Al-Based Signal Intelligence for Real-Time Threat Detection

Abstract

Technology in AI and signal processing has changed signal intelligence (SIGINT) in recent years. This study examines AI-based Signal Intelligence (AI-SIGINT) systems for real-time threat detection in military, cyber security, and critical infrastructure protection. AI-SIGINT uses cutting-edge machine learning (ML) and deep learning (DL) algorithms to evaluate massive volumes of signal data from radio frequency (RF), satellite, and mobile networks to detect and neutralize threats in real time. AI-SIGINT systems autonomously monitor, intercept, and decode signal communications to quickly identify aberrant patterns that may indicate hostile activity or impending threats. A key component of AI-based signal intelligence is adaptive danger detection. Using reinforcement learning (RL) and anomaly detection, the system continuously evolves to improve threat perception. This adaptability detects sophisticated, changing threats like jamming attempts, frequency hopping, and cyber intrusions. This research also examines AI-driven SIGINT's ethical issues, including data privacy and unlawful surveillance. It also addresses technology issues like merging AI algorithms with SIGINT infrastructure and the necessity for high computational resources.