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 pr

inciples 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.