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Attackers increasingly take advantage of naive users who tend to treat non-executable files casually, as if they are benign. Such users often open non-executable files although they can conceal and perform malicious operations. Existing defensive solutions currently used by organizations prevent executable files from entering organizational networks via web browsers or email messages. Therefore, recent advanced persistent threat attacks tend to leverage non-executable files such as portable document format (PDF) documents which are used daily by organizations. Machine Learning (ML) methods have recently been applied to detect malicious PDF files, however these techniques lack an essential element-they cannot be efficiently updated daily. In this study we present an active learning (AL) based framework, specifically designed to efficiently assist anti-virus vendors focus their analytical efforts aimed at acquiring novel malicious content. This focus is accomplished by identifying and acquiring both new PDF files that are most likely malicious and informative benign PDF documents. These files are used for retraining and enhancing the knowledge stores of both the detection model and anti-virus. We propose two AL based methods: exploitation and combination. Our methods are evaluated and compared to existing AL method (SVM-margin) and to random sampling for 10 days, and results indicate that on the last day of the experiment, combination outperformed all of the other methods, enriching the signature repository of the anti-virus with almost seven times more new malicious PDF files, while each day improving the detection model's capabilities further. At the same time, it dramatically reduces security experts' efforts by 75 %. Despite this significant reduction, results also indicate that our framework better detects new malicious PDF files than leading anti-virus tools commonly used by organizations for protection against malicious PDF files.

Recently, a new type of mobile malware applications with self-updating capabilities was found on the official Google Android marketplace. Malware applications of this type cannot be detected using the standard signatures approach or by applying regular static or dynamic analysis methods. In this paper we first describe and analyze this new type of mobile malware and then present a new network-based behavioral analysis for identifying such malware applications. For each application, a model representing its specific traffic pattern is learned locally on the device. Machine-learning methods are used for learning the normal patterns and detection of deviations from the normal application's behavior. These methods were implemented and evaluated on Android devices.