The effects of feature selection on the classification of encrypted botnet

Author:

Ismail ZahianORCID,Jantan Aman,Yusoff Mohd. Najwadi,Kiru Muhammad Ubale

Funder

Universiti Sains Malaysia

Publisher

Springer Science and Business Media LLC

Subject

Computational Theory and Mathematics,Hardware and Architecture,Software,Computer Science (miscellaneous)

Reference41 articles.

1. Cisco.: Enterprise Network Security - Encrypted Traffic Analytics (ETA). https://www.cisco.com/c/en/us/solutions/enterprise-networks/enterprise-network-security/eta.html#~stickynav=1 (2019). Accessed 22 March 2019

2. Schwartz, M.J.: Police disrupt banking malware botnet but worldwide impact of ramnit takedown may prove temporary. https://www.bankinfosecurity.com/european-police-target-ramnit-botnet-a-7947 (2015). Accessed 20 March 2019

3. CyberSecurity Malaysia.: Malaysia vs Malware. http://www.cybersecurity.my/en/knowledge_bank/news/2010/main/detail/1900/index.html (2010). Accessed 12 May 2016

4. Cloudbric.: 3 devastating cyber attacks on banks that show how vulnerable our money is. https://www.cloudbric.com/blog/2015/09/3-devastating-cyberattacks-on-banks-that-show-how-vulnerable-our-money-is/ (2018). Accessed 21 March 2019

5. Zelster, L.: When bots use social media for command and control. https://zeltser.com/bots-command-and-control-via-social-media/ (2015). Accessed 24 March 2019

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