By Mu Zhang, Heng Yin
This SpringerBrief explains the rising cyber threats that undermine Android program safety. It additional explores the chance to leverage the state-of-the-art semantics and context–aware strategies to guard opposed to such threats, together with zero-day Android malware, deep software program vulnerabilities, privateness breach and inadequate defense warnings in app descriptions. The authors start through introducing the heritage of the sector, explaining the final working approach, programming positive aspects, and protection mechanisms. The authors seize the semantic-level habit of cellular purposes and use it to reliably realize malware editions and zero-day malware. subsequent, they suggest an automated patch new release strategy to become aware of and block risky info movement. A bytecode rewriting process is used to restrict privateness leakage. User-awareness, a key issue of safeguard dangers, is addressed by way of immediately translating security-related software semantics into average language descriptions. widespread habit mining is used to find and compress universal semantics. for that reason, the produced descriptions are security-sensitive, human-understandable and concise.By masking the heritage, present threats, and destiny paintings during this box, the short is acceptable for either pros in and advanced-level scholars operating in cellular safety and functions. it truly is necessary for researchers, as well.
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Additional resources for Android Application Security: A Semantics and Context-Aware Approach
Therefore, we can focus on unique behaviors (rather than unique apps). Moreover, with more and more apps being fed into the 3 Semantics-Aware Android Malware Classification Number of Unique Graphs 38 12000 10000 8000 6000 4000 2000 0 3000 4000 5000 6000 7000 8000 9000 10000 11000 Number of Benign Apps Fig. 12 Convergence of unique graphs in benign apps benign database, the database size grows slower and slower. 12 depicts our discovery. When the number of apps increases from 3000 to 4000, there is a sharp increase (2087) of unique graphs.
The bank) nor any familiar parties from the contacts. It is a constant URL belonging to an unknown third-party. 4 Graph Generation We have implemented a graph generation tool on top of Soot  in 20 k lines of code. This tool examines an Android app to conduct entry point discovery and perform context-sensitive, flow-sensitive, and interprocedural dataflow analyses. These analyses locate API call parameters and return values of interest, extract constant parameters, and determine the data dependencies among the API calls.
Next, we used the remaining 2100 benign apps as test samples to evaluate the false positive rate of our anomaly detector. 15 % of clean apps are mistakenly recognized as suspicious ones during anomaly detection. This means, if our anomaly detector is applied to Google Play, among the approximately 1200 new apps per day , around 60 apps will be mislabeled as containing anomalies and be bounced back to the developers. We believe that this is an acceptable ratio for vetting purpose. 5 Evaluation 39 further eliminate these false positives during this interactive process.
Android Application Security: A Semantics and Context-Aware Approach by Mu Zhang, Heng Yin