Performance Evaluation of Machine Learning Classifiers for Predicting Denial-of-Service Attack in Internet of Things

• Published in: International journal of advanced computer science & applications Vol. 15; no. 1
• Main Authors: Almomani, Omar, Alsaaidah, Adeeb, Shareha, Ahmad Adel Abu, Alzaqebah, Abdullah, Almomani, Malek
• Format: Journal Article
• Language: English
• Published: West Yorkshire Science and Information (SAI) Organization Limited 2024
• Subjects: Classifiers; Cybersecurity; Decision trees; Denial of service attacks; Internet of Things; Intrusion detection systems; Machine learning; Performance evaluation; Sensitivity; Support vector machines; Target detection
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2024.0150125

Eliminating security threats on the Internet of Things (IoT) requires recognizing threat attacks. IoT and its implementations are currently the most common scientific field. When it comes to real-world implementations, IoT's attributes, on the one hand, make it simple to apply, but on the other hand, they expose it to cyber-attacks. Denial of Service (DoS) attack is a type of threat that is now widespread in the field of IoT. Its primary goal is to stop or damage service or capability on a target. Conventional Intrusion Detection Systems (IDS) are no longer sufficient for detecting these sophisticated attacks with unpredictable behaviors. Machine learning (ML)--based intrusion detection does not need a massive list of expected activities or a variety of threat signatures to create detection rules. This study aims to evaluate different ML classifiers for network intrusion detection that focus on DoS attacks in the IoT environment to determine the best ML classifier that can detect the DoS attack. The XGBoost, Decision Tree (DT), Gaussian Naive Bayes (NB), Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM) ML classifiers are used to evaluate the DoS attack. The UNSW-NB15 dataset was used for this study. The obtained accuracy rate for XGboost was 98.92%, SVM 98.62%, Gaussian NB 83.75%, LR 97.74%, RF 99.48%, and DT 99.16%. where the precision rate for XGboost, SVM, Gaussian NB, LR, RF, and DT was 98.40%, 98.29%, 77.50%, 97.14%, 99.21%, and 99.12%, respectively. The sensitivity rate for XGboost, SVM, Gaussian NB, LR, RF, and DT was 99.29%, 98.76%, 91.87%, 98.06%, 99.69%, and 99.08%, respectively. The results show that the RF classifier outperformed other classifiers in terms of Accuracy, Precision, and Sensitivity.