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With the meteoric rise in popularity of the Android platform, there is an urgent need to combat the accompanying proliferation of malware. Existing work addresses the area of consumer malware detection, but cannot detect novel, sophisticated, domain-specific malware that is targeted specially at one aspect of an organization (eg. ground operations of the US Military). Adversaries can exploit domain knowledge to camoauge malice within the legitimate behaviors of an app and behind a domain-specific trigger, rendering traditional approaches such as signature-matching, machine learning, and dynamic monitoring ineective. Manual code inspections are also inadequate, scaling poorly and introducing human error. Yet, there is a dire need to detect this kind of malware before it causes catastrophic loss of life and property.
This dissertation presents the Security Toolbox, our novel solution for this challenging new problem posed by DARPA’s Automated Program Analysis for Cyber security (APAC) program. We employ a human-in-the-loop approach to amplify the natural intelligence of our analysts. Our automation detects interesting program behaviors and exposes them in an analysis Dashboard, allowing the analyst to brainstorm aw hypotheses and ask new questions, which in turn can be answered by our automated analysis primitives. The Security Toolbox is built on top of Atlas, a novel program analysis platform made by EnSoft. Atlas uses a graph- based mathematical abstraction of software to produce a unied property multigraph, exposes a powerful API for writing analyzers using graph traversals, and provides both automated and interactive capabilities to facilitate program comprehension. The Security Toolbox is also powered by FlowMiner, a novel solution to mine ne-grained, compact data ow summaries of Java libraries. FlowMiner allows the Security Toolbox to complete a scalable and accurate partial program analysis of an application without including all of the libraries that it uses (eg. Android).
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