Student research abstract

Although much research effort has focused on Android malware detection, very little attention has been given to implementationlevel vulnerabilities. This paper focuses on Content Provider Leakage vulnerability that can be exploited by viewing or editing sensitive data through malware. We present a new technique for detecting content provider leakage vulnerability. We propose Kullback-Leibler Divergence (KLD) as a measure to detect the content provider leakage vulnerability. In particular, our contribution includes the development of a set of elements and mapping the elements to programming principles for secure implementation of content provider classes. These elements are captured from the implementation to form the initial population set. The population set is used to measure the divergence of a newly implemented application with content provider to identify potential vulnerabilities. We also apply a back-off smoothing technique to compute the KLD value. We implement a java prototype tool to evaluate a set of content provider implementations to show the effectiveness of the proposed approach. The initial results show that by choosing an appropriate threshold level, KLD is an effective method for detecting content provider leakage vulnerability.

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Student research abstract

Cited by 2 publications

References 2 publications

An Efficient Approach to Detect TorrentLocker Ransomware in Computer Systems

An Efficient Approach to Detect TorrentLocker Ransomware in Computer Systems

Content Provider Leakage Vulnerability Detection in Android Applications

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