%0 Conference Proceedings
%T A Probabilistic Diffusion Scheme for Anomaly Detection on Smartphones
%+ Deutsche Telekom Labs.
%+ Fraunhofer Institute for Intelligent Analysis and Information Systems (Fraunhofer IAIS)
%+ DAI-Labor
%A Alpcan, Tansu
%A Bauckhage, Christian
%A Schmidt, Aubrey-Derrick
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 4th IFIP WG 11.2 International Workshop on Information Security Theory and Practices: Security and Privacy of Pervasive Systems and Smart Devices (WISTP)
%C Passau, Germany
%Y Pierangela Samarati; Michael Tunstall; Joachim Posegga; Konstantinos Markantonakis; Damien Sauveron
%I Springer
%3 Information Security Theory and Practices. Security and Privacy of Pervasive Systems and Smart Devices
%V LNCS-6033
%P 31-46
%8 2010-04-12
%D 2010
%R 10.1007/978-3-642-12368-9_3
%K Anomaly detection
%K mobile security
%K machine learning
%Z Computer Science [cs]/Digital Libraries [cs.DL]Conference papers
%X Widespread use and general purpose computing capabilities of next generation smartphones make them the next big targets of malicious software (malware) and security attacks. Given the battery, computing power, and bandwidth limitations inherent to such mobile devices, detection of malware on them is a research challenge that requires a different approach than the ones used for desktop/laptop computing. We present a novel probabilistic diffusion scheme for detecting anomalies possibly indicating malware which is based on device usage patterns. The relationship between samples of normal behavior and their features are modeled through a bipartite graph which constitutes the basis for the stochastic diffusion process. Subsequently, we establish an indirect similarity measure among sample points. The diffusion kernel derived over the feature space together with the Kullback-Leibler divergence over the sample space provide an anomaly detection algorithm. We demonstrate its applicability in two settings using real world mobile phone data. Initial experiments indicate that the diffusion algorithm outperforms others even under limited training data availability.
%G English
%2 https://inria.hal.science/hal-01056068/document
%2 https://inria.hal.science/hal-01056068/file/60330032.pdf
%L hal-01056068
%U https://inria.hal.science/hal-01056068
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-LNCS-6033
%~ IFIP-TC
%~ IFIP-TC11
%~ IFIP-WISTP
%~ IFIP-WG11-2
%~ IFIP-2010