Abstract
Almost any malware attack involves data communication between the infected host and the attacker host/server allowing the latter to remotely control the infected host. The remote control is achieved through opening different types of sessions such as remote desktop, webcam video streaming, file transfer, etc. In this paper, we present a traffic analysis based malware detection technique using Hidden Markov Model (HMM). The main contribution is that the proposed system does not only detect malware infections but also identifies with precision the type of malicious session opened by the attacker. The empirical analysis shows that the proposed detection system has a stable identification precision of 90 % and that it allows to identify between 40 % and 75 % of all malicious sessions in typical network traffic.
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Notes
- 1.
Malware and Bot will be used interchangeably.
- 2.
For large scale systems, the honeypot machine can be replaced by a full honeyNet network.
- 3.
All the experiments were carried out using virtual machines both for the infected host and the attacker/C&C server.
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© 2015 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Zhioua, S., Jabeur, A.B., Langar, M., Ilahi, W. (2015). Detecting Malicious Sessions Through Traffic Fingerprinting Using Hidden Markov Models. In: Tian, J., Jing, J., Srivatsa, M. (eds) International Conference on Security and Privacy in Communication Networks. SecureComm 2014. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 152. Springer, Cham. https://doi.org/10.1007/978-3-319-23829-6_47
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DOI: https://doi.org/10.1007/978-3-319-23829-6_47
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