Exploring the Fusion of Graph Theory and Diverse Machine Learning Models in Evaluating Cybersecurity Risk

Authors : Clive Ebomagune Asuai; Gideon Yuniyus Giroh

Volume/Issue : Volume 8 - 2023, Issue 8 - August

Google Scholar : https://bit.ly/3TmGbDi

Scribd : http://tinyurl.com/mwvehmxn

DOI : https://doi.org/10.5281/zenodo.8315238

The frequency and severity of cyber-attacks have surged, causing detrimental impacts on businesses and their operations. To counter the ever-evolving cyber threats, there's a growing need for robust risk assessment systems capable of effectively pinpointing and mitigating potential vulnerabilities. This paper introduces an innovative risk assessment technique rooted in both Machine Learning and graph theory, which offers a method to evaluate and foresee companies' susceptibility to cybersecurity threats. In pursuit of this objective, four Machine Learning algorithms (Random Forest, AdaBoost, XGBoost, Multi- Layer Perceptron (MLP)) will be employed, trained, and assessed using the UNSW-NB15 dataset that has a hybrid of real modern normal activities and synthetic contemporary attack behaviours..The findings indicate that the Multilayer Perceptron (MLP) performs better than other classifiers, achieving an accuracy of 98.2%.. By harnessing the capabilities of data-derived insights and intricate network analysis, this groundbreaking approach aims to equip organizations with a comprehensive and forward-looking cybersecurity defense strategy.

Keywords : Cyber-Attacks, Risk Assesment, Graph Theory, Multi-Layer Perceptron, AdaBoost, Random Forest, XGBoost


Paper Submission Last Date
31 - December - 2023

Paper Review Notification
In 1-2 Days

Paper Publishing
In 2-3 Days

Video Explanation for Published paper

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.