Hybrid Intelligent Systems for Cybersecurity and Intrusion Detection
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The proliferation of sophisticated cyber threats has rendered traditional security mechanisms insufficient, necessitating the development of advanced, intelligent defense systems. This chapter explores the application of Hybrid Intelligent Systems (HIS) for cybersecurity, with a specific focus on Intrusion Detection Systems (IDS). We propose a novel hybrid model that synergizes the temporal feature extraction capabilities of Bidirectional Long Short-Term Memory (BiLSTM) networks with the spatial feature learning prowess of Convolutional Neural Networks (CNN). This chapter details the design, implementation, and evaluation of this hybrid IDS. A comprehensive simulation is conducted on a synthetic dataset modeled after the NSLKDD benchmark, and the performance of the proposed hybrid model is compared against standalone machine learning models, including Random Forest and Gradient Boosting. The results demonstrate the superior performance of the hybrid approach in terms of accuracy, precision, recall, and F1-score, highlighting the potential of HIS in building robust and adaptive cybersecurity defenses.
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Principles of Hybrid Intelligent Systems
