Leveraging Artificial Intelligence for Enhancing Cybersecurity: A Deep Learning Approach to Real-Time Threat Detection

Abstract

This paper explores the transformative potential of Artificial Intelligence (AI), specifically deep learning, in strengthening cybersecurity through real-time threat detection. Given the rapid evolution of cyber threats, traditional detection methods often fall short, necessitating innovative approaches that can adapt and respond swiftly. This study employs a qualitative approach with a literature review and library research methodology to analyze current AI applications in cybersecurity. The research investigates the implementation of deep learning algorithms for identifying patterns and anomalies indicative of potential threats in digital systems. The findings indicate that deep learning models, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), enhance the precision and speed of threat detection, enabling proactive defense mechanisms. The study also addresses the challenges of implementing AI in cybersecurity, including data privacy, computational demands, and the need for continual model updates to counteract evolving threats. This work concludes that deep learning offers promising advancements for real-time threat detection, although its effectiveness depends on balanced integration with other cybersecurity practices and robust frameworks for data protection. Future research is encouraged to explore hybrid models combining deep learning with other AI techniques to further bolster cybersecurity defenses.