The Role of Artificial Intelligence in Boosting Cybersecurity and Trusted Embedded Systems Performance: A Systematic Review on Current and Future Trends

Abstract

As technology becomes increasingly interconnected, ensuring the security of cyber and embedded systems is critical due to escalating vulnerabilities and sophisticated cyber threats. Researchers are exploring artificial intelligence (AI) to improve security mechanisms, yet there is a lack of a comprehensive technical, AI-focused analysis detailing the integration of AI into existing security hardware and frameworks. To address this gap, this article systematically reviews 63 articles on AI in cybersecurity and trusted embedded systems. The reviewed articles are categorized into four application domains: 1) Intrusion Detection and Prevention (IDPS), 2) Malware Detection, 3) Industrial Control and Cyber-Physical Systems (CPS) and 4) Distributed Denial-of-Service (DDoS) Detection and Prevention. We investigated current trends in integrating AI into security domains by summarizing the hardware used, the AI methodologies adopted, and the statistical distribution by publication year and region. The key findings of our review indicate that AI significantly enhances security measures by enabling capabilities such as detection, classification, feature selection, data privacy preservation, model combination, data generation, output interpretation, optimization, and adaptation. In addition, the benefits and challenges identified in these studies provide insight into the future potential of AI integration in security. Suggested directions for future work include improving generalization and scalability, exploring continuous or real-time monitoring, and improving AI model performance. This analysis serves as a foundation for advancing AI applications in the effective securing of cyber and embedded systems effectively.