大数据环境下混合入侵信息查询路径优化仿真

Query Path Optimization of Hybrid Intrusion Information in Big Data Environment

针对当前网络入侵查询路径优化方法实现后查询延迟长、能耗高的问题,提出基于蚁群的大数据环境下混合入侵信息查询路径优化方法。利用Hilbert变换实现入侵信息数据信号频谱特征提取,通过卡尔曼滤波法完成混合入侵信号的前置滤波,得到真实入侵信号,分析信号瞬时频率与瞬时幅值,将HHT频谱提取出来当作大数据环境下混合入侵信息数据信号检测结果。根据混合入侵信息数据信号检测结果,构建查询路径优化目标函数。对蚁群运行的参数和起点位置进行初始化,将查询关系间连接代价和蚂蚁于各关系间检索时释放出的信息素当作选择连接下个关系的基础,通过蚁群算法寻优特点找到最优连接路径,即满足目标函数的混合入侵信息查询路径,实现大数据环境下入侵数据查询路径优化。实验结果表明,所提方法实现后信息查询能耗平均为64.2nJ/bit,查询延迟短。

Due to long query delay and high energy consumption of current network intrusion query path optimization method, this paper proposes a method to optimize mixed intrusion information query path based on ant colony in big data environment. Firstly, Hilbert transform was used to extract the spectral feature of intrusion information data signal. Secondly, Kalman filtering method was used to complete the pre-filtering of mixed intrusion signal, so as to obtain the real intrusion signal. The instantaneous frequency and the instantaneous amplitude of signal were analyzed, and then HHT spectrum was extracted as the signal detection result of mixed intrusion information data. According to the detection result of mixed intrusion information data signal, the optimized objective function of query path was constructed. Moreover, operational parameter and starting position of ant colony were initialized. After that, the connection cost between query relationships and the pheromone released by ant when searching among relationships was regarded as the basis for selecting and connecting the next relationship. Finally, the ant colony algorithm optimization feature was used to find the optimal connection path, namely the mixed intrusion information query path that satisfied the objective function. Thus, the optimization of intrusion data query path in big data environment was achieved. Simulation results show that the average energy consumption of information query by using proposed method is 64.2 nJ/bit. Meanwhile, the query delay is short.