Abstract
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.