MADAM: A multi-level anomaly detector for android malware

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7531 LNCS, Issue , 2012, Pages 240-253

  Dini, Gianluca ^a   Martinelli, Fabio ^b   Saracino, Andrea ^a,b   Sgandurra, Daniele ^b  

^a UNIVERSITY OF PISA   (Italy)

^b Istituto di informatica e telematica del Consiglio nazionale delle ricerche   (Italy)

Author keywords

Android; Classification; Intrusion detection; Security

Indexed keywords

ANDROID (OPERATING SYSTEM); CLASSIFICATION (OF INFORMATION); COMPUTER ARCHITECTURE; INTRUSION DETECTION; LEARNING SYSTEMS; MALWARE; NETWORK ARCHITECTURE; NETWORK SECURITY; SMARTPHONES;

ANDROID; ANDROID MALWARE; ANOMALY DETECTOR; FALSE POSITIVE; LEARNING PHASE; MACHINE LEARNING TECHNIQUES; SECURITY; SECURITY SOLUTIONS;

MOBILE SECURITY;

EID: 84872224783     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33704-8_21     Document Type: Conference Paper

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