Industrial Control Systems for Cyber-Security Networks in Data Science
Keywords:
Industrial control systems, threats, security, machine learning, Artificial Intelligence risk assessmentAbstract
Data science is the driving force behind the significant technological and operational changes that cyber security is going through in the computer world. Identifying trends or insights regarding security occurrences in cyber security data and creating a data-driven model that correlates with them are essential elements in creating an automated and intelligent safety system. This means gathering data from pertinent cyber security sources and applying analytics to improve the most recent trends based on data. The article also highlights important variables that affect the design choices made for the control, communication, redundancy, and reliability of ICS, as these aspects are crucial in figuring out the security requirements of the system. Network segmentation, access control, patches management, and security monitoring are just a few of the security countermeasures that are currently in place. Additionally, the paper investigates how machine learning methods might be integrated to improve ICS cyber security. Subsequently, we list the pros and cons of the available security solutions, talk about how to secure industrial control systems (ICSs) and implement additional security measures (like risk assessment methodologies), point out unresolved security research issues related to ICSs, and make recommendations for future directions in ICS security research.
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