi Duck - Wireless injection attack Platform
https://github.com/spacehuhn/WiFiDuck
WiFi Satellite - monitoring and logging 2.4GHz WiFi Traffic
This podcast is brought to you by Varonis, if you'd like to learn more check out the Cyber Attack Lab at https://www.varonis.com/cyber-workshop/
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A honeypot is a tool that acts as bait, luring an attacker into revealing themselves by presenting a seemingly juicy target. In our first Security Tools podcast, we explore a free tool called Grabify that can gather information about scammers or attackers when they click on a honeypot tracking link.
https://twitter.com/grabifydotlink
This podcast is brought to you by Varonis, if you'd like to learn more check out the Cyber Attack Lab at https://www.varonis.com/cyber-workshop/
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We wanted you to be the first to know that next week; we will be back in this same feed with a new security podcast from Varonis.
The new Security Tools podcast will keep you up to date with the most exciting and useful tools the Infosec community has to offer.
Join us on the new show to hear from the researchers and hackers behind tools like Grabify, a link-based Honeypot service that unmasks scammers leveraging the same web tracking tactics used by most modern websites. We’ll find out why it’s so hard to stay anonymous online and show you how to use the power of tracking links to find the real location of an online scammer.
See you next week.
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Summer is approaching, and of course, that’s when we feel the most heat. However, for cybersecurity managers, they feel the heat all the time. They must be right every time because cybercriminals only have to be right once. So summer can potentially feel like it’s year-round for cybersecurity pros and it can cause job burnout.
Another problem that managers face is the potential ineffectualness of cybersecurity awareness training. Learning and sharing interesting security information in a class is really wonderful and expansive for a user’s mind. However, if it doesn’t change a user’s behavior and he continues to click on links he shouldn't be clicking on, training might not be as helpful as it claims to be.
Other articles discussed:
Panelists: Cindy Ng, Mike Buckbee, Kris Keyser, Kilian Englert
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Searching a traveler’s phone or laptop is not an extension of a search made on a piece of luggage. As former commissioner of Ontario Ann Cavoukian said, “Your smartphone and other digital devices contain the most intimate details of your life: financial and health records.”
In general, it’s also dangerous to connect laws made in accordance with the physical world to the digital space. But even with GDPR that’s aimed to protect consumer data, the law hasn’t taken action against any major technology firms such as Google or Facebook.
It seems our relationship with technology might get worse before it gets better.
Other articles discussed:
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Lately, we’ve been hearing more from security experts who are urging IT pros to stop scapegoating users as the primary reason for not achieving security nirvana. After covering this controversy on a recent episode of the Inside Out Security Show, I thought it was worth having an in-depth conversation with an expert.
So, I contacted Angela Sasse, Professor of Human-Centred Technology in the Department of Computer Science at University College London, UK. Over the past 15 years, she has been researching the human-centered aspects of security, privacy, identity and trust. In 2015, for her innovative work, she was awarded the Fellowship of the Royal Academy of Engineering(FREng) for being one of the best and brightest engineer and technologist in the UK.
In part one of my interview with Professor Angela Sasse, we cover the challenges that CISOs have in managing risk while finding a way to understand what’s being asked of the user. And more importantly, why improving the usability of security can positively impact an organization’s profits.
For her exceptional work in 2015, Professor Angela Sasse was awarded the Fellowship of the Royal Academy of Engineering as being one of the best and brightest engineers and technologists in the UK.
I think what you're doing is multilayered, multifaceted, and you're targeting two very different fields where you're trying to think about how to design innovative technologies that are functional while driving the bottom line. So that's B2B and then also improve the well-being of individuals and society and that's B2C and the strategies of those two things are very different. So maybe to just peel the layers back to start from the beginning, your research focuses on human usability of security and perhaps privacy too. Maybe it might be helpful to define what usability encompasses.
Angela Sasse: Okay. So, usability, there's a traditional definition, there's an, you know, International Standards Organization definition of it, and it says,"Usability is if a specified user group can use the mechanism to achieve their goals in a specified context of use." And that actually makes it really quite, quite complex, because what it's really saying is there isn't a sort of, like, hard-line measure of what's usable and what isn't. It's about the fit, how well it fits the person that's using it and the purpose they're using it for in the situation that they're using it.
Cindy Ng: Usability is more about the user, the human and not necessarily the technology, it's, after all, just a tool. And we have to figure out a way to fit usability into the technology we're using.
Angela Sasse: Yes, of course, and what it amounts to is that, of course, it's not economic. It wouldn't be economically possible to get a perfect fit for a 120 different types of interactions in situations that you do. What we generally do is we use four or five different forms of interaction, you know, that work well enough across the whole range of interactions that we do. So their locally optimal and globally optimal, so you could make a super good fit for different situations. But if you don't want to know about 120 different ways of doing something, so globally optimal is to have a limited set of interactions and symbols and things that you're dealing with when you're working with technology.
So, security, however, one of the things that a lot of people overlook when it comes to security and usability is that from the user's point of view, security is always what usability people call a secondary task or enabling task. So this is a task I have to do to get to the thing I really want to do, and so the kind of tolerance or acceptance that people have for delays or difficulty is even less than with their sort of primary interactions.
Cindy Ng: It's like a chore. For instance, an example would be I need to download an app, perhaps, in order to register for something.
Angela Sasse: Yeah, and so what you want to do is, you know, you want to use the app for a particular purpose, and then if you basically have...if the user perceives that in order to be able to use the app, you know, all the stuff you have to do to get to that point is too much of a hurdle, then most of them would just turn around and say, "It's not worth it. I'm not going ahead."
Cindy Ng: When it comes to the security aspect how does a CISO or an IT security admin decide that users are dangerous, and that if they only had the same knowledge that I have, that they would behave differently. Where does downloading the app or using a website intersect with the jobs of what a CISO does?
Angela Sasse: CISO is trying to manage the risks, and some of the risks might affect the individual employee or individual customer as well. But other risks are really risks to the organization, and if something went wrong it wouldn't directly affect the employee or the customer. But I think what, a CISO or SysAdmin, I would say to them is, "You've got to understand what you are asking the user to do. You have to accept that you're a security specialist, and you are focused on delivering security, but you're the only person in the organization for whom security is a primary task.
For everybody else, it's a secondary task. It's a hurdle they have to jump over in order to do what they've been trained for, what they are good at, what they're paid to do. And so it's in your best interest to make that hurdle as small as possible. You should effectively manage the risk, but you've got to find ways of doing it that no one really bothers, where you're really taking as little time and effort away from the people who have to do it. Because otherwise you end up eating all the profits. Right?"
Angela Sasse: The more effort you're basically taking away from the main activity that people do, the more you're reducing the profits of the organization.
Cindy Ng: You've done the research, and you're presenting them and you're interacting with CISOs and SysAdmins and how has the mindset evolved and also some of the push back. Can you provide some examples?
Angela Sasse: Early on a lot of of the push back was really, well, people should do what they are told, and the other main push back is, "So, you're telling me, this is difficult or effortful to do for people. Can we give them some training?" The real push back is that they don't want to think about changing, making changes to the technology and to the way they are managing the risks. So their first thought is always, "How can I make people do what I want them to do." And so the very first big study that Adams and I did, we then subsequently...it's published in the paper, "Users Are Not the Enemy."
So, this was a very big telecommunication company and when we said to them, "Look, your staff have between 16 and 64 different passwords, six digit pins and eight character passwords, complex, and you're telling them they have to have a different one and they can't write it down. And they were also expiring them every 30 days, so they had to change them every 30 days.
And basically I said, "Nobody can do this." Then they said, "Okay, could they do it if we gave them some extra training?" And my response was, "Yes, and that would look like this, all your employees have to go on a one-year course to become memory athletes. Even when they come back, they're going to spend half an hour a day doing the memory techniques that you need to do in order to be able to recall all this stuff."
And if you think about it that way, it's just absurd that rather than making changes to the password policy or providing easier to use authentication mechanism. Sometimes what's equally ridiculous is, so, like, "Can you give me a psychology test so I can screen out the people who are not compliant so that I can recruit people that are naturally compliant."
That's bizarre. You need to recruit people who are good at the jobs that your business relies on, good at the stuff your business delivers. If you just recruit compliant and risk averse people, you're gonna go bust. So, you sometimes you have to really show the absurdity of the natural thinking that there is. There is this initial resistance to go, like, "I don't really want to change the way how I think about security, and I don't want to change the mechanisms I use."
Cindy Ng: I think a lot of the CISOs and the SysAdmins are restricted too by the tools and the software, and they feel like they're confined and have to work within a framework, because their job is really technical. It's always about are you able to secure my network first over the human aspect of it. And I really like what you said about how phishing scam attackers understand more of the human element of security than security designers have. Can you elaborate more on that?
Angela Sasse: I think... So, I'm working with some of the government here in the UK, with those government agencies that are responsible for security and for advising companies about security. And I think it's very interesting to see that they have concluded that CISOs need, and security practitioners, that they need to develop their soft skills and that they need to engage. They need to listen more, and they need to also learn how to...once they have listened, you know, and understand how they can provide a fit, then how they can persuade people of the need for change.
You know, because part of the whole problem is if you reconfigure the mechanisms, and they're now easier to use without people still need to change their behavior. They still need to move on from existing habit to the new ones, and that can be a bit of a blocker for change, and you need to persuade people to embark on this journey of changing their existing habits. And for that you need soft skills, and you need to persuade them that I have now made it as easy as possible to use. Now your part, your responsibility is to change your existing habit towards this new secure one, you know, which is feasible to do. And it's not particularly onerous, but you need to work through that process of changing, learning a new habit.
Cindy Ng: How long do they want it to be? How long does it actually take, and how has their mindset evolved?
Angela Sasse: Most of them now realize that their role is really is to be a cheerleader for security, not, you know, the kind of the old school that they are some sort of gatekeeper who can stop everybody. So most of them now do realize.
Cindy Ng: When did that happen?
Angela Sasse: I think it's happened...it's only very recent. For the majority of them it happened in the last, maybe, four or five years. Some still haven't gotten there, but quite a few of them, and, you know, I've seen some very...I mean, if I go to Infosec for instance to meet people there who've really done a very good job.
And I think, actually, say if you, for instance, look at the born digital companies. I think they generally do...they do very well. You know, if you look at Google, Amazon, Facebook, eBay, they've generally worked very hard to secure their business without...and they know that it would be a threat to their business if people couldn't use the security or found the security to be cumbersome. And I think they've actually done a good job, pretty good job, to look at how you can make it easier to use. So I think those companies are currently leading the charge.
But I've seen this happen in a couple of other... So, I think basically, other companies that have very big customer bases, you know, sort of experiences that they get with that that they realize that they have to make it easier for the customers to access services or use devices. Those lessons then also tend to filter through to how they are designing security for their own employees.
So, you know, if you look at mobile phone companies and the television companies, you know, cable and satellite TV companies, I think they've really internalized...so the people working there really have quite a modern outlook. I think next coming around the corner is the big software and technology development companies. They have started to...so companies like Microsoft have started to realize this as well.
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Over the past few weeks, Kaiser Fung has given us some valuable pointers on understanding the big data stats we are assaulted with on a daily basis. To sum up, learn the context behind the stats — sources and biases — and know that the algorithms that crunch numbers may not have the answer to your problems. In this third segment of our podcast, Kaiser points out all the ways the stats can trick us through its inherently random nature — variability in stats-speak. Transcript Cindy Ng: In part one and two with our interview with Kaiser Fung, we discussed the process behind a numerical finding, then focused on accuracy. In our last installment, Kaiser reveals one last way to cultivate numbersense. Your third point is to have a nose for doctored statistics. And for me, it's kind of like…if you don't know what you don't know? Kind of like I was surprised to read in the school rankings chapter in Number Sense that different publications have different rules in ranking. And then I didn't know that like reporting low GPAs as not available, it's a magic trick that causes a median GPA to rise. And so if I didn't know this, I would just use any number in any of these publications and use it in my marketing. How do I cultivate a nose for doctored statistics? Kaiser Fung: Well, I think...well, for a lot of people, I think it would involve like reading certain authors, certain people who specializes in this sort of stuff. I'm one of them but there are also others out there who have this sort of skepticism and they will point out how...you know, I mean I think it's all about figuring out how other people do it and then you can do it even to just follow the same types of logic. Often times, it involves sort of like, there are multiple stages to this. So there's the stage of can you smell something fishy? So it's sort of this awareness that, "Okay, do I want to believe this or not?" And then there's the next stage of, do you...once you smell something, do you know where to look, how to look, how do you investigate it? So usually when you smell something that means that you have developed an alternative hypothesis or interpretation that is different from what the thing you're reading. So in sort of this scientific method, what we want to do at that point is to try to go out and find cooperating evidence. So then the question becomes do you have this notion of what kinds of things I could find that could help you decide whether you're right or whether the original person is right? And here the distinction is really around if you're more experienced, you might be able to know if I am able to find this information that will be sufficient for me to even validate this or to fortify that. So you don't necessarily go through the entire analysis. Maybe you just find a shortcut to get to a certain point. And then the last stage is, that's the hardest to achieve and also not always necessary but it's sort of like okay if you no longer believe in what was published, how do you develop your alternative argument? So that requires a little more work and that's the kind of thing that I try to train my students to do. So often times when I set very open-ended type problems for them, you can see these people in different stages. Like there are people who don't recognize where the problems are, you know, just believe what they see. There are people who recognize the problems and able to diagnose what's wrong. Then there are ones that can diagnose what's wrong and they will have...you know, whether it's usually through looking at some other data or some other data points, they can decide, okay, instead of making the assumptions that the original people made which you no longer believe, I'm going to make a different set of assumptions. So like make this other set of assumptions, what would be the logical outcome of the analysis? So I think it's something that can be trained. It's just difficult in the classroom setting in our traditional sort of textbook lecture style. That type of stuff is very difficult to train. Andy Green: Something you said about sort of being able to train ourselves. And one thing that, it comes up in your books a lot, is that a lot of us don't have the sense of variability in the data. We don't understand what that means or what it...if we were to sort of put it out on a bar chart, we don't have that picture in our mind. And one example that you talk about I think on a blog post in something as marketers, we do a lot is A/B testing. And so we'll look at, we'll do a comparison of changing one website slightly and then testing it and then noticing that maybe it does better, we think. And then when we roll it out, we find out it really doesn't make too much of a difference. So you talked about reasons why something might not scale up in an A/B test. I think you wrote about that for one of the blogs. I think it was Harvard Business Review, Kaiser Fung: ...I'm not sure about whether we're saying the same things. I'm not quite exactly remembering what I wrote about there. But from an A/B testing perspective, I think there are lots of little things that people need to pay attention to because ultimately what you're trying to do is to come up with a result that is generalizable, right? So you can run your test in a period of time but in reality, you would like this effect to hold, I mean that you'll find anything over the next period of time. Now, I think both in this case as well as what I just talked about before, one of the core concepts in statistics is not just understanding it's variability. Whatever number is put in front of you, it's just a, at the moment sort of measurement, right? It's sort of like if you measure your weight on the same scale it's going to fluctuate, morning, night, you know different days. But you don't have this notion that your weight has changed. But the actual measurement of the weight, even though if it's still the same weight, will be slightly different. So that's the variability but the next phase is understanding that there are sources of variability. So there are many different reasons why things are variable. And I think that's sort of what we're getting into. So in the case of A/B testing, there are many different reasons why your results have been generalized. One very obvious example is that what we call the, we say that there's a drift in population. Meaning that especially websites, you know, a site changes over time. So even if you keep stable during the test, when you roll it forward it may have changed. And just a small change in the same part of the website could actually have a very large change in the type of people that comes to the page. So I have done...in the past, I've done a lot of A/B testing around kind of what you call the conversion funnel in marketing. And this is particularly an issue if you...let's say you're testing on a page that is close to the end of the funnel. Now, people do that because that's the most impactful place because the conversion rates are much higher in those pages. But the problem is because it's at the end of many steps. Anything that changed in any of the prior steps, it's going to potentially change the types of people ended up on your conversion page. So that's one reason why there are tests that test variability in the type of people coming to your page. Then even if the result worked during a test, it's not going to work later. But there's plenty of other things including something that people often times fail to recognize which is the whole basis of A/B testing is you are randomly placing people into more pockets. And the randomization, it's supposed to on average tell you that they are comparable and the same. But random while it will get you there almost all of the time but you can throw a coin 10 times and get 10 heads. But there's a possibility that there is something odd about that case. So another problem is what is your particular test had this weird phenomenon? Now, in statistics, we account for that by putting error box around these things. But it still doesn't solve the problem that that particular sample was a very odd sample. And so one of the underlying assumptions of all the analysis in statistics is that you're not analyzing that rare sample. That rare sample is kind of treated as part of the outside of normal situation. So yeah, there are a lot of subtlety in how you would actually interpret these things. And so A/B testing is still one of the best ways of measuring something. But even there, there are lots of things that you can't tell. I mean, I also wrote about the fact that sometimes it doesn't tell you...we'd like to say A/B testing gives you cause-effect analysis. It all depends on what you mean by cause-effect because even the most...for a typical example, like the red button and the green button, it's not caused by the color. It's like the color change did not cause anything. So there are some more intricate mechanisms there that if you really want to talk about cause, you wouldn't say color is a cause. Although in a particular way of interpreting this, you can say that the color is the cause. Andy Green: Right, right. Cindy Ng : It really just sounds like at every point you have to ask yourself, is this accurate? Is this the truth? It's a lot more work to get to the truth of the matter. Kaiser Fung: Yes. So I think when people sell you the notion that somehow because of the volume of the data everything becomes easy, I think it's the opposite. I think that's one of the key points of the book. When you have more data, it actually requires a lot more work. And going back to the earlier point which is that when you have more data, the amount of potentially wrong analysis or coming to the wrong conclusion is exponentially larger. And a lot of it is because of the fact that most analysis, especially with data that is not experimental, it's not randomized, not controlled, you essentially you rely on a lot of assumptions. And when you rely a lot on assumptions, it's the proverbial thing about you can basically say whatever the hell you want with this data. And so that's why I think it's really important for people when especially for those people who are not actually in this business of generating analysis, if you're in the business of consuming analysis, you really have to look out for yourself because you really could, in this day and age, could say whatever you want with the data that you have. Cindy Ng: So be a skeptic, be paranoid. Kaiser Fung: Well the nice thing is like when they're only talking about the colors of your bicycles and so on, you can probably just ignore and not do the work because it's not really that important to the problem. But on the other hand, when you...you know, in the other case that is ongoing which is the whole Tesla autopilot algorithm thing, right? Like in those cases and also when people are now getting into healthcare and all these other things where your potential...there's a life and death decision, then you really should pay more attention. Cindy Ng: This is great. Do you have any kind of final thoughts in terms of Numbersense? Kaiser Fung: Well, I'm about...I mean, this is a preview of a blog post that I'm going to put out probably this week. And I don't know if this works for you guys because this could be a bit more involved but so here's the situation. I mean, it's again that basically reinforces the point that you can easily get fooled by the data. So my TA and I were reviewing a data set that one of our students is using for their class projects. And this was basically some data about the revenue contributions of various customers and some characteristics of the customers. So we were basically trying to solve the problem of is there a way to use these characteristics to explain why the revenue contributions for different customers have gone up or down? So we've spent a bit of time thinking about it and we eventually come up with a nice way of doing it. You know, it's not an obvious problem, so we have a nice way of doing it. We thought that actually produced pretty nice results. So then we met with the student and pretty much the first thing that we learned from this conversation is that, oh, because this is for proprietary data, all the revenue members were completely made up. Like there is some, this thing, formula or whatever that she used to generate the number. So that's sort of the interesting sort of dynamic there. Because on the one hand, like obviously all of the work that we spent kind of put in creating this model and then the reason why we like the model is that it creates a nicely interpretable results. Like it actually makes sense, right? But it turns that yes, it makes sense in that imaginary world but it really doesn't have any impact on reality, right? So I think that's the...and then the other side of this which I kind of touch upon in my book too is well, if you were to just look at the methodology of what we did and the model that we built, you would say we did a really good work. Because we applied a good methodology, generate it, quick results. So the method and the data and then your assumptions, I mean all these things play a role in this ecosystem. And I think that...so going back to what I was saying today, I mean it's the problem is all these data. I think we have not spent sufficient time to really think about what are the sources of the data, how believable is this data? And in this day and age, especially with marketing data, with online data and all that, like there's a lot of manipulation going on. There are lots of people who are creating this data for a purpose. Think about online reviews and all other things. So on the analysis side, we have really not faced up to this issue. We just basically take the data and we just analyze and we come up with models and we say things. But how much of any of those things would be refuted if we actually knew how the data was created? Cindy Ng: That's a really good takeaway. You are working on many things, it sounds like. You're working on a blog, you teach. What else are you working on these days? Kaiser Fung: Well, I'm mainly working on various educational activities that are hoping to train the next generation of analysts and people who look at data that will hopefully have...the Numbersense that I want to talk about. I have various book projects in mind which I hope to get to when I have more time. And from the Numbersense perspective, I'm interested in exploring ways to describe this in a more concrete way, right? So there this notion of...I mean, this is a general ecosystem of things that I've talked about. But I want a system that ties it a bit. And so I have an effort ongoing to try to make it more quantifiable. Cindy Ng: And so if people want to follow what you're doing, what is your Twitter handle on your website? Kaiser Fung: Yes, so my Twitter is @junkcharts. And that's probably where most of my, like in terms of updates that's where things go. I have a personal website called just kaiserfung.com where they can learn more about what I do. And then I try to update my speaking schedule there because I do travel around the country, speak at various events. And then they will also read about other things that I do like for corporations that are mostly around, again, training managers, training people in this area of statistical reasoning, data visualization, number sense and all that. Want to join us live?…
Full show notes at the publisherIt’s great to be Amazon to only have one on-call security engineer and have security automated. However, for many organizations today, having security completely automated is still an aspirational goal. Those in healthcare might would love to upgrade, but what if you’re using a system that’s FDA approved, which makes upgrading a little more difficult. What if hackers were able to download personal data from a web server because many weren’t up-to-date and had outdated plugins. Meanwhile, here’s a lesson from veteran report, Brian Krebs on how not to acknowledge a data breach.
By the way, would you ever use public wifi and do you value certificates over experience?
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