@article {Karbal2020157,
	title = {A Comparison of Different Machine Learning Algorithms for Intrusion Detection},
	journal = {Communications in Computer and Information Science},
	volume = {1264},
	year = {2020},
	note = {cited By 0},
	pages = {157-169},
	abstract = {With the rapid development of the internet, intrusion detection became one of the major research problems in computer security. Many Intrusion Detection Systems (IDS) use data mining algorithms for classifying network traffic data and detecting different security violations. In this paper, we present some of the datasets and methods employed with the focus on network anomaly detection. We compare different machine learning techniques used in the latest research carried out for developing network intrusion detection systems. We also present an overview of some deep learning methodologies and their application for IDS purposes. The primary objective of this survey is to provide with a researcher, the state of the artwork already performed in this field of research. {\textcopyright} 2020, Springer Nature Switzerland AG.},
	keywords = {Anomaly detection, Classification (of information), Computer crime, Data communication systems, Data mining, Data mining algorithm, Deep learning, Intrusion detection, Intrusion Detection Systems, Learning algorithms, Learning systems, Machine learning techniques, Network anomaly detection, Network intrusion detection systems, Network security, Primary objective, Research problems, Security violations},
	doi = {10.1007/978-3-030-61143-9_13},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097408678\&doi=10.1007\%2f978-3-030-61143-9_13\&partnerID=40\&md5=3725b891261e386c6df84eebcbc0e514},
	author = {Karbal, B. and Romadi, R.}
}
