基于离散优化算法和机器学习的传感云入侵检测

doi:10.13229/j.cnki.jdxbgxb20181170

Abstract

Abstract:

A sensor cloud intrusion detection algorithm based on parallel discrete optimization feature extraction and machine learning is proposed in the fog computing model to overcome the large-scale, high dimensional data and variable intrusion behavior of sensor cloud. First, on the basis of defining the optimal feature evaluation index, the parallel discrete optimization feature extraction framework is constructed to effectively reduce the data dimensionality and improve the robustness of the feature extraction process. Second, a new discrete optimization algorithm (DOA) is designed, and the DOA implementation process is given according to the characteristics of engineering optimization problem. As DOA is proved to converge to the global optimal solution, the best feature combination is extracted. Finally, sensor cloud intrusion detection is carried out by using the best feature subset and the distributed fuzzy clustering technology. The intelligent iterative evolution method and adaptive clustering strategy are introduced to improve the performance of fuzzy clustering algorithm. Experimental results show that the proposed algorithm can effectively give intrusion detection results. Compared with other detection algorithms, the anomaly detection accuracy and missed detection rate of the proposed algorithm are significantly improved, and it has strong anti-noise ability.

Key words: computer application, fog computing, sensor cloud, discrete optimization algorithm, machine learning, intrusion detection

CLC Number:

TP393

Zhou-zhou LIU,Wen-xiao YIN,Qian-yun ZHANG,Han PENG. Sensor cloud intrusion detection based on discrete optimization algorithm and machine learning[J].Journal of Jilin University(Engineering and Technology Edition), 2020, 50(2): 692-702.

Figures/Tables 7

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Table 1

Comparison of feature subset selection and algorithm operation time"

		AGFCM	PLS-CVM	F-Score	本文
		AGFCM	PLS-CVM	F-Score	$L = 5$	$L = 10$	$L = 15$
m=40	$k$	23	21	17	13	12	14
m=40	$t$ /s	11.38	15.42	8.77	12.46	13.01	11.98
m=80	$k$	41	46	38	21	18	20
m=80	$t$ /s	22.14	30.82	18.37	24.08	25.37	29.14

Table 1

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