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
A Reactive Tunneling (RT) Scheme to Improve Handover Latency over the Mobile Network Environment
AICT '08 Proceedings of the 2008 Fourth Advanced International Conference on Telecommunications
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In this paper, we would like to introduce a multi-agent mechanism to protect target systems from computer virus infections. Using a machine learning approach, we first define a form of object knowledge to determine computer viruses and diagnosed objects. Second, we set up an association model of knowledge base and database. The database stores information of diagnosed objects. The knowledge base contains certain sets of deduction rules. Finally, we build two active agents to control virus infections. In an event-learning model, the first agent named Virus Auto-protect Agent is used to monitor all suspicious events. The second one, Virus Scanning Agent is used in an explanation-learning model to scan for viruses, to warn users of dangers and to restore the data from the previous state of safety. The experimentation results show that the anti-virus system can quickly recognize known and unknown computer virus infections.