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المؤلفEltanbouly, Sohaila
المؤلفBashendy, May
المؤلفAlnaimi, Noora
المؤلفChkirbene, Zina
المؤلفErbad, Aiman
تاريخ الإتاحة2024-03-04T04:55:29Z
تاريخ النشر2020-02-01
اسم المنشور2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020
المعرّفhttp://dx.doi.org/10.1109/ICIoT48696.2020.9089465
الاقتباسS. Eltanbouly, M. Bashendy, N. AlNaimi, Z. Chkirbene and A. Erbad, "Machine Learning Techniques for Network Anomaly Detection: A Survey," 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), Doha, Qatar, 2020, pp. 156-162, doi: 10.1109/ICIoT48696.2020.9089465.
الترقيم الدولي الموحد للكتاب 9781728148212
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85085496336&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/52616
الملخصNowadays, 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.
اللغةen
الناشرIEEE
الموضوعAnomaly detection
intrusion detection systems
machine Learning
network security
العنوانMachine Learning Techniques for Network Anomaly Detection: A Survey
النوعConference Paper
الصفحات156-162
dc.accessType Abstract Only


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