Applying artificial intelligence techniques to intrusion detection systems in serial-based industrial networks
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Ralf Luis de Moura
Abstract
Industrial control systems often rely on serial-based networks that lack robust cybersecurity measures, making them vulnerable to attacks as they increasingly integrate with corporate networks. While intrusion detection systems (IDSs) are extensively used in Ethernet-based networks, their adoption in serial-based networks remains limited. This chapter investigates the application of artificial intelligence (AI) techniques to enhance intrusion detection in these networks. By combining rule-based methods with supervised and unsupervised learning, AI-powered IDS can detect known and novel attacks effectively. The chapter reviews AI techniques, compares their effectiveness, and highlights their potential to extend cybersecurity measures to the most vulnerable layers of industrial networks. The findings emphasize the critical role of AI in safeguarding industrial serial-based systems and propose strategies for developing IDS, tailored to their unique requirements, addressing challenges such as legacy system constraints and the need for real-time anomaly detection.
Abstract
Industrial control systems often rely on serial-based networks that lack robust cybersecurity measures, making them vulnerable to attacks as they increasingly integrate with corporate networks. While intrusion detection systems (IDSs) are extensively used in Ethernet-based networks, their adoption in serial-based networks remains limited. This chapter investigates the application of artificial intelligence (AI) techniques to enhance intrusion detection in these networks. By combining rule-based methods with supervised and unsupervised learning, AI-powered IDS can detect known and novel attacks effectively. The chapter reviews AI techniques, compares their effectiveness, and highlights their potential to extend cybersecurity measures to the most vulnerable layers of industrial networks. The findings emphasize the critical role of AI in safeguarding industrial serial-based systems and propose strategies for developing IDS, tailored to their unique requirements, addressing challenges such as legacy system constraints and the need for real-time anomaly detection.
Chapters in this book
- Frontmatter I
- Preface V
- Contents IX
- List of contributing authors XI
- Game-based testing for active cyberdefense and cyberdeception 1
- Graph-ensemble methods for generating malware behavioral signatures 31
- Efficient cyber threat detection on SCADA systems using feature-grouped generative adversarial networks 57
- Applying artificial intelligence techniques to intrusion detection systems in serial-based industrial networks 71
- A hybrid intelligent intrusion detection system 91
- PwnPilot: could an adversary be pair programming with our most trusted software engineers? 109
- How to attack a far galaxy and beyond 145
- Injecting uniform chaotic sequences into an ANN’s learning fabric to reduce overfitting 179
- Effectiveness of machine learning and deep learning in cybersecurity 199
- Quantum-enhanced cyber threat detection with mini-batch optimization 215
- Future of auditable AI systems 231
- Virtual cybersecurity testbeds for industrial Internet of Things 255
- Security verification of authenticated encryption with associated data under chosen message attack assumption using Tamarin prover 281
- Security and privacy challenges in Internet of Medical Things (IoMT) using RFID and sensor nodes 319
- Empowering users with an effective tool for social media spam detection 333
- Comparative analysis of email digital forensics tools validation 355
- An implementation of a web platform for training in phishing attack detection using cognitive security, cognitive psychology, and game theory 383
- Index 411
- De Gruyter Series in Intelligent Computing
Chapters in this book
- Frontmatter I
- Preface V
- Contents IX
- List of contributing authors XI
- Game-based testing for active cyberdefense and cyberdeception 1
- Graph-ensemble methods for generating malware behavioral signatures 31
- Efficient cyber threat detection on SCADA systems using feature-grouped generative adversarial networks 57
- Applying artificial intelligence techniques to intrusion detection systems in serial-based industrial networks 71
- A hybrid intelligent intrusion detection system 91
- PwnPilot: could an adversary be pair programming with our most trusted software engineers? 109
- How to attack a far galaxy and beyond 145
- Injecting uniform chaotic sequences into an ANN’s learning fabric to reduce overfitting 179
- Effectiveness of machine learning and deep learning in cybersecurity 199
- Quantum-enhanced cyber threat detection with mini-batch optimization 215
- Future of auditable AI systems 231
- Virtual cybersecurity testbeds for industrial Internet of Things 255
- Security verification of authenticated encryption with associated data under chosen message attack assumption using Tamarin prover 281
- Security and privacy challenges in Internet of Medical Things (IoMT) using RFID and sensor nodes 319
- Empowering users with an effective tool for social media spam detection 333
- Comparative analysis of email digital forensics tools validation 355
- An implementation of a web platform for training in phishing attack detection using cognitive security, cognitive psychology, and game theory 383
- Index 411
- De Gruyter Series in Intelligent Computing
