A New Algorithm of Network Intrusion Detection base on the Application of Conditional Random Fields

While the network brings convenience to people, its own fragility offers intrusion opportunities for hackers and malicious attackers. Along with the diversity and complexity of intrusion attack, high performance intrusion detection techniques are required, and so the study of on-line detection, adaptive detection and multiclass detection techniques becomes current hotspot. To improve the performance of multiclass intrusion detection system(IDS), this thesis puts forward a method of CRFs (Conditional Random Fields) based on attribute sets in IDS. This Algorithm uses varied connection information and its relativity in network connection information data sequence as well as the feature sets relativity in data sequence to attack detection and discovery of abnormal phenomenon. In this thesis, after the discussion of the work process of the models and the comparison between KDD cup’99 data sets’ detective conclusion and other test methods. The simulation results show that the proposed algorithm is practicable, reliable and efficient.