Machine Learning is widely used in cybersecurity for detecting network intrusions. Though network attacks are increasing steadily, the percentage of such attacks to actual network traffic is significantly less. And here lies the problem in training Machine Learning models to enable them to detect and classify malicious attacks from routine traffic. The ratio of actual attacks to benign data is significantly high and as such forms highly imbalanced datasets. In this work, we address this issue using data resampling techniques. Though there are several oversampling and undersampling techniques available, how these oversampling and undersampling techniques are most effectively used is addressed in this paper. Two oversampling techniques, Borderline SMOTE and SVM-SMOTE, are used for oversampling minority data and random undersampling is used for undersampling majority data. Both the oversampling techniques use KNN after selecting a random minority sample point, hence the impact of varying KNN values on the performance of the oversampling technique is also analyzed. Random Forest is used for classification of the rare attacks. This work is done on a widely used cybersecurity dataset, UNSW-NB15, and the results show that 10% oversampling gives better results for both BMSOTE and SVM-SMOTE.

One of the major problems in classifying network attack tactics is the imbalanced nature of data. Typical network datasets have an extremely high percentage of normal or benign traffic and machine learners are skewed toward classes with more data; hence, attack data remain incorrectly classified. This paper addresses the class imbalance problem using resampling techniques on a newly created dataset, UWF-ZeekData22. This is the first dataset with tactic labels, labeled as per the MITRE ATT&CK framework. This dataset contains about half benign data and half attack tactic data, but specific tactics have a meager number of occurrences within the attack tactics. Our objective in this paper was to use resampling techniques to classify two rare tactics, privilege escalation and credential access, never before classified. The study also looks at the order of oversampling and undersampling. Varying resampling ratios were used with oversampling techniques such as BSMOTE and SVM-SMOTE and random undersampling without replacement was used. Based on the results, it can be observed that the order of oversampling and undersampling matters and, in many cases, even an oversampling ratio of 10% of the majority data is enough to obtain the best results.