Deep Learning in Cybersecurity in the Modern Era - Ali Raza A Khan; Muhammad Ismaeel Khan; Aftab Arif (JURIHUM : Jurnal Inovasi dan Humaniora, 2024)
The integration of deep learning into cybersecurity has marked a transformative shift in the way organizations approach threat detection and mitigation. This review article explores the modern era of deep learning in cybersecurity, detailing its significant advantages over traditional security measures, particularly in enhancing threat detection and response mechanisms. Deep learning models, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), have demonstrated remarkable proficiency in identifying anomalies and adapting to evolving cyber threats, enabling real-time responses that mitigate potential damage. Despite its promise, the implementation of deep learning in cybersecurity faces several challenges, including data privacy concerns, model interpretability issues, adversarial vulnerabilities, and the resource-intensive nature of training these models. The emergence of explainable AI (XAI) aims to enhance trust in automated systems by providing interpretable outputs, while federated learning addresses privacy risks by enabling collaborative training without data centralization. Future directions in this field include advancements in adversarial training techniques, the integration of multi-modal data sources, and the deployment of edge computing for real-time threat detection. As organizations continue to embrace deep learning technologies, they will enhance their ability to navigate the complexities of the digital landscape and strengthen their defenses against a continuously evolving array of cyber threats. Overall, deep learning is set to play a crucial role in reshaping cybersecurity practices, driving innovations that improve security postures and operational efficiencies in the face of rising cyber risks.