OnionBots: Subverting Privacy Infrastructure for Cyber Attacks

Over the last decade botnets survived by adopting a sequence of increasingly sophisticated strategies to evade detection and take overs, and to monetize their infrastructure. At the same time, the success of privacy infrastructures such as Tor opened the door to illegal activities, including botnets, ransomware, and a marketplace for drugs and contraband. We contend that the next waves of botnets will extensively subvert privacy infrastructure and cryptographic mechanisms. In this work we propose to preemptively investigate the design and mitigation of such botnets. We first, introduce OnionBots, what we believe will be the next generation of resilient, stealthy botnets. OnionBots use privacy infrastructures for cyber attacks by completely decoupling their operation from the infected host IP address and by carrying traffic that does not leak information about its source, destination, and nature. Such bots live symbiotically within the privacy infrastructures to evade detection, measurement, scale estimation, observation, and in general all IP-based current mitigation techniques. Furthermore, we show that with an adequate self-healing network maintenance scheme, that is simple to implement, OnionBots achieve a low diameter and a low degree and are robust to partitioning under node deletions. We developed a mitigation technique, called SOAP, that neutralizes the nodes of the basic OnionBots. We also outline and discuss a set of techniques that can enable subsequent waves of Super OnionBots. In light of the potential of such botnets, we believe that the research community should proactively develop detection and mitigation methods to thwart OnionBots, potentially making adjustments to privacy infrastructure.Comment: 12 pages, 8 figure

How Unique is Your .onion? An Analysis of the Fingerprintability of Tor Onion Services

Recent studies have shown that Tor onion (hidden) service websites are particularly vulnerable to website fingerprinting attacks due to their limited number and sensitive nature. In this work we present a multi-level feature analysis of onion site fingerprintability, considering three state-of-the-art website fingerprinting methods and 482 Tor onion services, making this the largest analysis of this kind completed on onion services to date. Prior studies typically report average performance results for a given website fingerprinting method or countermeasure. We investigate which sites are more or less vulnerable to fingerprinting and which features make them so. We find that there is a high variability in the rate at which sites are classified (and misclassified) by these attacks, implying that average performance figures may not be informative of the risks that website fingerprinting attacks pose to particular sites. We analyze the features exploited by the different website fingerprinting methods and discuss what makes onion service sites more or less easily identifiable, both in terms of their traffic traces as well as their webpage design. We study misclassifications to understand how onion service sites can be redesigned to be less vulnerable to website fingerprinting attacks. Our results also inform the design of website fingerprinting countermeasures and their evaluation considering disparate impact across sites.Comment: Accepted by ACM CCS 201

Virtual machines for virtual worlds

Multi User Virtual Worlds provide a simulated immersive 3D environment that is similar to the real world. Popular examples include Second Life and OpenSim. The multi-user nature of these simulations means that there are significant computational demands on the processes that render the different avatar-centric views of the world for each participant, which change with every movement or interaction each participant makes. Maintaining quality of experience can be difficult when the density of avatars within the same area suddenly grows beyond a relatively small number. As such virtual worlds have a dynamic resource-on-demand need that could conceivably be met by Cloud technologies. In this paper we make a start to assessing the feasibility of using the Cloud for virtual worlds by measuring the performance of virtual worlds in virtual machines of the type used for Clouds. A suitable benchmark is researched and formulated and the construction of a test-bed for carrying out load experiments is described. The system is then used to evaluate the performance of virtual worlds running in virtual machines. The results are presented and analysed before presenting the design of a system that we have built for managing virtual worlds in the cloud.Postprin

Single-stroke Language-Agnostic Keylogging using Stereo-Microphones and Domain Specific Machine Learning

Mobile phones are equipped with an increasingly large number of precise and sophisticated sensors. This raises the risk of direct and indirect privacy breaches. In this paper, we investigate the feasibility of keystroke inference when user taps on a soft keyboard are captured by the stereoscopic microphones on an Android smartphone. We developed algorithms for sensor-signals processing and domain specific machine learning to infer key taps using a combination of stereo-microphones and gyroscopes. We implemented and evaluated the performance of our system on two popular mobile phones and a tablet: Samsung S2, Samsung Tab 8 and HTC One. Based on our experiments, and to the best of our knowledge, our system (1) is the first to exceed 90 % accuracy requiring a single attempt, (2) operates on the standard Android QWERTY and number keyboards, and (3) is language agnostic. We show that stereo-microphones are a much more effective side channel as compared to the gyroscope, however, their data can be combined to boost the accuracy of prediction. While previous studies focused on larger key sizes and repetitive attempts, we show that by focusing on the specifics of the keyboard and creating machine learning models and algorithms based on keyboard areas combined with adequate filtering, we can achieve an accuracy of 90%- 94 % for much smaller key sizes in a single attempt. We also demonstrate how such attacks can be instrumentalized by a malicious application to log the keystrokes of other sensitive applications. Finally, we describe some techniques to mitigate these attacks