構(gòu)建了基于免疫算法和神經(jīng)網(wǎng)絡(luò)的新型抗體網(wǎng)絡(luò)入侵檢測系統(tǒng)，系統(tǒng)與網(wǎng)絡(luò)入侵檢測功能相結(jié)合，應(yīng)用于大型網(wǎng)絡(luò)的入侵檢測任務(wù)，具有良好的可擴(kuò)充性；重點(diǎn)討論了新型抗體網(wǎng)絡(luò)原理，引進(jìn)BP神經(jīng)網(wǎng)絡(luò)自學(xué)習(xí)能力，對已有的抗體網(wǎng)絡(luò)模型進(jìn)行改進(jìn)；通過對網(wǎng)絡(luò)數(shù)據(jù)集的測試表明，該算法相對于傳統(tǒng)抗體網(wǎng)絡(luò)，其檢測效率得到了明顯的改善。This paper designs an antibody network base on immune algorithm and neural network used for Intrusion Detection System. The Abnet network system combines network-based intrusion detection functions. It can be used to protect large area network and has relatively good expansibility. This paper also discusses the theory of the Abnet. How to implement of the Abnet with BP neural network in intrusion detection. Because of the traditional Abnet weakness in learning time and performance, This network is designed. The algorithm has been tested on a network data set. The result showed that it had much better performance than traditional Abnet.