Journal
KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING
Volume -, Issue -, Pages 1507-1515Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3097983.3098026
Keywords
Android Malware Detection; Application Programming Interface Calls; Relation Analysis; Heterogeneous Information Network
Categories
Funding
- U.S. National Science Foundation [CNS-1618629]
- WVU Senate Grants for Research and Scholarship [R-16-043]
- China 973 Fundamental RD Program [2014CB340304]
- Direct For Computer & Info Scie & Enginr [1618629] Funding Source: National Science Foundation
- Division Of Computer and Network Systems [1618629] Funding Source: National Science Foundation
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With explosive growth of Android malware and due to the severity of its damages to smart phone users, the detection of Android malware has become increasingly important in cybersecurity. The increasing sophistication of Android malware calls for new defensive techniques that are capable against novel threats and harder to evade. In this paper, to detect Android malware, instead of using Application Programming Interface (API) calls only, we further analyze the different relationships between them and create higher-level semantics which require more efforts for attackers to evade the detection. We represent the Android applications (apps), related APIs, and their rich relationships as a structured heterogeneous information network (HIN). Then we use a meta path based approach to characterize the semantic relatedness of apps and APIs. We use each meta-path to formulate a similarity measure over Android apps, and aggregate different similarities using multi-kernel learning. Then each meta-path is automatically weighted by the learning algorithm to make predictions. To the best of our knowledge, this is the first work to use structured HIN for Android malware detection. Comprehensive experiments on real sample collections from Comodo Cloud Security Center are conducted to compare various malware detection approaches. Promising experimental results demonstrate that our developed system HinDroid outperforms other alternative Android malware detection techniques.
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