000			04283nam a22003855i 4500
001			361400
003			ES-MaUEC
005			20230102121353.0
007			cr nn nnnaamaa
008			210710s2021 gw | s |||| 0|eng d
020			_a9783030746643
024	7		_a10.1007/978-3-030-74664-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC
050		4	_aTK5105.59 _b2021 EB
100	1		_aKarbab, ElMouatez Billah _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aAndroid Malware Detection using Machine Learning _bData-Driven Fingerprinting and Threat Intelligence _cby ElMouatez Billah Karbab, Mourad Debbabi, Abdelouahid Derhab, Djedjiga Mouheb.
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Publising _c2021
300			_a1 recurso en línea (XIV, 202 páginas) _b81 ilustraciones, 64 ilustraciones a color
347			_aarchivo de texto _bPDF
490	0		_aAdvances in Information Security _x1568-2633 _v86
505	0		_aIntroduction -- Background and Related Work -- Fingerprinting Android Malware Packages -- Robust Android Malicious Community Fingerprinting -- Android Malware Fingerprinting Using Dynamic Analysis -- Fingerprinting Cyber-Infrastructures of Android Malware -- Portable Supervised Malware Fingerprinting using Deep Learning -- Resilient and Adaptive Android Malware Fingerprinting and Detection -- Conclusion.
520	3		_aThe authors develop a malware fingerprinting framework to cover accurate android malware detection and family attribution in this book. The authors emphasize the following: (1) the scalability over a large malware corpus; (2) the resiliency to common obfuscation techniques; (3) the portability over different platforms and architectures. First, the authors propose an approximate fingerprinting technique for android packaging that captures the underlying static structure of the android applications in the context of bulk and offline detection at the app-market level. This book proposes a malware clustering framework to perform malware clustering by building and partitioning the similarity network of malicious applications on top of this fingerprinting technique. Second, the authors propose an approximate fingerprinting technique that leverages dynamic analysis and natural language processing techniques to generate Android malware behavior reports. Based on this fingerprinting technique, the authors propose a portable malware detection framework employing machine learning classification. Third, the authors design an automatic framework to produce intelligence about the underlying malicious cyber-infrastructures of Android malware. The authors then leverage graph analysis techniques to generate relevant intelligence to identify the threat effects of malicious Internet activity associated with android malware. The authors elaborate on an effective android malware detection system, in the online detection context at the mobile device level. It is suitable for deployment on mobile devices, using machine learning classification on method call sequences. Also, it is resilient to common code obfuscation techniques and adaptive to operating systems and malware change overtime, using natural language processing and deep learning techniques. Researchers working in mobile and network security, machine learning and pattern recognition will find this book useful as a reference. Advanced-level students studying computer science within these topic areas will purchase this book as well.
700	1		_aDebbabi, Mourad _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aDerhab, Abdelouahid _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aMouheb, Djedjiga _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iPrinted edition: _z9783030746636
776	0	8	_iPrinted edition: _z9783030746650
776	0	8	_iPrinted edition: _z9783030746667
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-74664-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
490	0		_aComputer Science (SpringerNature-11645)
490	0		_aComputer Science (R0) (SpringerNature-43710)
942			_2lcc _cLE
988			_aSpringer_Computer_2021
999			_c361400 _d361400 _x1