Advanced Detection Techniques to neutralize Supply Chain Cyber-attacks on small and medium businesses in the United States: The Machine Learning and AI Approach.

Abstract

Cyber-attacks targeting supply chains represent a significant and increasing risk for small and medium-sized businesses (SMBs) in the United States. This paper examines the role of machine learning (ML) and artificial intelligence (AI) in detecting and mitigating these cybersecurity threats. Key challenges faced by SMBs include high implementation costs, a shortage of skilled personnel, inadequate data quality, and the complexity of integrating advanced technologies into existing systems. Despite these hurdles, the paper identifies strategic approaches that can empower SMBs to effectively leverage ML and AI, including affordable solutions, targeted training, and improved data management practices. It emphasizes the importance of addressing privacy and usability concerns to facilitate successful technology adoption. Recommendations include pursuing cost-effective, subscription-based AI and ML models, utilizing government incentives, and enhancing workforce skills through training and partnerships. Additionally, collaboration with larger organizations can improve data management and system efficacy. By implementing robust protective measures and increasing awareness through educational initiatives, SMBs can strengthen their cybersecurity posture and better navigate the evolving landscape of cyber threats.