Self-Organizing Computational System for Network Anomaly Exploration using Learning Algorithms
P. Preethi, Lalitha Krishnasamy, J. Yogapriya
Abstract
The forum in the nation for reporting information security flaws had 14,871 reports by the end of 2021, a 46.6% increase from 2020. The total of 5,567 high risk vulnerabilities, an increase of nearly 1,400 over the previous year. Evidently, both the total number of vulnerabilities found annually, and the total number of high-risk vulnerabilities are rising. In order for data mining technology to play a wider part in the predictive investigation of network security models, it is advised that its capability have to be improved. This paper combines the concepts of data mining (DM) with machine learning (ML), which introduces similar technologies from DM technology and security establishing collection channel, thereby finally introduces the computer network security maintenance process based on data mining in order to improve the application effect of DM in the predictive analysis of network security models. In this paper, a self-organizing neural network technique that detects denial of service (DOS) in complicated networks quickly, effectively, and precisely is introduced. It also analyses a number of frequently employed computer data mining methods, including association, clustering, classification, neural networks, regression, and web data mining. Finally, it introduces a computer data mining method based on the self-organizing (SO) algorithm. In comparison to conventional techniques, the SO algorithm-based computer data mining technology is also used in defensive detection tests against Dos attacks. A detection average accuracy rate of more than 98.56% and a detection average efficiency gain of more than 20% are demonstrated by experimental data to demonstrate that tests based on the Data Mining connected SO algorithm have superior defensive detection effects than standard algorithms.