@inproceedings{1de26a1cab854e6d8f0b8ede0ae1eca3,
title = "Machine Learning Techniques for Network Anomaly Detection: A Survey",
abstract = "Nowadays, distributed data processing in cloud computing has gained increasing attention from many researchers. The intense transfer of data has made the network an attractive and vulnerable target for attackers to exploit and experiment with different types of attacks. Therefore, many intrusion detection techniques have been evolving to protect cloud distributed services by detecting the different attack types on the network. Machine learning techniques have been heavily applied in intrusion detection systems with different algorithms. This paper surveys recent research advances linked to machine learning techniques. We review some representative algorithms and discuss their proprieties in detail. We compare them in terms of intrusion accuracy and detection rate using different data sets.",
keywords = "Anomaly detection, intrusion detection systems, machine Learning, network security",
author = "Sohaila Eltanbouly and May Bashendy and Noora Alnaimi and Zina Chkirbene and Aiman Erbad",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020 ; Conference date: 02-02-2020 Through 05-02-2020",
year = "2020",
month = feb,
doi = "10.1109/ICIoT48696.2020.9089465",
language = "English",
series = "2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "156--162",
booktitle = "2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020",
address = "United States",
}