Deep Learning-Based Network Intrusion Detection Using Hybrid CNN and LSTM Architecture

Abstract

The rapid growth of digital communication technologies and interconnected network infrastructures has increased the frequency and sophistication of cyber threats. Traditional Network Intrusion Detection Systems (NIDS), which primarily depend on signature-based and rule-based approaches, often struggle to detect zero-day attacks and evolving intrusion patterns. This study presents the design and implementation of a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) deep learning-based Network Intrusion Detection System to improve the detection accuracy and reliability of malicious network activities. The proposed system combines the feature-extraction capabilities of CNNs with the temporal sequence-learning capabilities of LSTMs to identify both spatial and sequential characteristics of network traffic. The CICIDS2017 benchmark dataset was utilised for system training and evaluation. Data preprocessing techniques such as removing missing values, feature scaling, label encoding, and Synthetic Minority Oversampling Technique (SMOTE) balancing were applied to improve data quality and class distribution. The model was implemented using Python, TensorFlow, Keras, Scikit-learn, NumPy, and Pandas within a Google Colab environment. The processed dataset was partitioned into 80% for training and 20% for testing. Experimental results demonstrated high intrusion detection performance with an accuracy of 98.48%, precision of 97.75%, recall of 99.23%, F1-score of 98.49%, and a false alarm rate of 2.28%. In addition to the deep learning model, a web-based user interface was developed to support traffic prediction, performance monitoring, and management of prediction history. The findings indicate that hybrid deep learning techniques can improve network security by enhancing intrusion detection capability while reducing false alarms.