Paper Contents
Abstract
To ensure optimal performance, network infrastructure must be properly managed by administrators, especially when adding new communication devices and fixing network problems. As the dynamic IT organization landscape becomes more complicated, system management must function in ad hoc, cross-domain scenarios. Static hierarchical structures are the foundation of current management systems, which are intended for relatively small companies with 103 or less members. The proliferation of smart gadgets, mobile technologies, and network developments has led to an increase in the quantity of network devices and services. Due to this expansion, a greater number of devices are now online, increasing the demand for real-time processing and broadening the range of services needed. This work presents a system that efficiently predicts possible assaults on power systems utilizing a variety of machine learning techniques, such as random forest and logistic regression. Network advancements, mobile technologies, and the spread of smart devices have increased the number of network devices and services. A larger number of devices are now online as a result of this expansion, which raises the need for real-time processing and expands the breadth of services that are required. This study presents a system that efficiently predicts possible assaults on power systems by utilizing a range of machine learning methods, including logistic regression and random forest.
Copyright
Copyright © 2025 VASANTHAKUMAR S. This is an open access article distributed under the Creative Commons Attribution License.
