  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.
