Malicious traffic analysis using Markov chain

Author:

Djap Ryandy1,Lim Charles1,Silaen Kalpin Erlangga1

Affiliation:

1. Swiss German University, Indonesia

Publisher

ACM

Reference27 articles.

1. BOTection: Bot Detection by Building Markov Chain Models of Bots Network Behavior

2. Davide Ariu , Roberto Tronci , and Giorgio Giacinto . 2011. HMMPayl: An intrusion detection system based on Hidden Markov Models. computers & security 30, 4 ( 2011 ), 221–241. https://doi.org/10.1016/j.cose.2010.12.004 10.1016/j.cose.2010.12.004 Davide Ariu, Roberto Tronci, and Giorgio Giacinto. 2011. HMMPayl: An intrusion detection system based on Hidden Markov Models. computers & security 30, 4 (2011), 221–241. https://doi.org/10.1016/j.cose.2010.12.004

3. Using Hidden Markov Models to Evaluate the Risks of Intrusions

4. Limitation of Listed-Rule Firewall and the Design of Tree-Rule Firewall

5. Ryandy Djap , Charles Lim , Kalpin Erlangga Silaen , and Andi Yusuf . 2021 . XB-Pot: Revealing Honeypot-based Attacker’s Behaviors. In 2021 9th International Conference on Information and Communication Technology (ICoICT). IEEE, 550–555 . https://doi.org/10.1109/ICoICT52021.2021.9527422 10.1109/ICoICT52021.2021.9527422 Ryandy Djap, Charles Lim, Kalpin Erlangga Silaen, and Andi Yusuf. 2021. XB-Pot: Revealing Honeypot-based Attacker’s Behaviors. In 2021 9th International Conference on Information and Communication Technology (ICoICT). IEEE, 550–555. https://doi.org/10.1109/ICoICT52021.2021.9527422

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