DC Field | Value | Language |
dc.contributor.author | BENCHIEKH, MOustafa Choukri | - |
dc.contributor.author | BENHABRA, ABdesselam | - |
dc.date.accessioned | 2022-04-18T10:10:01Z | - |
dc.date.available | 2022-04-18T10:10:01Z | - |
dc.date.issued | 2020 | - |
dc.identifier.uri | https://repository.esi-sba.dz/jspui/handle/123456789/252 | - |
dc.description.abstract | Due 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.iso | en | en_US |
dc.title | Network Anomaly Detection | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | Master
|