HYBRID DEEP LEARNING FRAMEWORK FOR INTRUSION DETECTION: INTEGRATING CNN, LSTM, AND ATTENTION MECHANISMS TO ENHANCE CYBERSECURITY

Advanced intrusion detection systems (IDS) are required to protect contemporary networks due to the increasing complexity of cyber attacks. Due to their inability to fully capture the complex temporal and spatial patterns in network traffic, traditional intrusion detection systems (IDS) methods—such as standalone machine learning and deep learning models—frequently result in high false-positive rates and decreased detection accuracy. These drawbacks show how creative frameworks that can handle these problems are required. A hybrid deep learning framework that combines Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks and an attention mechanism is proposed in this study. The framework effectively captures spatial features, sequential dependencies, and critical network traffic patterns, enhancing accuracy and interpretability. The methodology includes comprehensive data preprocessing, principal component analysis (PCA) for dimensionality reduction, and recursive feature elimination (RFE) for feature selection. Hybrid Deep Learning-based Intrusion Detection (HDLID), a revolutionary algorithm, directs the suggested system's implementation. Tested on the UNSW-NB15 dataset, the framework outperforms state-of-the-art precision, recall, and F1-score models, achieving an impressive accuracy of 97.89%. The results validate its robustness and scalability for real-world applications. The proposed framework offers a practical, high-performance solution for intrusion detection, addressing limitations in existing methodologies and contributing to improved cybersecurity in diverse network environments.