A Cooperative Deep Belief Network for Intrusion Detection
Xudong Yang, Ling Chao Gao, Hai Wang, Jie Zheng, Rui Cao · 2018
With the large-scale promotion of cloud computing, intrusion detection is a necessary way to guaranteen cloud security. However, because of the lack of adaptive model, detection accuracy is still a challenge issue in intrusion detection. In our works, based on attribute significant expressing cooperative deep belief network (CDBN) was proposed for specific attack in intrusion detection. Firstly, a specific attack multi-view division method was proposed to extract the significant features of specific attack. secondly, an adaptive coding mechanism based on multi-view encoding was described to denoise and compress the attack features.finally, based on cumulative prospects, an cooperative decision-making deep belief network was proposed for cooperative intrusion detection. Thought testing and verified on the NSL-KDD data set, it proved that our proposed method has good applicability and high detection rate compared to the current general model.