By Mu Zhang, Heng Yin
This SpringerBrief explains the rising cyber threats that undermine Android software protection. It additional explores the chance to leverage the state of the art semantics and context–aware thoughts to shield opposed to such threats, together with zero-day Android malware, deep software program vulnerabilities, privateness breach and inadequate safety warnings in app descriptions. The authors commence by means of introducing the history of the sector, explaining the final working process, programming beneficial properties, and safety mechanisms. The authors catch the semantic-level habit of cellular functions and use it to reliably become aware of malware variations and zero-day malware. subsequent, they suggest an automated patch new release strategy to realize and block harmful info movement. A bytecode rewriting strategy is used to restrict privateness leakage. User-awareness, a key issue of safety dangers, is addressed through immediately translating security-related application semantics into average language descriptions. widespread habit mining is used to find and compress universal semantics. consequently, the produced descriptions are security-sensitive, human-understandable and concise.By protecting the heritage, present threats, and destiny paintings during this box, the short is appropriate for either pros in and advanced-level scholars operating in cellular protection and functions. it's worthy for researchers, as well.
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Extra info for Android Application Security: A Semantics and Context-Aware Approach
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To understand these semantic-level differences, we perform backward dataflow analysis on selected parameters and collect all possible constant values on the backward trace. We generate a constant set for each critical API argument and mark the parameter as “Constant” in the corresponding node on the WC-ADG. While a more complete string constant analysis is also possible, the computation of regular expressions is fairly expensive for static analysis. The substring set currently generated effectively reflects the semantics of a critical API call and is sufficient for further feature extraction.
1 Signature Detection We use a multi-label classification to identify the malware family of the unrecognized malicious samples. Therefore, we expect to only include those malware behavior graphs, that are well labeled with family information, into the database. 5 Evaluation 37 Project and use them to construct the malware graph database. Consequently, we built such a database of 862 unique behavior graphs, with each graph labeled with a specific malware family. We then selected 1050 malware samples from the Android Malware Genome Project and used them as a training set.
Android Application Security: A Semantics and Context-Aware Approach by Mu Zhang, Heng Yin