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

. 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 cre-ating an effective intrusion detection system plays a vital role in today’s security. However, it is difficult to accurately model cyber threats since modern security databases contain large number of security features that could include Outliers . 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),…