基于ANN与KPCA的LDoS攻击检测方法

doi:10.11959/j.issn.1000-436x.2018073

Abstract

Abstract:

Low-rate denial-of-service (LDoS) attack is a new type of attack mode for TCP protocol.Characteristics of low average rate and strong concealment make it difficult for detection by traditional DoS detecting methods.According to characteristics of LDoS attacks,a new LDoS queue future was proposed from the router queue,the kernel principal component analysis (KPCA) method was combined with neural network,and a new method was present to detect LDoS attacks.The method reduced the dimensionality of queue feature via KPCA algorithm and made the reduced dimension data as the inputs of neural network.For the good sell-learning ability,BP neural network could generate a great LDoS attack classifier and this classifier was used to detect the attack.Experiment results show that the proposed approach has the characteristics of effectiveness and low algorithm complexity,which helps the design of high performance router.

Key words: low-rate denial of service, queue feature, kernel principal component analysis, neural network

CLC Number:

TP302

Zhijun WU,Liang LIU,Meng YUE. Detection method of LDoS attacks based on combination of ANN ＆ KPCA[J]. Journal on Communications, 2018, 39(5): 11-22.

Figures/Tables 15

References 13

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阈值η	检测率	虚警概率
0.45	97.21%	8.22%
0.47	96.52%	7.36%
0.49	96.24%	5.71%
0.51	95.62%	3.70%
0.53	95.21%	1.33%
0.55	94.54%	1.15%
0.57	93.25%	1.13%
0.59	92.12%	1.10%
0.61	90.35%	0.91%
0.63	88.21%	0.87%
0.65	85.42%	0.82%

算法	时间复杂度	时间频度/s
NCPSD	O(n²)	24.1
HMM	O(n²)	20.6
Adaptive-KPCA	O(n³)	32.5
KPCA网络	Tn)₀(	5.1

算法	输入数据量	空间复杂度
NCPSD	n	O(n)
HMM	n	O(n)
Adaptive-KPCA	n	O(n)
KPCA网络	n6	O(n)

算法	检测率	漏警概率	虚警概率
NCPSD	88.0%	12.0%	16.7%
HMM	99.9%	0.04%	1.11%
Adaptive-KPCA	99.2%	0.8%	2.0%
KPCA网络（正常）	95.2%	4.8%	1.3%
KPCA网络（突发）	94.2%	5.8%	2.0%

Detection method of LDoS attacks based on combination of ANN ＆ KPCA

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