LEADER 04038nam 22006015 450 001 9910151858103321 005 20200704131703.0 010 $a3-319-47812-5 024 7 $a10.1007/978-3-319-47812-8 035 $a(CKB)3710000000952897 035 $a(DE-He213)978-3-319-47812-8 035 $a(MiAaPQ)EBC4745440 035 $a(iGPub)SPNA0045391 035 $a(PPN)197138748 035 $a(EXLCZ)993710000000952897 100 $a20161117d2016 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAndroid Application Security $eA Semantics and Context-Aware Approach /$fby Mu Zhang, Heng Yin 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (XI, 105 p. 37 illus., 29 illus. in color.) 225 1 $aSpringerBriefs in Computer Science,$x2191-5768 311 $a3-319-47811-7 320 $aIncludes bibliographical references at the end of each chapters. 327 $aIntroduction -- Background -- Semantics-Aware Android Malware Classification -- Automatic Generation of Vulnerability-Specific Patches for Preventing Component Hijacking Attacks -- Efficient and Context-Aware Privacy Leakage Confinement -- Automatic Generation of Security-Centric Descriptions for Android Apps -- Limitation and Future Work -- Conclusion. 330 $aThis SpringerBrief explains the emerging cyber threats that undermine Android application security. It further explores the opportunity to leverage the cutting-edge semantics and context?aware techniques to defend against such threats, including zero-day Android malware, deep software vulnerabilities, privacy breach and insufficient security warnings in app descriptions. The authors begin by introducing the background of the field, explaining the general operating system, programming features, and security mechanisms. The authors capture the semantic-level behavior of mobile applications and use it to reliably detect malware variants and zero-day malware. Next, they propose an automatic patch generation technique to detect and block dangerous information flow. A bytecode rewriting technique is used to confine privacy leakage. User-awareness, a key factor of security risks, is addressed by automatically translating security-related program semantics into natural language descriptions. Frequent behavior mining is used to discover and compress common semantics. As a result, the produced descriptions are security-sensitive, human-understandable and concise. By covering the background, current threats, and future work in this field, the brief is suitable for both professionals in industry and advanced-level students working in mobile security and applications. It is valuable for researchers, as well. 410 0$aSpringerBriefs in Computer Science,$x2191-5768 606 $aComputer security 606 $aComputer networks 606 $aElectrical engineering 606 $aSystems and Data Security$3https://scigraph.springernature.com/ontologies/product-market-codes/I28060 606 $aComputer Communication Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/I13022 606 $aCommunications Engineering, Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/T24035 615 0$aComputer security. 615 0$aComputer networks. 615 0$aElectrical engineering. 615 14$aSystems and Data Security. 615 24$aComputer Communication Networks. 615 24$aCommunications Engineering, Networks. 676 $a005.365 700 $aZhang$b Mu$4aut$4http://id.loc.gov/vocabulary/relators/aut$0922404 702 $aYin$b Heng$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910151858103321 996 $aAndroid Application Security$92069862 997 $aUNINA