Journal of Advances in Developmental Research

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Adversarial AI and Cyber–Physical System Resilience: Protecting Critical

Author(s) Ashwin Sharma, Deepak Kejriwal, Anil Kumar Pakina
Country India
Abstract AI technology together with CPS systems face operational gaps because different industries are utilizing these systems in greater numbers. The study explores CPS security issues from the pairing between adversarial AI and CPS through its analysis of automated essential system management in healthcare facilities and transportation systems and power stations. Research document analysis coupled with field assessments allows the author to properly identify adversary threats to CPS systems while showing the importance of creating protective mechanisms for system defense.
The initial section of this work introduces fundamental adversarial AI principles accompanied by an explanation of how cyber criminals exploit accessible AI algorithm vulnerabilities to manipulate systems during operation and produce wrong outcomes. This document illustrates both data poisoning attacks together with model evasion tactics with the purpose of showing stakeholders why they need to boost their knowledge about CPS implementation. The section describes the negative influence of automated system attacks on both public safety and operational efficiency as well as user trust in automated systems. The findings prove that organizations need to understand enemy domains because this intelligence helps establish defensive barriers against attacks.
This final part introduces an incremental defense approach for CPS security through combined implementation of adversarial training techniques with robust algorithm development practices and continuous real-time systems watch. The paper advocates for research collaboration between engineers and policy makers and artificial intelligence programmers and cybersecurity researchers to create useful guidelines for both end users and government policy makers. The paper shows that effective initiatives to counter adversarial AI risks should protect vital systems with a dual objective of avoiding present threats and preventing new attack methods.
Keywords Adversarial AI, Cyber-Physical Systems, CPS Resilience, Critical Infrastructure, Cybersecurity, AI Vulnerabilities, Data Poisoning, Model Evasion, Automated Systems, Public Safety, Operational Efficiency, Trust In Technology, Malicious Actors, Attack Vectors, Defensive Strategies, Adversarial Training, Robust Algorithms, Real-Time Monitoring, Cross-Disciplinary Expertise, Engineering, Policy, Risk Assessment, Threat Landscape, System Manipulation, Safety Protocols, Security Frameworks, Proactive Measures, Incident Response, Mitigation Strategies, Technological Resilience, Cybersecurity Policies
Field Engineering
Published In Volume 14, Issue 2, July-December 2023
Published On 2023-07-06
Cite This Adversarial AI and Cyber–Physical System Resilience: Protecting Critical - Ashwin Sharma, Deepak Kejriwal, Anil Kumar Pakina - IJAIDR Volume 14, Issue 2, July-December 2023. DOI 10.71097/IJAIDR.v14.i2.1376
DOI https://doi.org/10.71097/IJAIDR.v14.i2.1376
Short DOI https://doi.org/g9f7mq
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