Edward Raff, William Fleming, Richard Zak, Hyrum Anderson, Charles Nicholas, Mark McLean

In this work, we present a method to find the top-k most frequent n-grams that is 60× faster for small n, and can tackle large n ≥ 1024. Despite the unprecedented size of n considered, we show how these features still have predictive ability for malware classification tasks. More important, large ngrams provide benefits in producing features that are interpretable by malware analysis, and can be used to create general purpose signatures compatible with industry standard tools like Yara.

Christopher S. Culling

As there are a large number of advanced capabilities contained within YARA, this paper will focus on easy-to-use, advanced features, including YARA's Portable Execution (PE) module, to highlight some of the more powerful aspects of YARA. While it takes more time and effort to learn and utilize advanced YARA rules, in the long run, this method is a worthwhile investment towards a safer networking environment.

Muhammad Ikram 1,2, Narseo Vallina-Rodriguez 3, Suranga Seneviratne 1, Mohamed Ali Kaafar 1, Vern Paxson 3,4 - 1. Data61, CSIRO 2. UNSW 3. ICSI 4. UC Berkeley

In this paper we provide a first comprehensive analysis of 283 Android apps that use the Android VPN permission, which we extracted from a corpus of more than 1.4 million apps on the Google Play store. We perform a number of passive and active measurements designed to investigate a wide range of security and privacy features and to study the behavior of each VPN-based app. Our analysis includes investigation of possible malware presence, third-party library embedding, and traffic manipulation, as well as gauging user perception of the security and privacy of such apps. Our experiments reveal several instances of VPN apps that expose users to serious privacy and security vulnerabilities, such as use of insecure VPN tunneling protocols, as well as IPv6 and DNS traffic leakage. We also report on a number of apps actively performing TLS interception. Of particular concern are instances of apps that inject JavaScript programs for tracking, advertising, and for redirecting e-commerce traffic to external partners.

Mahmood Sharif Carnegie Mellon University, Jumpei Urakawa KDDI Research, Inc., Nicolas Christin Carnegie Mellon University, Ayumu Kubota KDDI Research, Inc.,  Akira Yamada KDDI Research, Inc. 

Many computer-security defenses are reactive—they operate only when security incidents take place, or immediately thereafter. Recent efforts have attempted to predict security incidents before they occur, to enable defenders to proactively protect their devices and networks. These efforts have primarily focused on long-term predictions. We propose a system that enables proactive defenses at the level of a single browsing session. By observing user behavior, it can predict whether they will be exposed to malicious content on the web seconds before the moment of exposure, thus opening a window of opportunity for proactive defenses.