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Please use this identifier to cite or link to this item: https://repository.esi-sba.dz/jspui/handle/123456789/252
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dc.contributor.authorBENCHIEKH, MOustafa Choukri-
dc.contributor.authorBENHABRA, ABdesselam-
dc.date.accessioned2022-04-18T10:10:01Z-
dc.date.available2022-04-18T10:10:01Z-
dc.date.issued2020-
dc.identifier.urihttps://repository.esi-sba.dz/jspui/handle/123456789/252-
dc.description.abstractDue to the immense growth of applications with connected users and services in the last decade, monitoring networks and guarding them by identifying security vulnerabilities along with detecting anomalies has become the fundamental daily task of network administrators. Keeping an eye on the network traffic and bandwidth usage have proved its great efficiency when it comes to differentiating malicious network behavior from the normal one. Besides that and as the primary defense line of the network infrastructure, intrusion detection systems are expected to adapt to the dynamically changing threat patterns, thus, various techniques have been developed by researchers from different disciplines like mathematics, machine learning, and data mining in order to achieve a good immunity against attacks and reliable detection of anomalies.en_US
dc.language.isoenen_US
dc.titleNetwork Anomaly Detectionen_US
dc.typeThesisen_US
Appears in Collections:Master

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