Anomaly Detection with Various Machine Learning Classification Techniques over UNSW-NB15 Dataset

Anomaly Detection with Various Machine Learning Classification Techniques over UNSW-NB15 Dataset

Blog Article

Body Mist The exponential growth of computers and devices connected to the Internet and the variety of commercial services offered creates the need to protect Internet users.As a result, intrusion detection systems (IDS) are becoming an essential part of each computer-communication system, detecting and responding to malicious network traffic and computer abuse.In this paper, an IDS based on the UNSW-NB15 dataset has been implemented.The results obtained indicate F1 Score Vacuum Lid Seal and Recall values of 76.

1% and 85.3% for the Naive Bayes algorithm, 78.2% and 96.1% for Logistic Regression algorithm, 88.

3% and 95.4% for Decision Tree classifier, and 89.3% and 98.5% for Random Forest.