Nearest Neighbor Intrusion Detection Method Based on Perceived Hash Matrix

被引：0

作者：

Jiang Z.-T. ^{[1
]}

Zhou T.-S.-Z. ^{[1
]}

Han L.-Y. ^{[2
]}

机构：

[1] College of Computer and Information Security, Guilin University of Electronic Technology, Guilin, 541004, Guangxi

[2] College of Computer Science and Technology, Northwestern Polytechnical University, Xi'an, 710129, Shaanxi

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2019年 / 47卷 / 07期

关键词：

Detection rate; Intrusion detection; KNN; Perceptual Hash matrix; Quantization function;

D O I：

10.3969/j.issn.0372-2112.2019.07.019

中图分类号：

学科分类号：

摘要：

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. © 2019, Chinese Institute of Electronics. All right reserved.

引用

页码：1538 / 1546

页数：8

共 11 条

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