%AMohammad Masum, Hossain%AAyahiko Niimi, Future Ed.%BJournal Name: International journal of intellligent computing research; Journal Volume: 12; Journal Issue: 1 %D2021%I %JJournal Name: International journal of intellligent computing research; Journal Volume: 12; Journal Issue: 1 %K %MOSTI ID: 10273194 %PMedium: X %TA Transfer Learning with Deep Neural Network Approach for Network Intrusion Detection %XTraditional 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. %0Journal Article