Abstract:
In the age of increasing cyber attacks, strong network security is an ongoing challenge. This research uses the UNSW-NB15 dataset to create and test advanced machine learning methods for the efficient classification and prediction of network attacks. The study begins with careful data preprocessing, which involves outlier detection and mitigation, as well as the use of Borderline SMOTE to solve class imbalance. With a primary focus on binary classification, the study performs comparative analysis of some ensemble techniques. The Random Forest classifier was found to be the best performer with an accuracy of 93% and an F1-score of 0.93, thus surpassing past benchmarks where its accuracy was stated at 90%. Furthermore, multi-class classification experiments were conducted to further confirm the method and prove its usability in different attack scenarios. These results highlight the power of sophisticated classification methods in strengthening network defense mechanisms and offer significant insights for future studies in the field of cyberattack prediction and prevention.
