TY - GEN
T1 - Dimensionality Reduction for Machine Learning Based IoT Botnet Detection
AU - Bahsi, Hayretdin
AU - Nomm, Sven
AU - La Torre, Fabio Benedetto
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/18
Y1 - 2018/12/18
N2 - The rapid development of the internet of things caused severe security problems such as the cyber attacks launched by extremely huge botnets comprised of IoT devices. The detection of these devices is essential for protecting the networks. Recently, some of the studies have demonstrated the high accuracy of machine learning methods, including deep learning, in detecting IoT botnets. However, the minimizing of the required features for classification is highly needed for overcoming scalability and computation resource problems in IoT environments. Having results which can be readily interpretable by cyber security analysts and producing signatures for the contemporary intrusion detection or network monitoring systems are other significant factors in this area in which quick and widespread security adaption is highly required. In this study, we applied feature selection to minimize the number of features in detecting the IoT bots. It is shown that fewer features can achieve very high accuracy rates and afford interpretable results with a multi-class classifier based on a shallow method, decision tree.
AB - The rapid development of the internet of things caused severe security problems such as the cyber attacks launched by extremely huge botnets comprised of IoT devices. The detection of these devices is essential for protecting the networks. Recently, some of the studies have demonstrated the high accuracy of machine learning methods, including deep learning, in detecting IoT botnets. However, the minimizing of the required features for classification is highly needed for overcoming scalability and computation resource problems in IoT environments. Having results which can be readily interpretable by cyber security analysts and producing signatures for the contemporary intrusion detection or network monitoring systems are other significant factors in this area in which quick and widespread security adaption is highly required. In this study, we applied feature selection to minimize the number of features in detecting the IoT bots. It is shown that fewer features can achieve very high accuracy rates and afford interpretable results with a multi-class classifier based on a shallow method, decision tree.
UR - http://www.scopus.com/inward/record.url?scp=85060777585&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060777585&partnerID=8YFLogxK
U2 - 10.1109/ICARCV.2018.8581205
DO - 10.1109/ICARCV.2018.8581205
M3 - Conference contribution
AN - SCOPUS:85060777585
T3 - 2018 15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018
SP - 1857
EP - 1862
BT - 2018 15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018
Y2 - 18 November 2018 through 21 November 2018
ER -