AI-Enhanced Cybersecurity: Leveraging Neural Networks for Proactive Threat Detection and Prevention

Abstract

The evolving landscape of cybersecurity demands advanced methods for proactive threat detection and prevention. This paper explores the application of neural networks in cybersecurity, highlighting how AI-enhanced systems can identify patterns, predict threats, and respond to potential cyber-attacks in real-time. Neural networks, with their capacity to analyze vast datasets and uncover complex patterns, offer a dynamic solution for detecting malware, phishing attempts, and anomalous activities across digital infrastructures. By leveraging supervised and unsupervised learning techniques, these AI-driven systems can continuously adapt to emerging threats, reducing false positives and enhancing response times. Furthermore, integration with natural language processing and reinforcement learning enables a deeper understanding of threat vectors, allowing cybersecurity frameworks to evolve in tandem with sophisticated attack strategies. This research discusses challenges such as model interpretability and adversarial robustness, underscoring the importance of secure model training to avoid manipulation by threat actors. The study concludes with recommendations for future research on neural network-based cybersecurity systems and their scalability for robust, autonomous defense mechanisms.