An Isolation Forest Learning Based Outlier Detection Approach for Effectively Classifying Cyber Anomalies
This research is significant for cybersecurity professionals and organizations seeking to enhance intrusion detection systems by improving the accuracy of cyber anomaly classification, representing an incremental improvement in existing methods.
This paper addresses the problem of effectively classifying cyber anomalies by proposing an Isolation Forest Learning-Based Outlier Detection Model. The model improves the classification accuracy of cyber anomalies after removing outliers, outperforming several conventional machine learning approaches.
Cybersecurity has recently gained considerable interest in today's security issues because of the popularity of the Internet-of-Things (IoT), the considerable growth of mobile networks, and many related apps. Therefore, detecting numerous cyber-attacks in a network and creating an effective intrusion detection system plays a vital role in today's security. In this paper, we present an Isolation Forest Learning-Based Outlier Detection Model for effectively classifying cyber anomalies. In order to evaluate the efficacy of the resulting Outlier Detection model, we also use several conventional machine learning approaches, such as Logistic Regression (LR), Support Vector Machine (SVM), AdaBoost Classifier (ABC), Naive Bayes (NB), and K-Nearest Neighbor (KNN). The effectiveness of our proposed Outlier Detection model is evaluated by conducting experiments on Network Intrusion Dataset with evaluation metrics such as precision, recall, F1-score, and accuracy. Experimental results show that the classification accuracy of cyber anomalies has been improved after removing outliers.