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

Authors: Rony Chowdhury Ripan, Iqbal H. Sarker, Md Musfique Anwar, Md. Hasan Furhad, Fazle Rahat, Mohammed Moshiul Hoque, Muhammad Sarfraz
Published: 2020-12-09

Source: https://arxiv.org/abs/2101.03141

PDF: https://arxiv.org/pdf/2101.03141

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機械学習手法 性能評価 特徴抽出手法

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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.