Anomaly based Intrusion Detection System using Hybrid ResNet50 and 3D Convolutional Neural Network
Keywords:
Black Widow Optimization, Convolutional Neural Network, Deep Learning, Intrusion Detection System, ResNet50Abstract
An Intrusion Detection System (IDS) is used to monitor and analyze data to identify any intrusions that have been made into a system or network. Because of the large capacity of network data with redundant and irrelevant features, accurate and precise intrusion detection is challenging. To overcome this problem, this research proposed the Deep Learning (DL) approach of ResNet50 and 3D Convolutional Neural Network (CNN) with Black Widow Optimization (BWO) for detecting the network attacks in IDS. Initially, the input data is acquired from three public datasets named CICDDoS2019, NSL-KDD, and UNSW-NB15 datasets. The collected data is then pre-processed to preserve the redundancy using normalization. The pre-processed output is then provided to the feature selection process for compiling the optimum features by using Principal Component Analysis (PCA). The network weights are optimized using BWO to obtain the optimum solution for identifying intruders. According to the findings, the proposed method is more efficient and it achieves accuracy of 0.93, 0.96 and 0.99 on CICDDoS2019, UNSW-NB15 and NSL-KDD datasets when compared to existing methods like Deep Feed Forward Neural Network (DFNN), CNN and Generative Adversarial Network (GAN).
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