Ai-Driven Strategies for Securing Internet of Battlefield Things(IoBT)

<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The Internet of Battlefield Things (IoBT) represents a significant advancement in modern warfare, leveraging the power of interconnected devices, sensors, and autonomous systems to enhance situational awareness, decision-making, and operational efficiency. However, the increasing complexity and scale of IoBT also introduce significant cyber security challenges, including vulnerabilities to cyber attacks such as Distributed Denial of Service (DDoS), data tampering, and adversarial artificial intelligence (AI). This paper explores AI-driven strategies for securing IoBT ecosystems, focusing on leveraging machine learning algorithms, real-time threat detection, and autonomous response systems to mitigate cyber risks. It examines the role of AI in enhancing encryption protocols, identifying anomalous behaviors across battlefield networks, and responding to threats autonomously with minimal human intervention. Additionally, the paper proposes adaptive security frameworks that utilize AI to learn from evolving threats and protect sensitive battlefield information. Through a detailed analysis of current cybersecurity challenges and AI applications, this paper aims to contribute to the development of resilient, intelligent defense mechanisms capable of securing future IoBT deployments in dynamic and hostile environments.