A Hybrid Deep Learning and Machine Learning Model for Intelligent Cyber Threat Detection in Smart Networks

Le résumé fourni par la source

The rapid expansion of smart networks, encompassing the Internet of Things (IoT), software-defined networking (SDN), and 5G-enabled edge infrastructure, has dramatically increased the attack surface available to malicious actors, while simultaneously producing high-velocity, heterogeneous traffic that traditional signature-based intrusion detection systems struggle to analyze in real time. This paper proposes a Hybrid Deep Learning and Machine Learning (DL-ML) framework for intelligent cyber threat detection that fuses a Convolutional Neural Network combined with a Bidirectional Long Short-Term Memory (CNN-BiLSTM) branch, which captures spatial and temporal traffic patterns, with a gradient-boosted ensemble branch (XGBoost/Random Forest), which captures statistical flow-level signatures. The outputs of both branches are combined through a weighted feature-fusion and ensemble layer that produces a unified threat classification and severity score. The framework was evaluated on a large-scale smart-network intrusion dataset comprising over 1.8 million labeled flow records spanning six traffic classes: normal, DDoS, botnet, port scanning, malware communication, and spoofing. Experimental results show that the proposed hybrid model achieves 98.8% accuracy, 96.4% precision, 95.6% recall, and a 96.0% F1-score, exceeding the strongest individual baseline (LSTM) by 3.7 percentage points in F1-score and achieving an AUC of 0.992.

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