An Isolation Forest Learning Based Outlier Detection Approach for Effectively Classifying Cyber Anomalies

TL;DR

This paper introduces an Isolation Forest-based outlier detection model to improve cyber anomaly classification, demonstrating enhanced accuracy over traditional machine learning methods in intrusion detection tasks.

Contribution

The paper proposes a novel outlier detection approach using Isolation Forests specifically tailored for cyber anomaly classification, with comprehensive evaluation against standard classifiers.

Findings

Improved classification accuracy after outlier removal

Effective detection of cyber anomalies in network data

Outperforming traditional classifiers in precision and recall

Abstract

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.