Model for Predicting Cyber Threats Utilizing Advanced Data Science Techniques

Abstract

In the technological age and amidst the pervasive utilization of the internet, the data and personal information of internet users are increasingly vulnerable. Among numerous cyber-attacks, DDoS is one of the most perilous, employing single or multiple targets to render resources unavailable on both small and large scales. The frequency and severity of cyber-attacks are progressively rising alongside the growing use of the internet. Defensive measures are developed over time to safeguard a network and its devices from various breaches and attacks perpetrated by cyber terrorists. Data science enhances the prediction and detection of cyber-attacks, beyond conventional protection measures. This work suggested a data science-driven predictive model utilizing a significantdataset, CICDDOS2019. This research employs various Machine Learning models, including Decision Tree, Random Forest, SVM, and Naïve Bayes, after the dataset’s cleansing and the selection of optimal relevant characteristics to achieve maximum accuracy in detecting and predicting cyber risks.