A Probabilistic Diffusion Scheme for Anomaly Detection on Smartphones

Abstract : 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.
Type de document :
Communication dans un congrès
Pierangela Samarati; Michael Tunstall; Joachim Posegga; Konstantinos Markantonakis; Damien Sauveron. 4th IFIP WG 11.2 International Workshop on Information Security Theory and Practices: Security and Privacy of Pervasive Systems and Smart Devices (WISTP), Apr 2010, Passau, Germany. Springer, Lecture Notes in Computer Science, LNCS-6033, pp.31-46, 2010, Information Security Theory and Practices. Security and Privacy of Pervasive Systems and Smart Devices. 〈10.1007/978-3-642-12368-9_3〉
Liste complète des métadonnées

Littérature citée [28 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01056068
Contributeur : Hal Ifip <>
Soumis le : jeudi 14 août 2014 - 18:07:12
Dernière modification le : samedi 16 décembre 2017 - 07:18:04
Document(s) archivé(s) le : jeudi 27 novembre 2014 - 01:32:02

Fichier

60330032.pdf
Fichiers produits par l'(les) auteur(s)

Licence


Distributed under a Creative Commons Paternité 4.0 International License

Identifiants

Citation

Tansu Alpcan, Christian Bauckhage, Aubrey-Derrick Schmidt. A Probabilistic Diffusion Scheme for Anomaly Detection on Smartphones. Pierangela Samarati; Michael Tunstall; Joachim Posegga; Konstantinos Markantonakis; Damien Sauveron. 4th IFIP WG 11.2 International Workshop on Information Security Theory and Practices: Security and Privacy of Pervasive Systems and Smart Devices (WISTP), Apr 2010, Passau, Germany. Springer, Lecture Notes in Computer Science, LNCS-6033, pp.31-46, 2010, Information Security Theory and Practices. Security and Privacy of Pervasive Systems and Smart Devices. 〈10.1007/978-3-642-12368-9_3〉. 〈hal-01056068〉

Partager

Métriques

Consultations de la notice

136

Téléchargements de fichiers

321