Industrial Control Systems Security - Best Practices: Investigating best practices for securing industrial control systems (ICS) and supervisory control and data acquisition (SCADA) systems from cyber threats
Keywords:
Industrial Control Systems, SCADAAbstract
Industrial Control Systems (ICS) and Supervisory Control and Data Acquisition (SCADA) systems are critical components of modern industrial infrastructure, controlling processes in sectors such as energy, water, transportation, and manufacturing. However, these systems are increasingly targeted by cyber threats, posing risks to operational safety, reliability, and confidentiality. This paper investigates best practices for securing ICS and SCADA systems, focusing on preventive, detective, and corrective measures to mitigate cyber risks. We analyze key security challenges, such as legacy system vulnerabilities, insider threats, and the convergence of IT and OT networks, and propose a comprehensive security framework based on industry standards and guidelines. The framework includes strategies for risk assessment, network segmentation, access control, incident response, and security awareness training. Case studies and real-world examples illustrate the application of these best practices, highlighting the importance of a proactive and layered approach to ICS security. By implementing these recommendations, organizations can enhance the resilience of their industrial control systems against cyber threats.
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Prabhod, Kummaragunta Joel. "ANALYZING THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING TECHNIQUES IN IMPROVING PRODUCTION SYSTEMS." Science, Technology and Development 10.7 (2021): 698-707.
Sadhu, Amith Kumar Reddy, and Ashok Kumar Reddy Sadhu. "Fortifying the Frontier: A Critical Examination of Best Practices, Emerging Trends, and Access Management Paradigms in Securing the Expanding Internet of Things (IoT) Network." Journal of Science & Technology 1.1 (2020): 171-195.
Machireddy, Jeshwanth Reddy. "Revolutionizing Claims Processing in the Healthcare Industry: The Expanding Role of Automation and AI." Hong Kong Journal of AI and Medicine 2.1 (2022): 10-36.
Tatineni, Sumanth, and Karthik Allam. "Implementing AI-Enhanced Continuous Testing in DevOps Pipelines: Strategies for Automated Test Generation, Execution, and Analysis." Blockchain Technology and Distributed Systems 2.1 (2022): 46-81.
Pulimamidi, Rahul. "Emerging Technological Trends for Enhancing Healthcare Access in Remote Areas." Journal of Science & Technology 2.4 (2021): 53-62.
Perumalsamy, Jegatheeswari, Chandrashekar Althati, and Lavanya Shanmugam. "Advanced AI and Machine Learning Techniques for Predictive Analytics in Annuity Products: Enhancing Risk Assessment and Pricing Accuracy." Journal of Artificial Intelligence Research 2.2 (2022): 51-82.
Devan, Munivel, Lavanya Shanmugam, and Chandrashekar Althati. "Overcoming Data Migration Challenges to Cloud Using AI and Machine Learning: Techniques, Tools, and Best Practices." Australian Journal of Machine Learning Research & Applications 1.2 (2021): 1-39.
Althati, Chandrashekar, Bhavani Krothapalli, and Bhargav Kumar Konidena. "Machine Learning Solutions for Data Migration to Cloud: Addressing Complexity, Security, and Performance." Australian Journal of Machine Learning Research & Applications 1.2 (2021): 38-79.
Sadhu, Ashok Kumar Reddy, and Amith Kumar Reddy. "A Comparative Analysis of Lightweight Cryptographic Protocols for Enhanced Communication Security in Resource-Constrained Internet of Things (IoT) Environments." African Journal of Artificial Intelligence and Sustainable Development 2.2 (2022): 121-142.
Tatineni, Sumanth, and Venkat Raviteja Boppana. "AI-Powered DevOps and MLOps Frameworks: Enhancing Collaboration, Automation, and Scalability in Machine Learning Pipelines." Journal of Artificial Intelligence Research and Applications 1.2 (2021): 58-88.
Pelluru, Karthik. "Enhancing Security and Privacy Measures in Cloud Environments." Journal of Engineering and Technology 4.2 (2022): 1-7.
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