ADVANCEMENTS AND CHALLENGES IN DEEP LEARNING FOR CYBER THREAT DETECTION

Abstract

With cyber threats becoming more advanced and widespread, traditional detection methods, such as signature-based and rule-based systems, are proving increasingly inadequate. In response, deep learning has emerged as a transformative approach to cybersecurity, offering greater accuracy and the ability to detect threats in real time. This article explores how advanced techniques—such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and natural language processing (NLP) are enhancing cyber threat detection. Despite its potential, implementing deep learning in cybersecurity comes with challenges, including data quality issues, model transparency, and vulnerabilities to adversarial attacks. The article also examines future directions, such as integrating quantum computing and edge AI, while emphasizing the importance of collaboration and open-source initiatives in driving progress. By providing a comprehensive look at both the advancements and limitations of deep learning in cybersecurity, this article aims to offer valuable insights into its role in building more resilient digital defenses.