A Novel Approach for Network Intrusion Detection Using Multistage Deep Learning Image Recognition

The current rise in hacking and computer network attacks throughout the world has heightened demand for improved intrusion detection prevention solutions. system (IDS) is critical identifying abnormalities assaults on network, which have grown size pervasiveness. paper proposes a novel approach using multistage deep learning image recognition. features are transformed into four-channel (Red, Green, Blue, Alpha) images. images then used classification to train test pre-trained model ResNet50. proposed evaluated two publicly available benchmark datasets, UNSW-NB15 BOUN Ddos. On dataset, achieves 99.8% accuracy of generic attack. DDos suggested 99.7% attack normal traffic.