TY - GEN
T1 - Multi Class Machine Learning Algorithms for Intrusion Detection - A Performance Study
AU - Belavagi, Manjula C.
AU - Muniyal, Balachandra
N1 - Publisher Copyright:
© 2017, Springer Nature Singapore Pte Ltd.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - Advancement of the network technology has increased our dependency on the Internet. Hence the security of the network plays a very important role. The network intrusions can be identified using Intrusion Detection System (IDS). Machine learning algorithms are used to predict the network behavior as intrusion or normal. This paper discusses the prediction analysis of different supervised machine learning algorithms namely Logistic Regression, Gaussian Naive Bayes, Support Vector Machine and Random Forest on NSL-KDD dataset. These machine learning classification techniques are used to predict the four different types of attacks namely Denial of Service attack, Remote to Local (R2L), Probe and User to Root(U2R) attacks using multi-class classification technique.
AB - Advancement of the network technology has increased our dependency on the Internet. Hence the security of the network plays a very important role. The network intrusions can be identified using Intrusion Detection System (IDS). Machine learning algorithms are used to predict the network behavior as intrusion or normal. This paper discusses the prediction analysis of different supervised machine learning algorithms namely Logistic Regression, Gaussian Naive Bayes, Support Vector Machine and Random Forest on NSL-KDD dataset. These machine learning classification techniques are used to predict the four different types of attacks namely Denial of Service attack, Remote to Local (R2L), Probe and User to Root(U2R) attacks using multi-class classification technique.
UR - https://www.scopus.com/pages/publications/85034596795
UR - https://www.scopus.com/inward/citedby.url?scp=85034596795&partnerID=8YFLogxK
U2 - 10.1007/978-981-10-6898-0_14
DO - 10.1007/978-981-10-6898-0_14
M3 - Conference contribution
AN - SCOPUS:85034596795
SN - 9789811068973
T3 - Communications in Computer and Information Science
SP - 170
EP - 178
BT - Security in Computing and Communications - 5th International Symposium, SSCC 2017, Proceedings
A2 - Martinez Perez, Gregorio
A2 - Gomez Marmol, Felix
A2 - Thampi, Sabu M.
A2 - Westphall, Carlos Becker
A2 - Fan, Chun I.
A2 - Hu, Jiankun
PB - Springer Verlag
T2 - 5th International Symposium on Security in Computing and Communications, SSCC 2017
Y2 - 13 September 2017 through 16 September 2017
ER -