Comparison of Machine Learning Techniques on Snort for Predicting Realtime DoS and Probe Attack
Author:
Affiliation:
1. Politeknik Siber dan Sandi Negara,Cyber Security Engineering,Bogor,Indonesia
2. Politeknik Siber dan Sandi Negara,Cryptographic Engineering,Bogor,Indonesia
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10017433/10017449/10017776.pdf?arnumber=10017776
Reference17 articles.
1. INTRUSION DETECTION SYSTEM;tiwari;Article in International Journal of Technical Research and Applications,2017
2. Supervised Machine Learning Algorithms: Classification and Comparison
3. An Insider Data Leakage Detection Using One-Hot Encoding, Synthetic Minority Oversampling and Machine Learning Techniques
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