Machine Learning for Human–Machine Systems With Advanced Persistent Threats
Long Chen, Wei Zhang, Yanqing Song, Jianguo Chen · IEEE Transactions on Human-Machine Systems · 2024
This article conducts a thorough exploration of the implications of machine learning (ML) in conjunction with human–machine systems within the military domain. It scrutinizes the strategic development efforts of ML by pertinent institutions, particularly in the context of military applications and the domain of advanced persistent threats. Prominent nations have delineated a technical trajectory for the integration of ML into their military frameworks. To bolster the structure and efficacy of their various military branches and units, there has been a concentrated deployment of numerous ML research endeavors. These initiatives encompass the study of sophisticated ML algorithms and the acceleration of artificial intelligence technology adaptation for intelligence processing, autonomous platforms, command and control infrastructures, and weapons systems. Forces across the globe are actively embedding ML technologies into a range of platforms-terrestrial, naval, aerial, space-faring, and cybernetic. This integration spans weaponry, networks, cognitive operations, and additional systems. Furthermore, this article reviews the incorporation within the sphere of military human–machine interaction in the Russia–Ukraine conflict. In this war, cyber human–machine interaction has become a pivotal arena of contention between Russia and Ukraine, with key levers that influence the conflict's course. In addition, the article examines the adoption of ML in prospective military functions such as, operations, intelligence gathering, networking, logistics, identification protocols, healthcare, data analysis trends, and other critical areas marked by current developments and trajectories. It also proffers a series of recommendations for the future integration of ML to inform strategic direction and research.