A Transfer Learning with Deep Neural Network Approach for Network Intrusion Detection
Mohammad Masum, Hossain Shahriar, Hisham M. Haddad · International Journal of Intelligent Computing Research · 2021
Traditional Network Intrusion Detection Systems (NIDS) encounter difficulties due to the exponential growth of network traffic data and modern attacks' requirements.This paper presents a novel network intrusion classification framework using transfer learning from the VGG-16 pre-trained model.The framework extracts feature leveraging pre-trained weights trained on the ImageNet dataset in the initial step, and finally, applies a deep neural network to the extracted features for intrusion classification.We applied the presented framework on NSL-KDD, a benchmark dataset for network intrusion, to evaluate the proposed framework's performance.We also implemented other pre-trained models such as VGG19, MobileNet, ResNet-50, and Inception V3 to evaluate and compare performance.This paper also displays both binary classification (normal vs. attack) and multi-class classification (classifying types of attacks) for network intrusion detection.The experimental results show that feature extraction using VGG-16 outperforms other pre-trained models producing better accuracy, precision, recall, and false alarm rates.