Category: "Security"

Pages: 1 2

  2020-12-13

Machine Learning Security: a new crop of technologies

  By Hagai Bar-El   , 2587 words
Categories: Security Analysis, Security Engineering, Security, Management

Artificial Intelligence (AI), and Machine Learning (ML) specifically, are now at the stage in which we start caring about their security implications. Why now? Because that’s the point at which we usually start caring about the security considerations of new technologies we’ve started using. Looking at previous cases, such as of desktop computing, the Internet, car networks, and IoT (Internet of Things), those technologies first gained fast momentum by the urge to capitalize on their novel use-cases. They were deployed as fast as they could possibly be, by stakeholders rushing to secure their share of the emerging revenue pie. Once the systems started operating en masse, it was finally time to realize that where there is value – there is also malice, and every technology that processes an asset (valuable data that can be traded, the ability to display content to a user and grab her attention, potential for extortion money, etc.) will inevitably lure threat actors who demonstrate impressive creativity when attempting to divert or exploit those assets.

This flow of events is barely surprising, and we were not really shocked to learn that the Internet does not provide much security out of the box, that cars could be hacked remotely through their wireless interfaces, or that cheap home automation gear doesn’t bother to encrypt its traffic. This is economy, and unless there is an immediate public safety issue causing the regulator to intervene (often later than it should), we act upon security considerations only once the new technology is deployed, and the security risks are manifested in a way that they can no longer be ignored.

It happened with desktop computing in the 80’s, with the Internet in the 90’s, with car networks about a decade ago, and with mass IoT about half a decade ago. (In those approximate dates I am not referring to when the first security advocate indicated that there are threats, this usually happened right away if not before, but to when enough security awareness was built for the industry to commit resources towards mitigating some of those threats.) Finally, it’s now the turn of Machine Learning.

When we decide that a new technology “needs security” we look at the threats and see how we can address them. At this point, we usually divide into two camps:

  • Some players, such as those heavily invested in securing the new technology, and consultants keen on capitalizing on the new class of fear that the industry just brought on itself, assert that “this is something different”; everything we knew about security has to be re-learned, and all tools and methodologies that we’ve built no longer suffice. In short, the sky is falling and we’re for the rescue.
  • Older security folks will point at the similarities, concluding that it’s the same security, just with different assets, requirements, and constraints that need to be accounted for. IoT Security is the same security just with resource constrained devices, physical assets, long device lifetime, and harsh network conditions; car security is the same security with a different type of network, different latency requirements, and devastating kinetic effects in case of failure, and so forth.

I usually associate with the second camp. Each new area of security introduces a lot of engineering work, but the basic paradigms remain intact. It’s all about securing computer systems, just with different properties. Those different properties make tremendous differences, and call for different specializations, but the principles of security governance, and even the nature of the high-level objectives, are largely reusable.

With Machine Learning the situation is different. This is a new flavor of security that calls for a new crop of technologies and startups that deploy a different mindset towards solving a new set of security challenges; including challenges that are not at focus in other domains. The remainder of this post will delve into why ML Security is different (unlike the previous examples), and what our next steps could look like when investing in mitigation technologies.

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  2020-11-15

Addressing the shortcoming of machine-learning for security

  By Hagai Bar-El   , 2765 words
Categories: Security Analysis, IT Security, Security Engineering, Security, Cyber Security

In a previous post I wrote about cases in which machine-learning adds little to the reliability of security tools, because it often does not react well to novel threats. In this post I will share a thought about overcoming the limitation of machine-learning, by properly augmenting it with other methods. The challenge we tackle is not that of finding additional methods of detection, as we assume such are already known and deployed in other systems. The challenge we tackle is of how to combine traditional detection methods with those based on machine-learning, in a way that yields the best overall results. As promising as machine-learning (and artificial intelligence) is, it is less effective when deployed in silo (not in combination with existing technologies), and hence the significance of properly marrying the two.

I propose to augment the data used in machine-learning with tags that come from other, i.e., traditional, classification algorithms. More importantly, I suggest distinguishing between the machine-learning-based assessment component and the decision component, and using the tagging in both components, independently.

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  2020-09-26

Your Bitcoin wallet will never be your bank account

  By Hagai Bar-El   , 1399 words
Categories: Security Analysis, Security Policies, Security, Counter-media

Don’t get me wrong; Bitcoin and crypto currencies are a big deal, at least technology-wise. Bitcoin and blockchains taught us a lot on what can be done with security protocols, and at a lower level, it even taught us that computation inefficiency is not always a bad word, but something that can yield benefits, if that inefficiency is properly orchestrated and exploited. It was also the most prevalent demonstration of scarcity being artificially created by technology alone. As I wrote before, blockchains will probably have some novel use-cases one day, and Bitcoin, aside of being a mechanism for transferring money, also provides a target of speculation, which in itself can be (and is) monetized.

What I truly do not understand are the advocates who see Bitcoin wallets as the near-future replacement for bank accounts, and Bitcoin replacing banks (and other financial institutions) in the near future. I understand the motivation, as those are dreams easy to fall for, but for crypto-currency wallets to replace financial institutions much more is needed, and for the sake of this discussion I will not even delve into the many technical difficulties.

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  2020-09-13

An obvious limitation of machine-learning for security

  By Hagai Bar-El   , 726 words
Categories: IT Security, Security Engineering, Security, Counter-media

I recently came across this study titled “Unknown Threats are The Achilles Heel of Email Security”. It concludes that traditional e-mail scanning tools, that also utilize machine-learning to cope with emerging threats, are still not reacting fast enough to new threats. This is probably true, but I think this conclusion should be considered even more widely, beyond e-mail.

Threats are dynamic. Threat actors are creative and well-motivated enough to make threat mitigation an endlessly moving target. So aren’t we fortunate to have this new term, “machine learning”, recently join our tech jargon? Just like many other buzzwords, the term is newer than what it denotes, but nonetheless, a machine that learns the job autonomously seems to be precisely what we need for mitigating ever-changing threats.

All in all, machine-learning is good for security, but yet in some cases it is a less significant addition to our defense arsenal. Why? – Because while you learn, you often don’t do the job well enough; and a machine is no different. Eventually, the merits of learning-while-doing are to be determined by the price of the resulting temporary imperfectness.

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  2020-08-01

The effect of cloud services on our intimacy with IT

  By Hagai Bar-El   , 1775 words
Categories: IT Security, Security, Day-to-Day Security Advice

Years ago, we did not trust cloud service providers, or we trusted them only when we had no choice. Then, consumers started using web-mail and other such services, and finally companies also moved into replacing their own IT with cloud applications. By now, we trust our service providers sufficiently, for the most part. We model our risks, we consider the benefits, and we usually decide that it’s worth it. But often enough, our trust in service providers still does not cause us the necessary warm and fuzzy feeling that is required for us to hand off all our data to the cloud and live a truly digital life. As it seems, thinking you are secure is one thing, and feeling you are sufficiently secure, even with your most critical data, is something else.

What do we do for now? – Use the cloud, but not for everything…

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  2017-10-13

For and against security checklists, frameworks, and guidelines

  By Hagai Bar-El   , 636 words
Categories: Security Engineering, Security, Cyber Security, Counter-media

We have seen many of those by now. Starting with old ones like FIPS 140, and concluding with more recent additions as the NIST CSF (Cyber Security Framework). The question is: are whose worth my time? What are they good for? Do we need to adhere to them? In a nutshell, I think they have their value, and need to be consulted, but not worshiped.

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  2014-09-05

Capturing PINs using an IR camera

  By Hagai Bar-El   , 97 words
Categories: Security

This video demonstrates how an IR camera, of the type that can be bought for a reasonable price and attached to a smart-phone, can be used to capture a PIN that was previously entered on a PIN pad, by analyzing a thermal image of the pad after the fact. When the human finger presses a non-metallic button, it leaves a thermal residue that can be detected on a thermal image, even if taken many seconds later.

The video refers to the article: Heat of the Moment: Characterizing the Efficacy of Thermal Camera-Based Attacks, written in UC San-Diego.

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