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

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

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The exponential growth of computers and devices connected to the Internet and the variety of Furniture 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 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, Miele KMDA 7633 FL Induction Hob With Integrated Extractor and 89.3% and 98.5% for Random Forest.