Zero day attack vulnerabilities: mitigation using machine learning for performance evaluation - Idris Olanrewaju Ibraheem; Abdulrauf Uthman Tosho (Journal of Computers for Society, 2024)
The paper explores and investigate how machine learning methods can help defend against zero-day cyber-attacks, which are a major concern in cybersecurity. The study focuses on several machine learning algorithms, such as gradient boosting classifiers, random forests, decision trees, and support vector machines (SVM). The study examines how well these algorithms can detect and prevent zero-day attacks. To do this, we carefully prepare a dataset containing different network characteristics for analysis, ensuring that categorical variables are handled properly. We then train and test the selected algorithms using this dataset. Based on the data, random forest outperforms the other algorithms in terms of detection rates and accuracy. This is due to the fact that random forest's ability to recognize intricate patterns linked to zero-day assaults is enhanced by its continuous learning of weaker models. The results demonstrate how machine learning may be used to improve cybersecurity defenses against new threats like zero-day assaults. The CSE-CIC-IDS2018 Dataset was used in the study's execution and assessment.