An intelligent and efficient network intrusion detection system using deep learning

doi:10.1016/j.compeleceng.2022.107764

An intelligent and efficient network intrusion detection system using deep learning

2022-04-01

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DOI：10.1016/j.compeleceng.2022.107764

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With continuously escalating threats and attacks, accurate and timely intrusion detection in communication networks is challenging. Many approaches have already been proposed recently on network intrusion detection. However, they face critical challenges due to the continuous increase of new threats that current systems do not understand. Motivated by the outstanding performance of deep learning (DL) in many detection and recognition tasks, we introduce an intelligent and efficient network intrusion detection system (NIDS) based on DL. This study proposes a non-symmetric deep auto-encoder for network intrusion detection problems and presents its detailed functionality and performance. We validate the robustness and effectiveness of the proposed NIDS using a benchmark dataset, i.e., KDD CUP'99. Our DL-based method is implemented in the TensorFlow library and GPU framework, and it achieves an accuracy of 99.65%. The proposed system can be used in network security research domains and DL-based detection and classification systems.

Keywords:

Network security

Intrusion detection

Deep learning

Auto-encoder

SVM

An intelligent and efficient network intrusion detection system using deep learning

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