Secure AI for Robust Anomaly Detection in Manufacturing Processes

Project Description

Anomaly detection involves the identification of data and events that stray from the norm, rendering them inconsistent with the broader dataset. This functionality holds immense significance within manufacturing industry, facilitating security monitoring, safety enhancement, quality assurance, downtime reduction, and defect prevention. Embracing AI-driven anomaly detection techniques enables early recognition of irregularities, real-time analysis of extensive datasets, and the identification of intricate patterns and subtle anomalies often overlooked by conventional methods. However, for the opportunities of AI to be fully realised, it must be developed, deployed and operated in a manner that is both secure and responsible. AI systems are vulnerable to new security risks (e.g., poisoning and evasion attacks) that must be addressed in conjunction with traditional cybersecurity threats. The manufacturing industry, a vital pillar of global economies, stands as a prime target for cybercriminals, bearing the brunt of 20% of all cyber extortion attacks in 2023. Rather than diminishing, these cyber threats persist and intensify, driven by the industry's ongoing digital transformation and the widespread adoption of smart manufacturing technologies, including AI-based systems. The development and implementation of AI technologies in the manufacturing sector should consider adequate mitigation actions to reduce the security risks and threats. This project aims to develop an advanced, resilient AI-based model tailored to identify and mitigate data anomalies in the manufacturing industry. Through the integration of Secure AI techniques, the solution will ensure the security and integrity of the system, safeguarding AI models, algorithms, and data from unauthorized access, manipulation, theft, or misuse. By bolstering manufacturers' capacity to anticipate and address deviations, the solution seeks to enhance operational efficiency without compromising the security of their systems and infrastructure.