A Data-Driven Predictive Analysis on Cyber Security Threats with Key
  Risk Factors
Fatama Tuz Johora Md Shahedul Islam Khan Esrath Kanon Mohammad Abu Tareq Rony Md Zubair Iqbal H. Sarker
Cyber risk refers to the risk of defacing reputation, monetary losses, or
disruption of an organization or individuals, and this situation usually occurs
by the unconscious use of cyber systems. The cyber risk is unhurriedly
increasing day by day and it is right now a global threat. Developing countries
like Bangladesh face major cyber risk challenges. The growing cyber threat
worldwide focuses on the need for effective modeling to predict and manage the
associated risk. This paper exhibits a Machine Learning(ML) based model for
predicting individuals who may be victims of cyber attacks by analyzing
socioeconomic factors. We collected the dataset from victims and non-victims of
cyberattacks based on socio-demographic features. The study involved the
development of a questionnaire to gather data, which was then used to measure
the significance of features. Through data augmentation, the dataset was
expanded to encompass 3286 entries, setting the stage for our investigation and
modeling. Among several ML models with 19, 20, 21, and 26 features, we proposed
a novel Pertinent Features Random Forest (RF) model, which achieved maximum
accuracy with 20 features (95.95\%) and also demonstrated the association among
the selected features using the Apriori algorithm with Confidence (above 80\%)
according to the victim. We generated 10 important association rules and
presented the framework that is rigorously evaluated on real-world datasets,
demonstrating its potential to predict cyberattacks and associated risk factors
effectively. Looking ahead, future efforts will be directed toward refining the
predictive model's precision and delving into additional risk factors, to
fortify the proposed framework's efficacy in navigating the complex terrain of
cybersecurity threats.