One-Dimensional Dilated Hypothesized Learning Method for Intrusion Detection System Under Constraint Resource Environment

When a breach is discovered, intrusion detection systems can alert server administrators and researchers and channel packets for cyber security incidents. In complex systems, these reports are becoming uncontrollable. To create a flexible and effective system for detecting intrusions for unforeseen threats, a number of challenges must be overcome. Systems that detect intrusions have frequently used advanced machine learning approaches, but they frequently require large amounts of computational power and processing time. In this article, the classification model is an IDS based on a one-dimensional-dilated hypothesized learning algorithm (1-DDHL). In order to account for the greatest possible pooling layer's complexity and eliminate the data redundancies brought on by pooling as well as down-sampling, dilated hypothesized inversion with a dilatation rate that includes two is initiated. In order to gather morecontexts, the dilated hypothesized convolution can widen its convolution layers. To analyze the effectiveness of the suggested solution, experiments were conducted using the CIC-IDS2017( Dataset 1)& CSE-CIC-IDS2018 (Dataset 2) sets of data, 2 well-known datasets that are publicly accessible. The 1-DDHL derived method is more accurate than some other deep learning methods currently in use, according to simulation results. With a problematic occurrence and avoidance rate of approximately 99.8% over dataset 1 and 99.97% for dataset 2, the suggested method was remarkably accurate.