Secure Cyber Threats using Network Intrusion Detection System with Deep Learning

Abstract

NIDS plays a crucial role in safeguarding computer networks against cyber threats. This work explores the application of deep learning techniques in enhancing NIDS capabilities. A subset of machine learning called deep learning has demonstrated remarkable potential in identifying complex patterns within network traffic data. By leveraging CNN and RNN the proposed NIDS achieves superior accuracy in detecting various types of intrusions, including anomalies and known attack patterns. The model is more flexible and effective at spotting new threats because it can gather relevant features on its own from raw network data. An overview of deep learning’s application to NIDS is given in this paper. In this work, we will discuss various DL architectures such as CNN and RNN and their use in feature extraction, anomaly detection, and classification of network traffic. We also emphasize the benefits and difficulties of employing deep learning for intrusion detection, such as data pre-treatment and model complexity training on large-scale datasets that help the NIDS perform well in terms of generalization and real-time prediction accuracy. Moreover, the integration of deep learning allows for the system to continuously learn and improve its detection accuracy over time. We demonstrate how Deep Learning-based NIDS can enhance intrusion detection’s accuracy and robustness in the dynamic and complex cyber threat environment through a review of previous studies.

Keywords: CNN, Cyber threats, Deep learning, Neural networks, RNN.

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