Randomizing Smartphone Malware Profiles against Statistical Mining Techniques

Abstract : The growing use of smartphones opens up new opportunities for malware activities such as eavesdropping on phone calls, reading e-mail and call-logs, and tracking callers’ locations. Statistical data mining techniques have been shown to be applicable to detect smartphone malware. In this paper, we demonstrate that statistical mining techniques are prone to attacks that lead to random smartphone malware behavior. We show that with randomized profiles, statistical mining techniques can be easily foiled. Six in-house proof-of-concept malware programs are developed on the Android platform for this study. The malware programs are designed to perform privacy intrusion, information theft, and denial of service attacks. By simulating and tuning the frequency and interval of attacks, we aim to answer the following questions: 1) Can statistical mining algorithms detect smartphone malware by monitoring the statistics of smartphone usage? 2) Are data mining algorithms robust against malware with random profiles? 3) Can simple consolidation of random profiles over a fixed time frame prepare a higher quality data source for existing algorithms?
Type de document :
Communication dans un congrès
Nora Cuppens-Boulahia; Frédéric Cuppens; Joaquin Garcia-Alfaro. 26th Conference on Data and Applications Security and Privacy (DBSec), Jul 2012, Paris, France. Springer, Lecture Notes in Computer Science, LNCS-7371, pp.239-254, 2012, Data and Applications Security and Privacy XXVI. 〈10.1007/978-3-642-31540-4_18〉
Liste complète des métadonnées

https://hal.inria.fr/hal-01534774
Contributeur : Hal Ifip <>
Soumis le : jeudi 8 juin 2017 - 11:06:39
Dernière modification le : dimanche 31 décembre 2017 - 17:16:05
Document(s) archivé(s) le : samedi 9 septembre 2017 - 12:59:32

Fichier

978-3-642-31540-4_18_Chapter.p...
Fichiers produits par l'(les) auteur(s)

Licence


Distributed under a Creative Commons Paternité 4.0 International License

Identifiants

Citation

Abhijith Shastry, Murat Kantarcioglu, Yan Zhou, Bhavani Thuraisingham. Randomizing Smartphone Malware Profiles against Statistical Mining Techniques. Nora Cuppens-Boulahia; Frédéric Cuppens; Joaquin Garcia-Alfaro. 26th Conference on Data and Applications Security and Privacy (DBSec), Jul 2012, Paris, France. Springer, Lecture Notes in Computer Science, LNCS-7371, pp.239-254, 2012, Data and Applications Security and Privacy XXVI. 〈10.1007/978-3-642-31540-4_18〉. 〈hal-01534774〉

Partager

Métriques

Consultations de la notice

300

Téléchargements de fichiers

14