Real-Time threat identification and categorization in network traffic using deep learning behavioral analysis

Abstract The rapid growth of digital technologies has led to increasingly sophisticated cyber threats, requiring innovative solutions for instantaneous intrusion detection system, classification, and network traffic analysis. This investigation presents a data-centric deep learning framework that merges advanced security system concepts with an optimized fully connected neural network for behavioral analysis and vulnerability evaluation. The proposed system, Optimized Fully Connected Neural Network for Cyber Threat Detection, incorporates innovative data preprocessing techniques, leverages the Agile Correlation-Based Filter for feature selection, and tackles class imbalance using the artificial minority data oversampling method. Moreover, it bolsters the detection framework with adaptive security system mechanisms to efficiently filter malicious traffic and thwart unauthorized access. Empirical assessment demonstrates the framework’s ability to achieve 96.35% accuracy, exceeding current innovative methods. The proposed model can identify and categorize multiple network intrusions, ensure high accuracy, recall minimizing false positives rate, and enable comprehensive vulnerability assessments. The proposed solution enhances real-time monitoring, decision-making, and threat mitigation capabilities by integrating sophisticated security system techniques. This contribution safeguards digital landscapes, enabling the wider objective of fostering secure, reliable, and sustainable technological advancements that drive economic growth and social progress.