A Hybrid Deep Learning Approach for Network Intrusion Detection and Prevention
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Date
2025-07-30
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Abstract
With 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