基于自适应鲁棒性的入侵检测模型

A new intrusion detection model based on adaptability and robustness

传感器与网络技术的迅猛发展促进了信息物理系统的发展与应用.而传统网络系统的入侵检测技术已经发展成熟,信息物理系统(CPS)可以在借鉴传统网络系统入侵检测技术的基础上,结合自身特性进行改进.针对CPS所处地理位置复杂及网络传输不可靠导致的检测鲁棒性不高的问题,提出基于稀疏降噪自编码网络(SDAE)的入侵检测算法;同时,考虑到CPS对模型适应性及推广性的需求,将基于差分变换的头脑风暴优化算法(DBSO)与改进的自编码网络相结合,形成基于DBSO优化SDAE(DBSO-SDAE)的检测算法.该算法具有自动提取入侵数据最优特征表示的能力,同时在进一步提高模型鲁棒性的前提下,可极大地增强模型的适应性.仿真结果表明,所提出的DBSO-SDAE模型与其他模型相比,具有较高的鲁棒性、自适应性及较优的检测实时性,可极大地满足CPS对检测算法的高需求.

The rapid development of sensor and network technology promotes the development and application of cyber physical system,while the intrusion detection technology of the traditional network system has matured.The cyber physical system(CPS)can be improved in combination with its own characteristics based on the traditional intrusion network technology.The geographic location of CPS is complex and the network transmission is unreliable,which lead to that the detection robustness is not high.Aiming at this problem,an intrusion detection algorithm based on sparse denoising auto-encoders(SDAE)is proposed.What’s more,CPS requires models to be adaptive and generalized,so difference brain storm optimization(DBSO)based optimization of SDAE(DBSO-based optimization of SDAE,DBSO-SDAE)detection algorithm is formed by combining DBSO with improved auto-encoders.The algorithm can automatically extract the optimal feature representation of intrusion data and greatly enhance the adaptability of the model while further improving the robustness of the model.The simulation results show that the DBSO-SDAE model proposed in this paper has higher robustness,adaptability and better real-time detection than other models,which greatly satisfies the high demand of CPS for detection algorithms.