基于安全联邦蒸馏GAN的工业CPS协作入侵检测系统

doi:10.11959/j.issn.1000-436x.2023216

Abstract

Abstract:

Aiming at the data island problem caused by the imperativeness of confidentiality of sensitive information, a secure and collaborative intrusion detection system (CIDS) for industrial cyber physical systems (CPS) was proposed, called PFD-GAN.Specifically, a novel semi-supervised intrusion detection model was firstly developed by improving external classifier-generative adversarial network (EC-GAN) with Wasserstein distance and label condition, to strengthen the classification performance through the use of synthetic data.Furthermore, local differential privacy (LDP) technology was incorporated into the training process of developed EC-GAN to prevent sensitive information leakage and ensure privacy and security in collaboration.Moreover, a decentralized federated distillation (DFD)-based collaboration was designed, allowing multiple industrial CPS to collectively build a comprehensive intrusion detection system (IDS) to recognize the threats under the entire cyber systems without sharing a uniform template model.Experimental evaluation and theory analysis demonstrate that the proposed PFD-GAN is secure from the threats of privacy leaking and highly effective in detecting various types of attacks on industrial CPS.

Key words: intrusion detection system, cyber physical system, generative adversarial network, local differential privacy, decentralized federated distillation

CLC Number:

TP393.08

Junwei LIANG, Geng YANG, Maode MA, Sadiq Muhammad. Secure federated distillation GAN for CIDS in industrial CPS[J]. Journal on Communications, 2023, 44(12): 230-244.

Figures/Tables 13

参数名	参数值
潜在空间纬度dim(z)/维	24
批量采样数B/个	$B = \frac{\| S \|}{T}$
学习率 $α_{φ / ω / θ}$	5×10^-4
裁剪阈值 $C_{ω}, C_{θ}$	0.01
本地迭代次数 $T_{[φ, ω, θ]}$ /次	[50,5,100]
全局迭代次数T /次	$T = T_{φ} = \frac{T_{θ}}{2}$
无监督损失权重λ	0.1
伪标签阈值τ	0.7
LDP噪声尺度(ε, )δ	(6,1×10^-5)

References 24

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IDS模型		Precision	Recall	F1值	FAR	FLOPS/(flop·s^-1)	Latency/ms
不使用LDP技术	CNN	95.62%	94.56%	94.92%	5.41%	89 216	4.29
	EC-GAN	96.55%	95.89%	96.11%	3.93%	89 216	4.20
	Dev EC-GAN	97.13%	96.76%	96.81%	2.20%	89 216	4.26
	Fix PFD-GAN	98.65%	98.62%	98.60%	1.14%	89 216	4.31
	PFD-GAN	99.00%	98.99%	98.97%	0.79%	74 470	3.58
使用LDP技术	CNN	93.76%	92.77%	93.00%	7.85%	89 216	4.36
	EC-GAN	95.63%	94.14%	94.68%	4.57%	89 216	4.34
	Dev EC-GAN	96.41%	95.60%	95.82%	3.03%	89 216	4.38
	Fix PFD-GAN	97.94%	97.75%	97.77%	1.54%	89 216	4.29
	PFD-GAN	98.16%	97.97%	97.99%	1.47%	74 470	3.49

IDS模型	J_normal	Jf_looding	J_{impersonation}	J_injection	FLOPS/(flop·s^-1)	Latency/ms
DL-GAN	96.80%	54.28%	96.39%	95.65%	5.22×10 ⁵	25.12
MD-BiGAN	98.11%	74.35%	82.97%	96.62%	5.14×10 ⁵	24.73
Fed-ANIDS	98.33%	71.55%	80.58%	96.84%	2.51×10 ⁶	120.97
SSFL	98.70%	83.33%	91.21%	96.69%	2.48×10 ⁶	119.40
PFD-GAN	99.30%	87.94%	97.11%	97.02%	4.77×10 ⁵	22.93

IDS解决方案		Precision	Recall	F1值	FAR	Latency/ms
无任何辅助机制的IDS	GHSOM	96.17%	96.11%	95.99%	5.48%	3.40
	Hypothesis Testing	94.28%	94.00%	93.96%	7.15%	0.43
	Dolphine + SVM	95.99%	95.97%	95.87%	5.59%	1.44
	Markov Model	91.33%	93.17%	92.00%	10.40%	1.56
带辅助机制的IDS	GHSOM + MOEA	97.43%	97.03%	97.01%	2.70%	2.03
	t-test + Bayesian	96.46%	96.18%	96.08%	5.42%	0.57
	PFD-GAN w/o LDP	98.65%	98.62%	98.60%	1.14%	3.52
差分隐私保护的联邦IDS	FedAvg	93.20%	92.95%	92.82%	7.96%	5.74
	Fed+	93.49%	93.05%	93.09%	7.68%	5.89
	SecFedNIDS	95.89%	94.82%	95.06%	3.65%	4.28
	PFD-GAN w LDP	97.94%	97.75%	97.77%	1.54%	3.65

Secure federated distillation GAN for CIDS in industrial CPS

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