Comparison of Classification Model for the Detection of Cyber-attack using Ensemble Learning Models
Keywords:cyberattack, machine learning, ensemble learning
Incorporating digital technologies into security systems is a positive development. It's time for the digital system to be appropriately protected from potential threats and attacks. An intrusion detection system can identify both external and internal anomalies in the network. There are a variety of threats out there, both active and passive. If these dangers aren't addressed, attacks and data theft could occur from the point of origin all the way to the point of destination. Machine learning is still in its infancy, despite its wide range of applications. It is possible to predict the future by using machine learning. A cyber-attack detection system is depicted in this study using machine learning models. Machine learning algorithms were trained to predict cyber-attack scores using data from prior cyber-attacks on an open source website. In order to detect an attack at its earliest possible stage, this research also examined multiple linear machine learning algorithm-based categorization models. Classifiers' accuracy is also compared in the presentation, as is the presentation itself. Balance procedures were followed. Radio Frequency and GBC have the best accuracy, at 87.93%, followed by ABC at 86.11%, BT at 81.03%, ET at 70.31%, and DT at 70.31 percent (84.48 percent ).
How to Cite
Copyright (c) 2022 EAI Endorsed Transactions on Scalable Information Systems
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.