A probabilistic diffusion scheme for anomaly detection on smartphones

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

Volumn 6033 LNCS, Issue , 2010, Pages 31-46

  Alpcan, Tansu ^a   Bauckhage, Christian ^b   Schmidt, Aubrey Derrick ^a  

^a TECHNISCHE UNIVERSITÄT BERLIN   (Germany)

^b UNIVERSITY OF BONN   (Germany)

Author keywords

Anomaly detection; Machine learning; Mobile security

Indexed keywords

ANOMALY DETECTION; BANDWIDTH LIMITATION; BIPARTITE GRAPHS; COMPUTING POWER; DIFFUSION ALGORITHM; DIFFUSION KERNEL; FEATURE SPACE; GENERAL-PURPOSE COMPUTING; KULLBACK LEIBLER DIVERGENCE; LIMITED TRAINING DATA; MACHINE-LEARNING; MALICIOUS SOFTWARE; MALWARES; MOBILE SECURITY; NORMAL BEHAVIOR; RESEARCH CHALLENGES; SAMPLE POINT; SAMPLE SPACE; SECURITY ATTACKS; SIMILARITY MEASURE; SMART-PHONES; STOCHASTIC DIFFUSION PROCESS; USAGE PATTERNS;

COMPUTER CRIME; DIFFUSION; GRAPH THEORY; LEARNING SYSTEMS; MOBILE DEVICES; ARTIFICIAL INTELLIGENCE; COMPUTATION THEORY; E-LEARNING; MALWARE; MOBILE SECURITY; SMARTPHONES; STOCHASTIC SYSTEMS;

SECURITY OF DATA;

EID: 78650393426     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-12368-9_3     Document Type: Conference Paper

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