By Mu Zhang, Heng Yin
This SpringerBrief explains the rising cyber threats that undermine Android software safety. It extra explores the chance to leverage the state-of-the-art semantics and context–aware ideas to protect opposed to such threats, together with zero-day Android malware, deep software program vulnerabilities, privateness breach and inadequate protection warnings in app descriptions. The authors commence by means of introducing the history of the sphere, explaining the overall working procedure, programming good points, and defense mechanisms. The authors seize the semantic-level habit of cellular functions and use it to reliably observe malware versions and zero-day malware. subsequent, they suggest an automated patch iteration strategy to discover and block risky details movement. A bytecode rewriting strategy is used to restrict privateness leakage. User-awareness, a key issue of safety hazards, is addressed by means of immediately translating security-related application semantics into average language descriptions. common habit mining is used to find and compress universal semantics. therefore, 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 appropriate for either execs in and advanced-level scholars operating in cellular protection and functions. it truly is worthwhile for researchers, as well.
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Additional info for Android Application Security: A Semantics and Context-Aware Approach
In particular, component hijacking may fall into the latter category. When receiving a manipulated input from a malicious Android app, an app with a component hijacking vulnerability may exfiltrate sensitive information or tamper with the sensitive data in a critical data repository on behalf of the malicious app. In other words, a dangerous information flow may happen in either an outbound or inbound direction depending on certain external conditions and/or the internal program state. A prior effort has been made to perform static analysis to discover potential component hijacking vulnerabilities .
Moreover, even VirusTotal cannot provide reliable malware family information for a given sample because the antivirus products used by VirusTotal seldom reach a consensus. This fact tells us two things: (1) It is a nontrivial task to collect evident samples as the ground truth in the context of multi-label classification; (2) multi-label malware detection or classification is, in general, a challenging real-world problem. Despite the difficulty, we obtained 193 samples, each of which is detected as the same malware by major AVs.
And, in turn, all the existing graphs are projected to a feature vector. In other words, there exists a one-to-one correspondence between the elements in a feature vector and the existing graphs in the database. To construct the feature vector of the given app, we produce its WC-ADGs and then query the graph database for all the generated graphs. For each query, a best matching graph is found. The similarity score is then put into the feature vector at the position corresponding to this best matching graph.
Android Application Security: A Semantics and Context-Aware Approach by Mu Zhang, Heng Yin