A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems

Kim, Sangjun; Park, Kyung‐Joon

doi:10.3390/app11125458

Cited by 43 publications

(18 citation statements)

References 141 publications

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“…Alabadi and Celik in [53] presented a comprehensive survey about using CNN as a key solution for anomaly detection. Kim and Park [54] focus the attention on ML in Cyber-Physical Systems (CPS), which is the integration of a physical system into the real world and control applications in a computing system, interacting through a communications network. They suggest a CPS structure that divides the system's functions into three layers: physical, network, and software applications.…”

Section: B Surveys On Artificial Intelligence Applications In Cyberse...mentioning

confidence: 99%

Explainable Artificial Intelligence in CyberSecurity: A Survey

et al. 2022

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Nowadays, Artificial Intelligence (AI) is widely applied in every area of human being's daily life. Despite the AI benefits, its application suffer from the opacity of complex internal mechanisms and doesn't satisfy by design the principles of Explainable Artificial Intelligence (XAI). The lack of transparency further exacerbates the problem in the field of Cybersecurity because entrusting crucial decisions to a system that cannot explain itself presents obvious dangers. There are several methods in the literature capable of providing explainability of AI results. Anyway, the application of XAI in Cybersecurity can be a doubleedged sword. It substantially improves the Cybersecurity practices but simultaneously leaves the system vulnerable to adversary attacks. Therefore, there is a need to analyze the state-of-the-art of XAI methods in Cybersecurity to provide a clear vision for future research. This study presents an in-depth examination of the application of XAI in Cybersecurity. It considers more than 300 papers to comprehensively analyze the main Cybersecurity application fields, like Intrusion Detection Systems,Malware detection, Phishing and Spam detection, BotNets detection, Fraud detection, Zero-Day vulnerabilities, Digital Forensics and Crypto-Jacking . Specifically, this study focuses on the explainability methods adopted or proposed in these fields, pointing out promising works and new challenges.

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Section: B Surveys On Artificial Intelligence Applications In Cyberse...mentioning

confidence: 99%

Explainable Artificial Intelligence in CyberSecurity: A Survey

et al. 2022

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“…Cyber‐physical systems can be empowered and optimized by using big data analytics 67 and machine learning 68 . Data‐driven cyber‐physical approaches that are implemented in cloud‐based or edge computing‐based environments can support the development of personalized and intelligent services and products 69,70 .…”

Section: Cyber‐physical Systemsmentioning

confidence: 99%

Enhancing and securing cyber‐physical systems and Industry 4.0 through digital twins: A critical review

Λαμπρόπουλος

Siakas

2022

J Software Evolu Process

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Due to the fierce competitive global market, enterprises need to face and overcome new challenges and requirements to stay ahead of competition. Cyber-physical systems, Internet of things, and digital twins are some of the contemporary technologies that are used in the context of Industry 4.0 in order to instill intelligence into the industrial sector by changing traditional manufacturing to smart manufacturing. The digitalized and interconnected nature of Industry 4.0 creates new security, privacy, and safety challenges that need to be addressed in order for it to be fully realized.This study presents an overview regarding the use of digital twins as a means to reinforce and secure cyber-physical systems and Industry 4.0, in general. Digital twins connect the physical and virtual worlds and can be used in combination with other technologies to provide several merits in various sectors, such as real-time monitoring and controlling, prompt access to dynamic data, constant visualization and analysis, process optimization, advanced decision-making, and prediction systems. All in all, based on the literature review, digital twins can constitute an essential tool for the realization, reinforcement, and security of cyber-physical systems and Industry 4.0.

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“…Several studies have been conducted to apprehend and mitigate the effects of cyber attacks on CPS using ML [18]- [20]. Nevertheless, there has been less emphasis on AML attacks to ML-based solutions.…”

Section: Related Workmentioning

confidence: 99%

Security of Machine Learning-Based Anomaly Detection in Cyber Physical Systems

Jadidi¹,

Pal²,

Nayak³

et al. 2022

Preprint

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With the emergence of the Internet of Things (IoT) and Artificial Intelligence (AI) services and applications in the Cyber Physical Systems (CPS), the methods of protecting CPS against cyber threats is becoming more and more challenging. Various security solutions are implemented to protect CPS networks from cyber attacks. For instance, Machine Learning (ML) methods have been deployed to automate the process of anomaly detection in CPS environments. The core of ML is deep learning. However, it has been found that deep learning is vulnerable to adversarial attacks. Attackers can launch the attack by applying perturbations to input samples to mislead the model, which results in incorrect predictions and low accuracy. For example, the Fast Gradient Sign Method (FGSM) is a white-box attack that calculates gradient descent oppositely to maximize the loss and generates perturbations by adding the gradient to unpolluted data. In this study, we focus on the impact of adversarial attacks on deep learning-based anomaly detection in CPS networks and implement a mitigation approach against the attack by retraining models using adversarial samples. We use the Bot-IoT and Modbus IoT datasets to represent the two CPS networks. We train deep learning models and generate adversarial samples using these datasets. These datasets are captured from IoT and Industrial IoT (IIoT) networks. They both provide samples of normal and attack activities. The deep learning model trained with these datasets showed high accuracy in detecting attacks. An Artificial Neural Network (ANN) is adopted with one input layer, four intermediate layers, and one output layer. The output layer has two nodes representing the binary classification results. To generate adversarial samples for the experiment, we used a function called the 'fast gradient method' from the Cleverhans library. The experimental result demonstrates the influence of FGSM adversarial samples on the accuracy of the predictions and proves the effectiveness of using the retrained model to defend against adversarial attacks.

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A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems

Cited by 43 publications

References 141 publications

Explainable Artificial Intelligence in CyberSecurity: A Survey

Explainable Artificial Intelligence in CyberSecurity: A Survey

Enhancing and securing cyber‐physical systems and Industry 4.0 through digital twins: A critical review

Security of Machine Learning-Based Anomaly Detection in Cyber Physical Systems

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