000 03651nam a22006015i 4500
001 978-981-97-1459-9
003 DE-He213
005 20240730172145.0
007 cr nn 008mamaa
008 240523s2024 si | s |||| 0|eng d
020 _a9789819714599
_9978-981-97-1459-9
024 7 _a10.1007/978-981-97-1459-9
_2doi
050 4 _aTK5105.59
072 7 _aUTN
_2bicssc
072 7 _aCOM043050
_2bisacsh
072 7 _aUTN
_2thema
082 0 4 _a005.8
_223
100 1 _aNiu, Weina.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9102124
245 1 0 _aAndroid Malware Detection and Adversarial Methods
_h[electronic resource] /
_cby Weina Niu, Xiaosong Zhang, Ran Yan, Jiacheng Gong.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXIV, 190 p. 5 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
520 _aThe rise of Android malware poses a significant threat to users' information security and privacy. Malicious software can inflict severe harm on users by employing various tactics, including deception, personal information theft, and device control. To address this issue, both academia and industry are continually engaged in research and development efforts focused on detecting and countering Android malware. This book is a comprehensive academic monograph crafted against this backdrop. The publication meticulously explores the background, methods, adversarial approaches, and future trends related to Android malware. It is organized into four parts: the overview of Android malware detection, the general Android malware detection method, the adversarial method for Android malware detection, and the future trends of Android malware detection. Within these sections, the book elucidates associated issues, principles, and highlights notable research. By engaging with this book, readers will gain not only a global perspective on Android malware detection and adversarial methods but also a detailed understanding of the taxonomy and general methods outlined in each part. The publication illustrates both the overarching model and representative academic work, facilitating a profound comprehension of Android malware detection.
650 0 _aComputer networks
_xSecurity measures.
_93969
650 0 _aData protection.
_97245
650 0 _aData protection
_xLaw and legislation.
_923450
650 0 _aMachine learning.
_91831
650 0 _aBlockchains (Databases).
_9102127
650 1 4 _aMobile and Network Security.
_933624
650 2 4 _aData and Information Security.
_931990
650 2 4 _aSecurity Services.
_978910
650 2 4 _aPrivacy.
_935098
650 2 4 _aMachine Learning.
_91831
650 2 4 _aBlockchain.
_9102129
700 1 _aZhang, Xiaosong.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9102130
700 1 _aYan, Ran.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9102132
700 1 _aGong, Jiacheng.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9102133
710 2 _aSpringerLink (Online service)
_9102135
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819714582
776 0 8 _iPrinted edition:
_z9789819714605
776 0 8 _iPrinted edition:
_z9789819714612
856 4 0 _uhttps://doi.org/10.1007/978-981-97-1459-9
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cEBK
999 _c88067
_d88067