Artificial intelligence (AI) is not a fancy term anymore, or not limited to only researchers and academia. AI is currently becoming a part and parcel of our daily life, we are using AI/ intelligent systems by knowing or without knowing. Event product manufacturers are also trying to incorporate AI with their products to make it more preferable to consumers and trying to get the full benefit of using AI in their production and control units even for business decisions. Therefore, In our paper, we give a comprehensive survey of recent advances in Computational intelligence in industrial Control Systems and cover many usages of how industrial Control Systems are getting benefits from using Computational intelligence. We covered multiple domains like Manufacturing,  Energy Management,  Transportation, Food and Beverage Industry, and Pharmaceutical Industry, how these industries are utilizing multiple CI-based control systems like Programmable Logic Controllers, Distributed Control Systems, Supervisory Control and Data Acquisition, Industrial Automation, and Control Systems, Intelligent Electronic Devices and found benefits in their operations and manufacturing which helping them to focus more in innovation and improvement of their products. We believe that this survey shall be valuable to researchers across academia and industry.

The future digitalization of Nuclear power plants (NPP) involves uses of sensors data in digitalize formats and analysis by AI based techniques.  The planned and existing network architectures used by U.S. nuclear facilities, particularly in high-security zones near the reactor cores, typically rely on a private intranet that connects multiple computers in a peer-to-peer (P2P) or similar network configuration. These intranets are considered highly secure due to their isolation from the outside world (Internet) and the implementation of data diodes to regulate one-way data flow. Additionally, these facilities employ various security measures, including physical security, authorized access, pre-screened employees, antivirus software, and supply chain verification. While these “air gap” systems and their security frameworks are effective in protecting against traditional threats like malware, ransomware, trojans, and intrusion detection, they may overlook a growing vulnerability related to AI models within these air gap systems. AI models or decision systems utilized within nuclear facilities collect sensor data through the secure network and make critical decisions. However, despite the network’s robust security measures, the threat to AI models posed by adversarial attacks such as troj-AI, evasion-based attacks, backdoor attacks, and pre-trained poisoned attacks is often ignored by conventional virus scanners. These attacks exploit the vulnerabilities of AI models and can be difficult to detect without domain-specific knowledge related to the model data. As the security of nuclear power plants is paramount, it is crucial that we proactively scan and monitor AI models used in different sectors of these facilities. Unfortunately, there is currently no established framework to monitor and scan the behaviors and architectures of AI models, which poses a significant vulnerability for nuclear power plants. To ensure the comprehensive security of nuclear facilities, it is necessary to address this gap by developing specialized frameworks and mechanisms to monitor and assess the security of AI models. These frameworks should be capable of detecting and mitigating adversarial attacks targeting AI models, providing an additional layer of protection alongside existing security measures. By proactively addressing this emerging threat, we can enhance the overall security posture of nuclear power plants and better safeguard against potential risks.

Transformative developments have been brought in across a number of industries. Digital twin technologies are one of them. This revolutionary innovation has enhanced efficiency, optimized production, and elevated product design to new heights. Nevertheless, as industries embrace the potential of digital twins, cybersecurity concerns come to the forefront due to the convergence of physical and virtual realms. By addressing cybersecurity challenges effectively, industries can fully capitalize on the transformative capabilities of digital twin technology, driving competitiveness and resilience in the face of evolving digital landscapes. Our research explores the various industrial applications of digital twin technology. It also highlights the urgent need for strong cybersecurity measures. Secure data transmission, access control, encryption, and threat detection become crucial elements that must be ensured in order for digital twin systems in the industrial sector.

Many applications of Generative AI (such as DALL-E, GPT-3, ChatGPT, etc.) are making headline news in recent months and have been receiving both praise and criticism for their far reaching implications.  Some of these applications include query responses, language translation, text to images and videos, composing stories, essays, creating arts and music, generating programs, etc. This review provides an historical background of Generative AI techniques and how they evolved over the years. This report highlights the benefits of Generative AI technologies and their limitations/challenges in moving forward. It is also to be noted that the large-scale applications of AI and their successes are now possible due to exponential advances in hardware (computational power, storage capacity), cloud computing and related operational layers of software.