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Researchers have demonstrated how a malicious piece of software in an air-gapped computer can communicate with a nearby drone using a blinking LED on the computer.
I have mixed feelings about research like this. On the one hand, it’s pretty cool. On the other hand, there’s not really anything new or novel, and it’s kind of a movie-plot threat.
Research paper.
The evolutionary reasons why the strawberry squid has two different eyes. Additional articles.
As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.
Interesting article in Science discussing field research on how people are radicalized to become terrorists.
The potential for research that can overcome existing constraints can be seen in recent advances in understanding violent extremism and, partly, in interdiction and prevention. Most notable is waning interest in simplistic root-cause explanations of why individuals become violent extremists (e.g., poverty, lack of education, marginalization, foreign occupation, and religious fervor), which cannot accommodate the richness and diversity of situations that breed terrorism or support meaningful interventions. A more tractable line of inquiry is how people actually become involved in terror networks (e.g., how they radicalize and are recruited, move to action, or come to abandon cause and comrades).
Reports from the The Soufan Group, International Center for the Study of Radicalisation (King’s College London), and the Combating Terrorism Center (U.S. Military Academy) indicate that approximately three-fourths of those who join the Islamic State or al-Qaeda do so in groups. These groups often involve preexisting social networks and typically cluster in particular towns and neighborhoods.. This suggests that much recruitment does not need direct personal appeals by organization agents or individual exposure to social media (which would entail a more dispersed recruitment pattern). Fieldwork is needed to identify the specific conditions under which these processes play out. Natural growth models of terrorist networks then might be based on an epidemiology of radical ideas in host social networks rather than built in the abstract then fitted to data and would allow for a public health, rather than strictly criminal, approach to violent extremism.
Such considerations have implications for countering terrorist recruitment. The present USG focus is on “counternarratives,” intended as alternative to the “ideologies” held to motivate terrorists. This strategy treats ideas as disembodied from the human conditions in which they are embedded and given life as animators of social groups. In their stead, research and policy might better focus on personalized “counterengagement,” addressing and harnessing the fellowship, passion, and purpose of people within specific social contexts, as ISIS and al-Qaeda often do. This focus stands in sharp contrast to reliance on negative mass messaging and sting operations to dissuade young people in doubt through entrapment and punishment (the most common practice used in U.S. law enforcement) rather than through positive persuasion and channeling into productive life paths. At the very least, we need field research in communities that is capable of capturing evidence to reveal which strategies are working, failing, or backfiring.
There’s a really interesting paper from George Washington University on hacking back: “Into the Gray Zone: The Private Sector and Active Defense against Cyber Threats.”
I’ve never been a fan of hacking back. There’s a reason we no longer issue letters of marque or allow private entities to commit crimes, and hacking back is a form a vigilante justice. But the paper makes a lot of good points.
Interesting research. (Popular article here.)
As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.
Interesting research: “De-anonymizing Web Browsing Data with Social Networks“:
Abstract: Can online trackers and network adversaries de-anonymize web browsing data readily available to them? We show—theoretically, via simulation, and through experiments on real user data—that de-identified web browsing histories can be linked to social media profiles using only publicly available data. Our approach is based on a simple observation: each person has a distinctive social network, and thus the set of links appearing in one’s feed is unique. Assuming users visit links in their feed with higher probability than a random user, browsing histories contain tell-tale marks of identity. We formalize this intuition by specifying a model of web browsing behavior and then deriving the maximum likelihood estimate of a user’s social profile. We evaluate this strategy on simulated browsing histories, and show that given a history with 30 links originating from Twitter, we can deduce the corresponding Twitter profile more than 50% of the time. To gauge the real-world effectiveness of this approach, we recruited nearly 400 people to donate their web browsing histories, and we were able to correctly identify more than 70% of them. We further show that several online trackers are embedded on sufficiently many websites to carry out this attack with high accuracy. Our theoretical contribution applies to any type of transactional data and is robust to noisy observations, generalizing a wide range of previous de-anonymization attacks. Finally, since our attack attempts to find the correct Twitter profile out of over 300 million candidates, it is—to our knowledge—the largest scale demonstrated de-anonymization to date.
Interesting research: Sebastian Hellmeier, “The Dictator’s Digital Toolkit: Explaining Variation in Internet Filtering in Authoritarian Regimes,” Politics & Policy, 2016 (full paper is behind a paywall):
Abstract: Following its global diffusion during the last decade, the Internet was expected to become a liberation technology and a threat for autocratic regimes by facilitating collective action. Recently, however, autocratic regimes took control of the Internet and filter online content. Building on the literature concerning the political economy of repression, this article argues that regime characteristics, economic conditions, and conflict in bordering states account for variation in Internet filtering levels among autocratic regimes. Using OLS-regression, the article analyzes the determinants of Internet filtering as measured by the Open Net Initiative in 34 autocratic regimes. The results show that monarchies, regimes with higher levels of social unrest, regime changes in neighboring countries, and less oppositional competition in the political arena are more likely to filter the Internet. The article calls for a systematic data collection to analyze the causal mechanisms and the temporal dynamics of Internet filtering.
New paper: “A Simple Power Analysis Attack on the Twofish Key Schedule.” This shouldn’t be a surprise; these attacks are devastating if you don’t take steps to mitigate them.
The general issue is if an attacker has physical control of the computer performing the encryption, it is very hard to secure the encryption inside the computer. I wrote a paper about this back in 1999.
I am co-author on a paper discussing whether elections be classified as “critical infrastructure” in the US, based on experiences in other countries:
Abstract: With the Russian government hack of the Democratic National Convention email servers, and further leaks expected over the coming months that could influence an election, the drama of the 2016 U.S. presidential race highlights an important point: Nefarious hackers do not just pose a risk to vulnerable companies, cyber attacks can potentially impact the trajectory of democracies. Yet, to date, a consensus has not been reached as to the desirability and feasibility of reclassifying elections, in particular voting machines, as critical infrastructure due in part to the long history of local and state control of voting procedures. This Article takes on the debate in the U.S. using the 2016 elections as a case study but puts the issue in a global context with in-depth case studies from South Africa, Estonia, Brazil, Germany, and India. Governance best practices are analyzed by reviewing these differing approaches to securing elections, including the extent to which trend lines are converging or diverging. This investigation will, in turn, help inform ongoing minilateral efforts at cybersecurity norm building in the critical infrastructure context, which are considered here for the first time in the literature through the lens of polycentric governance.
The paper was speculative, but now it’s official. The U.S. election has been classified as critical infrastructure. I am tentatively in favor of this, but what really matter is what happens now. What does this mean? What sorts of increased security will election systems get? Will we finally get rid of computerized touch-screen voting?
EDITED TO ADD (1/16): This is a good article.
Signal, the encrypted messaging app I prefer, is being blocked in both Egypt and the UAE. Recently, the Signal team developed a workaround: domain fronting.
Signal’s new anti-censorship feature uses a trick called “domain fronting,” Marlinspike explains. A country like Egypt, with only a few small internet service providers tightly controlled by the government, can block any direct request to a service on its blacklist. But clever services can circumvent that censorship by hiding their traffic inside of encrypted connections to a major internet service, like the content delivery networks (CDNs) that host content closer to users to speed up their online experience—or in Signal’s case, Google’s App Engine platform, designed to host apps on Google’s servers.
“Now when people in Egypt or the United Arab Emirates send a Signal message, it’ll look identical to something like a Google search,” Marlinspike says. “The idea is that using Signal will look like using Google; if you want to block Signal you’ll have to block Google.”
The trick works because Google’s App Engine allows developers to redirect traffic from Google.com to their own domain. Google’s use of TLS encryption means that contents of the traffic, including that redirect request, are hidden, and the internet service provider can see only that someone has connected to Google.com. That essentially turns Google into a proxy for Signal, bouncing its traffic and fooling the censors.
This isn’t a new trick (Tor uses it too, for example), but it does work.
Sidebar photo of Bruce Schneier by Joe MacInnis.