Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Data Mining Methods for Detection of New Malicious Executables
SP '01 Proceedings of the 2001 IEEE Symposium on Security and Privacy
The Art of Computer Virus Research and Defense
The Art of Computer Virus Research and Defense
VirusMeter: Preventing Your Cellphone from Spies
RAID '09 Proceedings of the 12th International Symposium on Recent Advances in Intrusion Detection
Exploitation and threat analysis of open mobile devices
Proceedings of the 5th ACM/IEEE Symposium on Architectures for Networking and Communications Systems
SEIP: simple and efficient integrity protection for open mobile platforms
ICICS'10 Proceedings of the 12th international conference on Information and communications security
A specification based intrusion detection framework for mobile phones
ACNS'11 Proceedings of the 9th international conference on Applied cryptography and network security
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In this paper, we describe a new solution for detecting mobile phone viruses. The solution is based on Bayesian decision theory using heuristic rules derived from common functionalities among different virus samples. Specifically, we detect viruses according to the DLL usage of a program, which is directly linked to the functionality of this program. Our solution is able to detect unknown viruses, especially the variants of existing ones. We evaluate our solution on the Symbian platform, where most viruses are present in the wild. We constructed a virus detector based on DLL functions from a small set of virus samples. It detects 95% of mobile viruses and yields no false alarm.