الفهرس | Only 14 pages are availabe for public view |
Abstract Network security breaches are jumping to a higher level every day,so network intrusion detection systems (NIDSs) must keep pace with this enormous increase in intrusion methods. Most of current NIDSs are rule-based systems, which deoend on ststic rules that are very difficult in encoding and can’t be useful in the detection of novel intrusions.Therefore,this thesis is concerned with proposing a hybrid network intrusion detection framwork that depends on data mining classification and clustering techniques in misuse and anomaly intrision detection. |