Unknown Attack Traffic Classification in SCADA Network Using Heuristic Clustering Technique

Sheng, Chuan; Yao, Yu; Li, Wenxuan; Wei, Yang; Ying, Liu

doi:10.1109/tnsm.2023.3238402

Cited by 8 publications

(2 citation statements)

References 38 publications

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“…Sarhan, M. suggested a zero-sample learning technique to evaluate the performance of machine learning-based detection systems against unknown threats, providing valuable insights into their abilities to identify and mitigate such threats [26]. Sheng, C. devised a self-growing attack traffic classification system based on density-based heuristic clustering to improve the detection of unknown forms of attacks, enabling real-time automated detection [27]. Hairab, B.I.…”

Section: Related Workmentioning

confidence: 99%

An Effective Method for Detecting Unknown Types of Attacks Based on Log-Cosh Variational Autoencoder

Yu,

Xu,

Jiang

2023

Applied Sciences

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The increasing prevalence of unknown-type attacks on the Internet highlights the importance of developing efficient intrusion detection systems. While machine learning-based techniques can detect unknown types of attacks, the need for innovative approaches becomes evident, as traditional methods may not be sufficient. In this research, we propose a deep learning-based solution called the log-cosh variational autoencoder (LVAE) to address this challenge. The LVAE inherits the strong modeling abilities of the variational autoencoder (VAE), enabling it to understand complex data distributions and generate reconstructed data. To better simulate discrete features of real attacks and generate unknown types of attacks, we introduce an effective reconstruction loss term utilizing the logarithmic hyperbolic cosine (log-cosh) function in the LVAE. Compared to conventional VAEs, the LVAE shows promising potential in generating data that closely resemble unknown attacks, which is a critical capability for improving the detection rate of unknown attacks. In order to classify the generated unknown data, we employed eight feature extraction and classification techniques. Numerous experiments were conducted using the latest CICIDS2017 dataset, training with varying amounts of real and unknown-type attacks. Our optimal experimental results surpassed several state-of-the-art techniques, achieving accuracy and average F1 scores of 99.89% and 99.83%, respectively. The suggested LVAE strategy also demonstrated outstanding performance in generating unknown attack data. Overall, our work establishes a solid foundation for accurately and efficiently identifying unknown types of attacks, contributing to the advancement of intrusion detection techniques.

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Section: Related Workmentioning

confidence: 99%

An Effective Method for Detecting Unknown Types of Attacks Based on Log-Cosh Variational Autoencoder

Yu,

Xu,

Jiang

2023

Applied Sciences

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“…Water pressure in pipes, water storage levels, and water distribution are all things that may be managed and controlled using SCADA systems, which are employed by state and municipal governments. In a SCADA system, it is typical to have computing workstations, a HMI, PLCs, sensors, and actuators [8][9][10][11]. In the past, these sorts of systems were dependent on solitary and specialized network configurations.…”

Section: Introductionmentioning

confidence: 99%

Design of Efficient Based Artificial Intelligence Approaches for Sustainable of Cyber Security in Smart Industrial Control System

Alzahrani

Aldhyani

2023

Sustainability

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Online food security and industrial environments and sustainability-related industries are highly confidential and in urgent need for network traffic analysis to attain proper security information to avoid attacks from anywhere in the world. The integration of cutting-edge technology such as the Internet of things (IoT) has resulted in a gradual increase in the number of vulnerabilities that may be exploited in supervisory control and data acquisition (SCADA) systems. In this research, we present a network intrusion detection system for SCADA networks that is based on deep learning. The goal of this system is to defend ICSs against network-based assaults that are both conventional and SCADA-specific. An empirical evaluation of a number of classification techniques including k-nearest neighbors (KNN), linear discriminant analysis (LDA), random forest (RF), convolution neural network (CNN), and integrated gated recurrent unit (GRU) is reported in this paper. The suggested algorithms were tested on a genuine industrial control system (SCADA), which was known as the WUSTL-IIoT-2018 and WUSTL-IIoT-20121 datasets. SCADA system operators are now able to augment proposed machine learning and deep learning models with site-specific network attack traces as a result of our invention of a re-training method to handle previously unforeseen instances of network attacks. The empirical results, using realistic SCADA traffic datasets, show that the proposed machine learning and deep-learning-based approach is well-suited for network intrusion detection in SCADA systems, achieving high detection accuracy and providing the capability to handle newly emerging threats. The accuracy performance attained by the KNN and RF algorithms was superior and achieved a near-perfect score of 99.99%, whereas the CNN-GRU model scored an accuracy of 99.98% using WUSTL-IIoT-2018. The Rf and GRU algorithms achieved >99.75% using the WUSTL-IIoT-20121 dataset. In addition, a statistical analysis method was developed in order to anticipate the error that exists between the target values and the prediction values. According to the findings of the statistical analysis, the KNN, RF, and CNN-GRU approaches were successful in achieving an R2 > 99%. This was demonstrated by the fact that the approach was able to handle previously unknown threats in the industrial control systems (ICSs) environment.

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A Robust Deep Learning-based Approach for Network Traffic Classification using CNNs and RNNs

Jenefa

Sam

Nair

et al. 2023

2023 4th International Conference on Signal Processing and Communication (ICSPC)

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Unknown Attack Traffic Classification in SCADA Network Using Heuristic Clustering Technique

Cited by 8 publications

References 38 publications

An Effective Method for Detecting Unknown Types of Attacks Based on Log-Cosh Variational Autoencoder

An Effective Method for Detecting Unknown Types of Attacks Based on Log-Cosh Variational Autoencoder

Design of Efficient Based Artificial Intelligence Approaches for Sustainable of Cyber Security in Smart Industrial Control System

A Robust Deep Learning-based Approach for Network Traffic Classification using CNNs and RNNs

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