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
Abstract :
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
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
