A Hybrid Deep Learning Approach for Network Intrusion Detection and Prevention

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dc.contributor.authorNiloy Kumar Joy
dc.date.accessioned2025-11-23T06:35:56Z
dc.date.available2025-11-23T06:35:56Z
dc.date.issued2025-07-30
dc.description.abstractWith the ever-changing online threats that are entailing fast development, there are increased concerns on strong and intelligent network security. The static rule-based traditional intrusion detection systems (IDS) based on signature-based approach are not able to detect new or advanced attacks. The present thesis suggests a combination of both deep learning technologies based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks to improve the detection of intrusion and allow the intrusion prevention proactive measures. By combining the spatial feature extraction capability of CNN and the temporal dependence capture capacity of LSTM, CNN-LSTM model can be said to make best use of the two models when the domain is network traffic. On the NSL-KDD dataset which is a benchmark dataset that comprises of different classes of attacks namely DoS, Probe, R2L, and U2R, the model was trained and tested versus other deep learning models like the MLP, standalone LSTM and autoencoder. The presented model beat others, with the highest accuracy of 95.79 percent, and better precision, recall, and F1-score. Besides detection, this paper discusses rudimentary prevention systems to narrow the response time and constrain attacks. The findings prove that a hybrid deep learning architecture is more likely to offer a more precise, flexible, and integrated answer to intrusion detection and prevention. The study can be used to develop intelligent cyber security solutions and result as the precursor of real-time and self-learning IDS applications in the future. Keywords: Network Intrusion Detection, CNN-LSTM, Deep Learning, NSL-KDD Dataset, Cybersecurity, Intrusion Prevention, Hybrid Neural Network, Anomaly Detection
dc.identifier.urihttps://dspace.nstu.ac.bd/handle/123456789/145
dc.titleA Hybrid Deep Learning Approach for Network Intrusion Detection and Prevention

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