PWG-IDS: An Intrusion Detection Model for Solving Class Imbalance in IIoT Networks Using Generative Adversarial Networks

Zhang, Lei; Jiang, Shuaimin; Shen, Xiajiong; Gupta, Brij B.; Tian, Zhihong

doi:10.48550/arxiv.2110.03445

Cited by 7 publications

(7 citation statements)

References 48 publications

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“…Figure 1 depicts a multi-tier IIoT structure in progress. The main three layers that make up the IIoT network topology are edge, fog, and cloud [14]. This manuscript focuses on the attacks on edge devices, which are essential components of the IIoT network.…”

Section: Security Issues Of Iiot Networkmentioning

confidence: 99%

An Efficient Security Model for Industrial Internet of Things (IIoT) System Based on Machine Learning Principles

Qaddoori¹,

Ali²

2023

(AREJ)

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This paper presents a security paradigm for edge devices to defend against various internal and external threats. The first section of the manuscript proposes employing machine learning models to identify MQTT-based (Message Queue Telemetry Transport) attacks using the Intrusion Detection and Prevention System (IDPS) for edge nodes. Because the Machine Learning (ML) model cannot be trained directly on lowperformance platforms (such as edge devices),a new methodology for updating ML models is proposed to provide a tradeoff between the model performance and the computational complexity. The proposed methodology involves training the model on a high-performance computing platform and then installing the trained model as a detection engine on low-performance platforms (such as the edge node of the edge layer)to identify new attacks. Multiple security techniques have been employed in the second half of the manuscript to verify that the exchanged trained model and the exchanged data files are valid and undiscoverable (information authenticity and privacy) and that the source (such as a fog node or edge device) is indeed what it it claimed to be (source authentication and message integrity). Finally, the proposed security paradigm is found to be effective against various internal and external threats and can be applied to a low-cost singleboard computer (SBC).

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Section: Security Issues Of Iiot Networkmentioning

confidence: 99%

An Efficient Security Model for Industrial Internet of Things (IIoT) System Based on Machine Learning Principles

Qaddoori¹,

Ali²

2023

(AREJ)

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“…Many related works have been proposed in the literature. Hence, Zhang et al [47] proposed an IDS pretraining Wasserstein generative adversarial NIDS. They used LightGBM to double train the proposed model for detecting intrusions in IIoT networks and Wasserstein's generative adversarial network with gradient penalty.…”

Section: Literature Surveymentioning

confidence: 99%

An Ensemble Learning Based Intrusion Detection Model for Industrial IoT Security

Mohy-eddine

Guezzaz

Benkirane

et al. 2023

Big Data Min. Anal.

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Industrial Internet of Things (IIoT) represents the expansion of the Internet of Things (IoT) in industrial sectors. It is designed to implicate embedded technologies in manufacturing fields to enhance their operations.However, IIoT involves some security vulnerabilities that are more damaging than those of IoT. Accordingly, Intrusion Detection Systems (IDSs) have been developed to forestall inevitable harmful intrusions. IDSs survey the environment to identify intrusions in real time. This study designs an intrusion detection model exploiting feature engineering and machine learning for IIoT security. We combine Isolation Forest (IF) with Pearson's Correlation Coefficient (PCC) to reduce computational cost and prediction time. IF is exploited to detect and remove outliers from datasets. We apply PCC to choose the most appropriate features. PCC and IF are applied exchangeably (PCCIF and IFPCC). The Random Forest (RF) classifier is implemented to enhance IDS performances. For evaluation, we use the Bot-IoT and NF-UNSW-NB15-v2 datasets. RF-PCCIF and RF-IFPCC show noteworthy results with 99.98% and 99.99% Accuracy (ACC) and 6.18 s and 6.25 s prediction time on Bot-IoT, respectively. The two models also score 99.30% and 99.18% ACC and 6.71 s and 6.87 s prediction time on NF-UNSW-NB15-v2, respectively. Results prove that our designed model has several advantages and higher performance than related models.

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“…In the end, the performance evaluation specifies that their LDA-EPP scheme has lower computation and communication costs. Zhang et al 40 presented an intrusion detection model to solve the issue of class imbalance in the IIoT system using GAN. They introduced the pretraining model in the Wasserstein GAN with gradient penalty (WGAN-GP) initially.…”

Section: Industrial Internet Of Thingsmentioning

confidence: 99%

Adversarial learning techniques for security and privacy preservation: A comprehensive review

Hathaliya

Tanwar

Sharma

2022

Security and Privacy

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In recent years, the use of smart devices has increased exponentially, resulting in massive amounts of data. To handle this data, effective data storage and management has required. Cloud computing (CC) is a promising solution to deal with this huge amount of data. Electronic devices are collecting real‐time data from sensors and applications through a wireless communication channel in the digital era. In some cases, CC cannot protect against various malicious attacks in the wireless communication channel. To address this issue, we have used machine learning (ML) and deep learning (DL) techniques for attack detection in a wireless channel on an early basis. It trains a model to predict malicious activities of attackers, which aids in the security of CC's sensitive data. We employed adversarial learning techniques (AL) to add fake data into the model to ensure that the trained model was correct. The trained model can distinguish between the fake and real data from the training samples and improve the training samples' performance. AL provides different defense mechanisms to preserve the privacy of ML‐ and DL‐based model but does not ensure the system's robustness. To improve the system's robustness, we have used federated learning with blockchain technology to make a system more robust, reliable, accurate, and transparent. This integration aids in providing high‐graded security against adversarial attacks. This paper presents a comprehensive review to highlight the recent improvements in AL techniques. Moreover, we explored the various AL applications in security and privacy preservation. Finally, open research issues and future directions are discussed to show future research avenues.

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PWG-IDS: An Intrusion Detection Model for Solving Class Imbalance in IIoT Networks Using Generative Adversarial Networks

Cited by 7 publications

References 48 publications

An Efficient Security Model for Industrial Internet of Things (IIoT) System Based on Machine Learning Principles

An Efficient Security Model for Industrial Internet of Things (IIoT) System Based on Machine Learning Principles

An Ensemble Learning Based Intrusion Detection Model for Industrial IoT Security

Adversarial learning techniques for security and privacy preservation: A comprehensive review

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