An Enhanced Deep Learning Framework for Network Intrusion Detection Using Deep Autoencoders

Abstract

The rapid evolution of cyber threats has significantly increased the demand for intelligent and adaptive Intrusion Detection Systems (IDS) capable of protecting modern computer networks from sophisticated attacks. Traditional signature-based intrusion detection techniques are limited in identifying unknown attacks, zero-day exploits, and evolving malicious behaviors, making anomaly-based detection an essential cybersecurity solution. This project proposes a Deep Autoencoder-Based Intrusion Detection System (DAIDS) that utilizes unsupervised deep learning to model normal network traffic and detect anomalous activities through reconstruction error analysis. The proposed framework incorporates comprehensive data preprocessing, including data cleaning, feature encoding, normalization, and feature selection, followed by training a deep autoencoder to learn compact and meaningful representations of legitimate network traffic. The system is evaluated using the NSL-KDD and CICIDS2017 benchmark datasets, which contain diverse categories of network attacks and normal traffic patterns. Experimental evaluation demonstrates that the proposed model effectively distinguishes malicious traffic from legitimate network activities while maintaining a low falsepositive rate. Performance is assessed using Accuracy, Precision, Recall, F1-Score, ROC-AUC, and Confusion Matrix analysis, achieving an overall detection accuracy of approximately 98.2% with high precision and recall. The deep autoencoder successfully captures complex nonlinear relationships within network traffic, enabling robust detection of both known and previously unseen cyber threats. The proposed framework offers improved scalability, efficient feature learning, reduced dependence on manual feature engineering, and enhanced adaptability to dynamic network environments. These results indicate that deep autoencoder-based anomaly detection provides a reliable, efficient, and scalable solution for strengthening network security and developing next-generation intelligent intrusion detection systems. Keywords— Intrusion Detection System (IDS), Deep Autoencoder, Network Security, Anomaly Detection, Deep Learning, Cybersecurity, NSL-KDD, CICIDS2017, Feature Learning, Network Traffic Analysis.