Abstract

Artificial Intelligence (AI) and Machine Learning (ML) stands as a robust technology empowering cybersecurity team with the automation of repetitive tasks, expediting threat detection and response, and enhancing the precision of actions to fortify security against a myriad of threats and cyberattacks. This article conducts a meticulous systematic literature review, analyzing the diverse use cases of AI in cybersecurity. Out of 2395 studies examined, 236 were identified as primary sources. Employing a thematic analysis approach and aligning with the NIST cybersecurity framework, this article categorizes the identified AI use cases. The resulting classification framework offers readers a comprehensive understanding of AI's potential to enhance cybersecurity across various contexts. The review also unveils future research prospects in burgeoning cybersecurity application domains, advanced AI methodologies, data representation techniques, and the establishment of novel infrastructures, pivotal for the successful integration of AI-based cybersecurity in the contemporary landscape marked by digital transformation and polycrisis. This research delves into the domain of cybersecurity within electronic communication systems, employing advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques. The study aims to present comprehensive design concepts for the implementation of robust cybersecurity measures. With the ever-growing complexity and sophistication of cyber threats, the integration of AI and ML becomes imperative to fortify electronic communication systems against potential risks. The research explores innovative approaches, strategies, and technologies, offering a holistic view of how AI and ML can be effectively harnessed to enhance cybersecurity in electronic communication systems. The outcomes of this study provide valuable insights for the development and implementation of advanced cybersecurity measures in the realm of electronic communications.