Diverse protection systems for improving security: a study with AntiVirus engines

Diverse “barriers” or “protection systems” are very common in many industries, especially in safety-critical ones where the designers must use “defense in depth” techniques to prevent safety failures. Similar techniques are also commonly prescribed for security systems: using multiple, diverse detection systems to prevent security breaches. However empirical evidence of the effectiveness of diversity is rare. We present results of an empirical study which uses a large-scale dataset to assess the benefits of diversity with an important category of security systems: AntiVirus products. The analysis was based on 1599 malware samples collected from a distributed honeypot deployment over a period of 178 days. The malware samples were sent to the signature engines of 32 different AntiVirus products hosted by the VirusTotal service. We also present an exploratory model which shows that the number of diverse protection layers that are needed to achieve “perfect” detection with our dataset follows an exponential power-law distribution. If this distribution is shown to be generic with other datasets, it would be a cost-effective means for predicting the probability of perfect detection for systems that use a large number of barriers based on measurements made with systems that are composed of fewer (say 2, 3) barriers

apk2vec: Semi-supervised multi-view representation learning for profiling Android applications

Building behavior profiles of Android applications (apps) with holistic, rich and multi-view information (e.g., incorporating several semantic views of an app such as API sequences, system calls, etc.) would help catering downstream analytics tasks such as app categorization, recommendation and malware analysis significantly better. Towards this goal, we design a semi-supervised Representation Learning (RL) framework named apk2vec to automatically generate a compact representation (aka profile/embedding) for a given app. More specifically, apk2vec has the three following unique characteristics which make it an excellent choice for largescale app profiling: (1) it encompasses information from multiple semantic views such as API sequences, permissions, etc., (2) being a semi-supervised embedding technique, it can make use of labels associated with apps (e.g., malware family or app category labels) to build high quality app profiles, and (3) it combines RL and feature hashing which allows it to efficiently build profiles of apps that stream over time (i.e., online learning). The resulting semi-supervised multi-view hash embeddings of apps could then be used for a wide variety of downstream tasks such as the ones mentioned above. Our extensive evaluations with more than 42,000 apps demonstrate that apk2vec's app profiles could significantly outperform state-of-the-art techniques in four app analytics tasks namely, malware detection, familial clustering, app clone detection and app recommendation.Comment: International Conference on Data Mining, 201

Sound and Precise Malware Analysis for Android via Pushdown Reachability and Entry-Point Saturation

We present Anadroid, a static malware analysis framework for Android apps. Anadroid exploits two techniques to soundly raise precision: (1) it uses a pushdown system to precisely model dynamically dispatched interprocedural and exception-driven control-flow; (2) it uses Entry-Point Saturation (EPS) to soundly approximate all possible interleavings of asynchronous entry points in Android applications. (It also integrates static taint-flow analysis and least permissions analysis to expand the class of malicious behaviors which it can catch.) Anadroid provides rich user interface support for human analysts which must ultimately rule on the "maliciousness" of a behavior. To demonstrate the effectiveness of Anadroid's malware analysis, we had teams of analysts analyze a challenge suite of 52 Android applications released as part of the Auto- mated Program Analysis for Cybersecurity (APAC) DARPA program. The first team analyzed the apps using a ver- sion of Anadroid that uses traditional (finite-state-machine-based) control-flow-analysis found in existing malware analysis tools; the second team analyzed the apps using a version of Anadroid that uses our enhanced pushdown-based control-flow-analysis. We measured machine analysis time, human analyst time, and their accuracy in flagging malicious applications. With pushdown analysis, we found statistically significant (p < 0.05) decreases in time: from 85 minutes per app to 35 minutes per app in human plus machine analysis time; and statistically significant (p < 0.05) increases in accuracy with the pushdown-driven analyzer: from 71% correct identification to 95% correct identification.Comment: Appears in 3rd Annual ACM CCS workshop on Security and Privacy in SmartPhones and Mobile Devices (SPSM'13), Berlin, Germany, 201

Applications of Machine Learning to Threat Intelligence, Intrusion Detection and Malware

Artificial Intelligence (AI) and Machine Learning (ML) are emerging technologies with applications to many fields. This paper is a survey of use cases of ML for threat intelligence, intrusion detection, and malware analysis and detection. Threat intelligence, especially attack attribution, can benefit from the use of ML classification. False positives from rule-based intrusion detection systems can be reduced with the use of ML models. Malware analysis and classification can be made easier by developing ML frameworks to distill similarities between the malicious programs. Adversarial machine learning will also be discussed, because while ML can be used to solve problems or reduce analyst workload, it also introduces new attack surfaces