A Hybrid Deep Learning Model for Network Intrusion Detection System Using Seq2Seq and ConvLSTM Subnets

Network security has become a critical concern as the complexity and volume of cyber-attacks continue to rise, especially in environments with interconnected devices and systems. This project introduces a novel hybrid deep learning model for Network Intrusion Detection Systems (NIDS), which integrates Sequence to Sequence (Seq2Seq) architecture with Convolutional Long Short-Term Memory (ConvLSTM) subnets, Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Forward Neural Networks (FNN). The proposed hybrid model effectively combines the advantages of these architectures to detect and classify network intrusions by analyzing network traffic data for patterns indicative of malicious behavior.The Seq2Seq architecture is used to model temporal dependencies in network traffic, while the ConvLSTM network is applied to capture spatiotemporal features, enhancing the ability to detect complex attack patterns. Additionally, CNNs are used for feature extraction to capture high-level features from the data, which are then fed into FNNs for classification. The system incorporates Explainable Artificial Intelligence (XAI) using Local Interpretable Model-agnostic Explanations (LIME), providing transparency and interpretability to the detection process, ensuring trustworthiness in real-time decision-making.The model is trained and evaluated using standard benchmark datasets, achieving high detection accuracy and robustness against various attack types. This hybrid approach demonstrates superior performance over traditional intrusion detection methods and offers a scalable, interpretable solution for enhancing network security.

Keywords: Network Intrusion Detection, Hybrid Deep Learning, Seq2Seq, ConvLSTM, CNN, LSTM, FNN, XAI, LIME, Cybersecurity, Anomaly Detection, Real-Time Threat Detection, Deep Learning, Network Security.