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001 978-3-319-47812-8
003 DE-He213
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008 161117s2016 gw | s |||| 0|eng d
020 _a9783319478128
_9978-3-319-47812-8
024 7 _a10.1007/978-3-319-47812-8
_2doi
050 4 _aQA76.9.A25
072 7 _aUR
_2bicssc
072 7 _aUTN
_2bicssc
072 7 _aCOM053000
_2bisacsh
082 0 4 _a005.8
_223
100 1 _aZhang, Mu.
_eauthor.
245 1 0 _aAndroid Application Security
_h[electronic resource] :
_bA Semantics and Context-Aware Approach /
_cby Mu Zhang, Heng Yin.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXI, 105 p. 37 illus., 29 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Computer Science,
_x2191-5768
505 0 _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.
520 _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.
650 0 _aComputer science.
650 0 _aComputer communication systems.
650 0 _aComputer security.
650 0 _aElectrical engineering.
650 1 4 _aComputer Science.
650 2 4 _aSystems and Data Security.
650 2 4 _aComputer Communication Networks.
650 2 4 _aCommunications Engineering, Networks.
700 1 _aYin, Heng.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319478111
830 0 _aSpringerBriefs in Computer Science,
_x2191-5768
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-47812-8
912 _aZDB-2-SCS
942 _cEBK
999 _c57725
_d57725