基于联邦学习和自注意力的工业物联网入侵检测

doi:10.13229/j.cnki.jdxbgxb.20221027

Abstract

Abstract:

Aiming at the problems of fixed network topology， low dimensionality， uneven data distribution and low correlation， the training effect of intrusion detection model in industrial distributed environment is poor. In this paper， Fedformer， a federated deep learning algorithm based intrusion detection model for industrial Internet of Things （IOT）， is proposed. Firstly， the encoder structure of Transformer network model is introduced and improved， and the convolutional neural network and gated cyclic unit are integrated， and the intrusion detection model for industrial IOT is constructed by using the attention mechanism. Secondly， the detection model is integrated with the federated learning framework， which allows multiple industrial IOT to jointly build a comprehensive intrusion detection model. Under the premise of protecting the privacy of local data， the detection accuracy of industrial IOT network attacks is improved and the false positive rate is reduced. Experimental results show that the detection accuracy of Fedformer in the industrial network environment is 98.09%， and the false positive rate is reduced to 8.31%.

Key words: computer application, industrial internet of things, intrusion detection, federated deep learning, self-attention

CLC Number:

TP399

Jun WANG,Hua-lin WANG,Bo-wen HUANG,Qiang FU,Jun LIU. Intrusion detection for industrial internet of things based on federated learning and self-attention[J].Journal of Jilin University(Engineering and Technology Edition), 2023, 53(11): 3229-3237.

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实际情况入侵	入侵	正常
入侵	TP	FP
正常	FN	TN

标签	训练集		测试集
标签	数量	占比/%	数量	占比/%
Normal	56 000	31.94	37 000	44.94
Exploits	33 393	19.04	11 132	13.51
Dos	12 264	6.99	4 089	4.97
Backdoor	1 746	1.00	583	0.71
Analysis	2 000	1.14	677	0.82
Fuzzers	18 184	10.37	6 062	7.36
Generic	40 000	22.81	18 871	22.92
Reconnaissance	10 492	5.98	3 496	4.25
Shellcode	1 133	0.65	378	0.46
Worms	130	0.07	44	0.05

攻击名称	缩写	数量
正常	Normal （0）	61 156
Na?ve恶意响应注入	NMRI （1）	2 763
复杂恶意响应注入	CMRI （2）	15 466
恶意状态命令注入	MSCI （3）	782
恶意参数命令注入	MPCI （4）	7 637
恶意函数代码注入	MFCI （5）	573
拒绝服务	DOS （6）	1 837
侦察攻击	Recon （7）	6 805

模型	文献［19］			文献［20］			Fedformer
模型	Precision	Recall	F₁	Precision	Recall	F₁	Precision	Recall	F₁
Normal （0）	0.9471	0.9796	0.9630	0.8245	0.8129	0.8169	0.9503	0.9826	0.9655
NMRI （1）	1.0000	0.9276	0.9370	1.0000	0.0797	0.1477	1.0000	0.9325	0.9629
CMRI （2）	0.9349	0.9809	0.9573	0.5691	1.0000	0.7297	0.9457	0.9821	0.9635
MSCI （3）	0.9302	0.9524	0.9412	0.0000	0.0000	0.0000	0.9714	0.9577	0.9645
MPCI （4）	0.9684	0.9787	0.9761	0.0000	0.0000	0.0000	0.9762	0.9811	0.9787
MFCI （5）	1.0000	1.0000	1.0000	1.0000	0.9677	0.9836	0.9612	0.9020	0.9340
DOS （6）	1.0000	0.9619	0.9806	0.9901	0.9804	0.9890	0.9896	0.9223	0.9810
Recon （7）	0.9942	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000

指标	文献［18］	文献［19］	文献［21］	文献［20］	Fedformer
Precision	0.9348	0.9174	0.973	0.9736	0.979
Recall	0.9219	0.9235	0.969	0.9718	0.973
F₁	0.9201	0.9193	0.967	0.9675	0.968

Intrusion detection for industrial internet of things based on federated learning and self-attention

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