TY - GEN
T1 - An efficient botnet detection methodology using hyper-parameter optimization trough grid-search techniques
AU - Gonzalez-Cuautle, David
AU - Corral-Salinas, Uriel Yair
AU - Sanchez-Perez, Gabriel
AU - Perez-Meana, Hector
AU - Toscano-Medina, Karina
AU - Hernandez-Suarez, Aldo
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - In recent years botnets have become serious threats for Internet-based services and infrastructures. Prompt detection can mitigate the impact of several attacks including Denial-of-service (DDoS), spam, phishing, identity theft, and information leaking. Actually, physical and logical appliances over networks are addressing botnet discovery. However, signature-based solutions require constant updates from repositories, which is a concerning setback given the rapid development of new threats. An alternative solution to overcome such limitations is to train Machine Learning (ML) algorithms to accurately identify malicious network flows. Although the state-of-The-Art provide significant advances in botnet classification using machine and statistical learning, the algorithm selection procedure is not properly defined nor explained. In this work an algorithm portfolio is built to test performance between several supervised learning algorithms using a hyper-parameter optimization technique known as Grid Search. Experimental results prove that by tuning algorithms trained models can outperform detection accuracy in an efficient manner.
AB - In recent years botnets have become serious threats for Internet-based services and infrastructures. Prompt detection can mitigate the impact of several attacks including Denial-of-service (DDoS), spam, phishing, identity theft, and information leaking. Actually, physical and logical appliances over networks are addressing botnet discovery. However, signature-based solutions require constant updates from repositories, which is a concerning setback given the rapid development of new threats. An alternative solution to overcome such limitations is to train Machine Learning (ML) algorithms to accurately identify malicious network flows. Although the state-of-The-Art provide significant advances in botnet classification using machine and statistical learning, the algorithm selection procedure is not properly defined nor explained. In this work an algorithm portfolio is built to test performance between several supervised learning algorithms using a hyper-parameter optimization technique known as Grid Search. Experimental results prove that by tuning algorithms trained models can outperform detection accuracy in an efficient manner.
UR - http://www.scopus.com/inward/record.url?scp=85068479690&partnerID=8YFLogxK
U2 - 10.1109/IWBF.2019.8739208
DO - 10.1109/IWBF.2019.8739208
M3 - Contribución a la conferencia
AN - SCOPUS:85068479690
T3 - 2019 7th International Workshop on Biometrics and Forensics, IWBF 2019
BT - 2019 7th International Workshop on Biometrics and Forensics, IWBF 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Workshop on Biometrics and Forensics, IWBF 2019
Y2 - 2 May 2019 through 3 May 2019
ER -