Saif Rawashdeh*

Department of Computer Science, Jordan University of Science and Technology, Jordan

*Corresponding Author: Saif Rawashdeh, Department of Computer Science, Jordan University of Science and Technology, Jordan.

Received: October 11, 2023; Published: October 20, 2023

Abstract

The goal of this study is to detect anomaly assaults using a variety of machine learning methods (Decision Tree, Random Forest, Gradient Boosting, XGBoost, AdaBoost, Multilayer Perceptron, and Voting) using the well-known dataset NSL-KDD. Accuracy, precision, recall, and f1-score are the four assessment measures used to evaluate the performance of these algorithms. As a result, we will run two experiments to look for different kinds of assaults on this dataset: 1) Two categories of binary classification (normal and malicious attacks). 2) Multiclass classification (malicious attacks types). These tests check if the algorithms can distinguish between the many types of harmful attacks that can be found in the NSL-KDD dataset. The outcomes demonstrated that in both studies, the XGB classifier had the greatest performance results.

Keywords: NSL-KDD Dataset; Machine Learning; Cybersecurity Attacks; Detection Attacks

References

1. Ahsan Mostofa., et al. “Cybersecurity threats and their mitigation approaches using Machine Learning—A Review”. Journal of Cybersecurity and Privacy3 (2022): 527-555.‏
2. Corallo A., et al. “Cybersecurity awareness in the context of the Industrial Internet of Things: A systematic literature review”. Computers in Industry 137 (2022): 103614.‏
3. AlDaajeh S., et al. “The role of national cybersecurity strategies on the improvement of cybersecurity education”. Computers and Security (2022): 102754.‏
4. Mijwil M., et al. “The Significance of Machine Learning and Deep Learning Techniques in Cybersecurity: A Comprehensive Review”. Iraqi Journal for Computer Science and Mathematics1 (2023): 87-101.‏‏
5. Annamalai C. “Application of Factorial and Binomial identities in Information, Cybersecurity and Machine Learning”. International Journal of Advanced Networking and Applications 1 (2022): 5258-5260.‏
6. Amaizu G C., et al. “Investigating Network Intrusion Detection Datasets Using Machine Learning”. In 2020 International Conference on Information and Communication Technology Convergence (ICTC) (2020): 1325-1328.‏
7. Anwer M., et al. “Attack Detection in IoT using Machine Learning”. Engineering, Technology and Applied Science Research 3 (2021): 7273-7278.‏
8. Su T., et al. “BAT: Deep learning methods on network intrusion detection using NSL-KDD dataset”. IEEE Access 8 (2020): 29575-29585.‏
9. Xu W., et al. “Improving Performance of Autoencoder-Based Network Anomaly Detection on NSL-KDD Dataset”. IEEE Access 9 (2021): 140136-140146.‏
10. Kavitha S and Uma Maheswari N. “Network Anomaly Detection for NSL-KDD Dataset Using Deep Learning”. Information Technology in Industry2 (2021): 821-827.
11. Dhanabal L and Shantharajah S P. “A study on NSL-KDD dataset for intrusion detection system based on classification algorithms”. International Journal of Advanced Research in Computer and Communication Engineering6 (2015): 446-452.‏
12. Hancock J T and Khoshgoftaar TM. “Survey on categorical data for neural networks”. Journal of Big Data 1 (2020): 1-41.‏
13. Pal M. “Random forest classifier for remote sensing classification”. International Journal of Remote Sensing1 (2005): 217-222.‏
14. Farnaaz N and Jabbar M A. “Random forest modeling for network intrusion detection system”. Procedia Computer Science 89 (2016): 213-217.‏
15. Idhammad M., et al. “Detection system of HTTP DDoS attacks in a cloud environment based on information theoretic entropy and random forest”. Security and Communication Networks (2018).‏
16. Kingsford C and Salzberg S L. “What are decision trees?”. Nature Biotechnology9 (2008): 1011-1013.‏
17. Quinlan JR. “Induction of decision trees”. Machine Learning1 (11986): 81-106.‏
18. De Ville B. “Decision trees”. Wiley Interdisciplinary Reviews: Computational Statistics 5.6 (2013): 448-455.‏
19. Kotsiantis S B. “Decision trees: a recent overview”. Artificial Intelligence Review4 (2013): 261-283.‏
20. Amor N B., et al. “Naive bayes vs decision trees in intrusion detection systems”. In Proceedings of the 2004 ACM symposium on Applied computing (2004): 420-424.‏
21. Noriega L. “Multilayer perceptron tutorial”. School of Computing. Staffordshire University (2005).‏
22. Tang J., et al. “Extreme learning machine for multilayer perceptron”. IEEE Transactions on Neural Networks and Learning Systems4 (2015): 809-821.‏
23. Ramchoun H., et al. “Multilayer perceptron: Architecture optimization and training” (2016).‏
24. Mitchell R and Frank E. “Accelerating the XGBoost algorithm using GPU computing”. PeerJ Computer Science 3 (2017): e127.‏
25. Pan B. “Application of XGBoost algorithm in hourly PM2. 5 concentration prediction”. In IOP conference series: earth and environmental science 113.1 (2018): 012127.‏
26. Dong W., et al. “XGBoost algorithm-based prediction of concrete electrical resistivity for structural health monitoring”. Automation in Construction 114 (2020): 103155.‏
27. Hu W and Hu W. “Network-based intrusion detection using Adaboost algorithm”. In The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05) (2005): 712-717.
28. Jabri S., et al. “Moving vehicle detection using Haar-like, LBP and a machine learning Adaboost algorithm”. In 2018 IEEE International Conference on Image Processing, Applications and Systems (IPAS) (2018): 121-124.‏
29. Yuan L and Zhang F. “Ear detection based on improved adaboost algorithm”. In 2009 International Conference on Machine Learning and Cybernetics 4 (2009): 2414-2417.‏
30. Son J., et al. “Tracking-by-segmentation with online gradient boosting decision tree”. In Proceedings of the IEEE international conference on computer vision (2015): 3056-3064.‏
31. Peter S., et al. “Cost efficient gradient boosting”. Advances in Neural Information Processing Systems 30 (2017).‏
32. Lusa L. “Gradient boosting for high-dimensional prediction of rare events”. Computational Statistics and Data Analysis 113 (2017): 19-37.‏
33. Kumar U K., et al. “Prediction of breast cancer using voting classifier technique”. In 2017 IEEE international conference on smart technologies and management for computing, communication, controls, energy and materials (ICSTM) (2017): 108-114.‏
34. El-Kenawy E S M., et al. “Novel feature selection and voting classifier algorithms for COVID-19 classification in CT images”. IEEE Access 8 (2020): 179317-179335.‏
35. Khan MA., et al. “Voting classifier-based intrusion detection for iot networks”. In Advances on Smart and Soft Computing (2022): 313-328.‏
36. Mahabub A. “A robust technique of fake news detection using Ensemble Voting Classifier and comparison with other classifiers”. SN Applied Sciences4 (2020): 1-9.‏
37. Dalianis H. “Evaluation metrics and evaluation”. In Clinical text mining (2018): 45-53.

Citation

Citation: Saif Rawashdeh. “Detecting Cybersecurity Attacks Using Machine Learning Techniques".Acta Scientific Computer Sciences 5.11 (2023): 11-20.

Copyright

Copyright: © 2023 Saif Rawashdeh. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.