Performance Analysis of Machine Learning Models for Cybersecurity Risk Classification in IT Projects

Prasanth Varma Addepalli, Sridhar Reddy Bandaru, Dhuli Shyam, Prabu Manoharan, Muzaffer Hussain Syed, Uday Kumar Ragireddy

Citation: Prasanth Varma Addepalli, Sridhar Reddy Bandaru, Dhuli Shyam, Prabu Manoharan, Muzaffer Hussain Syed, Uday Kumar Ragireddy, "Performance Analysis of Machine Learning Models for Cybersecurity Risk Classification in IT Projects", Universal Library of Engineering Technology, Special Issue.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The recent improvement of cyber threats in the contemporary IT setting has aggravated the necessity to establish effective ways of classifying and managing cybersecurity risks. Increasing complexity in systems and more advanced attacks require more advanced analytical tools that can identify risks in time and properly. This paper carries out an extensive examination to assess machine learning (ML) systems in efficient cybersecurity risks identification in IT ventures. The study focuses on the nature of risk environments, supervised and unsupervised methods in ML, and how models, including SVM, Random Forest, Logistic Regression, and the use of clustering-based methods, play their role to determine vulnerabilities. Also, the paper explores such critical issues as skewed data, malicious manipulation, model drift, and human or organizational biases that affect risk outcomes. The detection of threats and decision-making but still have limitations in terms of scaling, interpretability, and deployment in the real world. Combining the ideas of risk assessment and the power of ML, this study indicate that ML has massive opportunities to enhance the cybersecurity risk management as long as the operational and technical issues are tackled in a systematic manner.


Keywords: Cybersecurity, Artificial Intelligence (AI), Information Technology (IT), Risk Classification, Machine Learning (ML).

Download doi https://doi.org/10.70315/uloap.ulete.2023.006