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
ef ectively pinpointing and mitigating potential
vulnerabilities. This paper introduces an innovative
risk assessment technique rooted in both Machine
Learning and graph theory, which of ers 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.
Asuai, C & Giroh, G (2023). Exploring the Fusion of Graph Theory and Diverse Machine Learning Models in Evaluating Cybersecurity Risk. Afribary. Retrieved from https://track.afribary.com/works/exploring-the-fusion-of-graph-theory-and-diverse-machine-learning-models-in-evaluating-cybersecurity-risk
Asuai, Clive and Gideon Giroh "Exploring the Fusion of Graph Theory and Diverse Machine Learning Models in Evaluating Cybersecurity Risk" Afribary. Afribary, 31 Aug. 2023, https://track.afribary.com/works/exploring-the-fusion-of-graph-theory-and-diverse-machine-learning-models-in-evaluating-cybersecurity-risk. Accessed 24 Dec. 2024.
Asuai, Clive, Gideon Giroh . "Exploring the Fusion of Graph Theory and Diverse Machine Learning Models in Evaluating Cybersecurity Risk". Afribary, Afribary, 31 Aug. 2023. Web. 24 Dec. 2024. < https://track.afribary.com/works/exploring-the-fusion-of-graph-theory-and-diverse-machine-learning-models-in-evaluating-cybersecurity-risk >.
Asuai, Clive and Giroh, Gideon . "Exploring the Fusion of Graph Theory and Diverse Machine Learning Models in Evaluating Cybersecurity Risk" Afribary (2023). Accessed December 24, 2024. https://track.afribary.com/works/exploring-the-fusion-of-graph-theory-and-diverse-machine-learning-models-in-evaluating-cybersecurity-risk