基于感知哈希矩阵的最近邻入侵检测算法

Nearest Neighbor Intrusion Detection Method Based on Perceived Hash Matrix

针对目前入侵检测效率不高的问题,本文提出一种基于感知哈希矩阵的最近邻入侵检测算法.首先计算训练集中入侵检测对象的感知哈希描述子,并将感知哈希描述子拼接成感知哈希矩阵;然后利用设计好的量化函数对矩阵中的哈希描述子进行量化,并按照感知哈希的性质对矩阵进行约简和调整;在入侵检测阶段用该矩阵快速定位与待检测对象最相近的K个样本,利用K近邻的投票原则完成入侵检测任务.通过理论分析及在KDDCUP99数据集上的相关实验验证了该方法以O(n)的时间复杂度来快速定位最近邻的K个样本,在保持高检测率的同时降低了存储和计算方面的开销,从而更加有效的保护网络环境.

In view of the low efficiency of current intrusion detection,this paper proposes a Nearest Neighbor Intrusion Detection algorithm based on Perceptual Hash Matrix.Firstly,the perceptual Hash descriptors of the intrusion detection object in the training set is calculated,and the perceptual Hash descriptors are spliced into a perceptual Hash matrix;Then use the designed quantization function to quantize the Hash digest in the matrix,and reduce and adjust the matrix according to the nature of the perceived Hash.In the intrusion detection phase,the matrix is used to quickly locate K samples closest to the object to be detected,using K nearest neighbors(KNN)’s voting principles to complete intrusion detection tasks.Theoretical analysis and related experiments on the KDDCUP99 dataset show that the method can quickly locate the nearest neighbor K samples with the O(n ) of time complexity,which can reduce the overhead of storage and calculation while maintaining high detection rate,and more effectively protect the network environment.