Exploring the Role of Deep Learning in Enhancing Accuracy and Efficiency of Threat Detection Systems

Abstract

Deep learning has emerged as a transformative technology in enhancing the accuracy and efficiency of threat detection systems across various domains, including cybersecurity, surveillance, and critical infrastructure protection. By leveraging advanced architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models, deep learning enables real-time processing and analysis of vast and complex datasets. This paper explores the role of deep learning in modern threat detection systems, focusing on its ability to improve anomaly detection, reduce false positives, and optimize computational resources. Case studies and empirical evaluations are presented to highlight the successful integration of deep learning frameworks in threat detection, with a particular emphasis on applications in malware detection, fraud prevention, and public safety. The challenges, including model interpretability, adversarial robustness, and computational overhead, are discussed, along with emerging trends such as federated learning and explainable AI. The findings demonstrate that deep learning not only enhances the efficacy of threat detection systems but also provides a foundation for next-generation security solutions capable of adapting to evolving threats.