AI-Driven Network Security: Using Deep Learning to Detect and Mitigate Cyber Threats in Real Time

Abstract

The exponential growth of cyber threats necessitates advanced security mechanisms capable of real-time detection and mitigation. Traditional signature-based security systems struggle with sophisticated attacks, zero-day exploits, and advanced persistent threats (APTs). This paper presents a comprehensive analysis of artificial intelligence (AI)-driven network security systems utilizing deep learning methodologies for real-time cyber threat detection and mitigation using the NSL-KDD dataset. We examine convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid deep learning architectures applied to network intrusion detection systems (NIDS). Our analysis encompasses recent developments in deep learning applications, including multi-layer perceptron (MLP) models, long short-term memory (LSTM) networks, and autoencoder-based anomaly detection systems. The paper evaluates performance metrics on the widely-used NSL-KDD benchmark dataset, demonstrating superior detection accuracy rates of 99.24% for binary classification and 98.73% for multiclass classification while maintaining low false-positive rates of 1.2%. Key contributions include a systematic evaluation of deep learning architectures for network security, analysis of real-time implementation challenges using the NSL-KDD dataset, and identification of emerging research directions in AI-powered cybersecurity. Results indicate that hybrid CNN-LSTM models achieve optimal performance for sequential network traffic analysis with 99.24% accuracy, outperforming traditional machine learning approaches by 15-20%.