Abstract: The Internet is inherently open and interactive,making the attacker use the network vulnerabilities to destroy the network.Network attacks are generally conceal and highly hazardous,so how to effectively detect network attacks becomes extremely important.In order to solve the problem that most of the detection algorithms can only detect a kind of network attack,and the detection delay is high,this paper proposed a negative selection algorithm based on density automatic partition clustering method with self-set,referred to DAPC-NSA.The algorithm uses the density clustering algorithm to preprocess the self-training data,performs cluster analysis on the training data,eliminates the noise,and generates the self-detector.And then it generates the nonself-detector according to the self-detector,and uses the self-detector and nonself-detector to detect the anomalies.The simulated intrusion detection experiment was carried out.The experiment shows that the algorithm can not only detect six kinds of attacks simultaneously,but also has the higher detection rate and the lower false alarm rate.The detection time is short compared with other detection algorithm,and it can achieve the target of real-time detection.

Key words: Attack detection, DAPC-NSA, Detectors, Network attack simulation, Network security, Self-adaptive immune