AI-Driven Intrusion Detection: Enhancing Cybersecurity with Neural Networks
A. Amizhtha, Siddu Devi Naga Susmitha, T. Prem Jacob · 2025
Intrusion Detection Systems (IDS) are critical for securing networks in the constantly changing cyber threat landscape. Traditional IDS struggle with the increasing complexity of modern attacks. This research explores the use of Artificial Intelligence (AI), specifically neural networks, to enhance IDS capabilities. The proposed AI-driven IDS uses advanced neural network architectures, such as Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN), to detect malicious behavior with high accuracy in real-time network traffic. Experimental results show that the AI-driven IDS outperforms traditional IDS by achieving a detection accuracy of 95%, significantly reducing false positives by 30%, and demonstrating better adaptability to evolving threats. The system's integration with existing security infrastructure is seamless, providing high scalability and efficiency. These findings suggest that AI-driven IDS is a crucial advancement for safeguarding modern networks against increasingly sophisticated cyber threats.