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 21, 2023

Abstract

Using the well-known dataset HTTP DATASET CSIC 2010, this work intends to build seven machine learning methods (Decision Tree, Random Forest, Gradient Boosting, XGBoost, AdaBoost, Multilayer Perceptron, and Voting) to identify anomaly assaults. Accuracy, precision, recall, and f1-score are four common evaluation metrics used to rate the effectiveness of these models. In order to identify several attack methods on this dataset, we conducted one experiment: Binary Classification into two categories (normal and malicious attacks). The findings demonstrated that in this experiment, the voting classifier and decision tree provided the greatest performance outcomes.

Keywords: HTTP DATASET CSIC 2010; Machine Learning; Cybersecurity Attacks; Detection Attacks

References

1. Seemma PS., et al. “Overview of cyber security”. International Journal of Advanced Research in Computer and Communication Engineering7.11 (2018): 125-128.‏
2. Ervural B C and Ervural B. “Overview of cyber security in the industry 4.0 era”. In Industry 4.0: managing the digital transformation (2018): 267-284.‏
3. Chowdhury A. “Recent cyber security attacks and their mitigation approaches–an overview”. In International conference on applications and techniques in information security (2016): 54-65.
4. El-Rewini Z., et al. “Cybersecurity challenges in vehicular communications”. Vehicular Communications23 (2020): 100214.‏
5. Handa A., et al. “Machine learning in cybersecurity: A review”. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery9.4 (2019): e1306.‏
6. Kim A., et al. “AI-IDS: Application of deep learning to real-time Web intrusion detection”. IEEE Access 8 (2020): 70245-70261.‏
7. Vartouni A M., et al. “An anomaly detection method to detect web attacks using stacked auto-encoder”. In 2018 6^th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS) (2018): 131-134.‏
8. Betarte G., et al. “Web application attacks detection using machine learning techniques”. In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) (2018): 1065-1072.‏
9. Tuan T A., et al. “Performance evaluation of Botnet DDoS attack detection using machine learning”. Evolutionary Intelligence (2019): 1-12.‏
10. Anwer M., et al. “Attack Detection in IoT using Machine Learning”. Engineering, Technology and Applied Science Research 3 (2021): 7273-7278.‏
11. Su T., et al. “BAT: Deep learning methods on network intrusion detection using NSL-KDD dataset”. IEEE Access 8 (2020): 29575-29585.‏
12. Xu W., et al. “Improving Performance of Autoencoder-Based Network Anomaly Detection on NSL-KDD Dataset”. IEEE Access9 (2021): 140136-140146.‏
13. Kavitha S and Uma Maheswari N. “Network Anomaly Detection for NSL-KDD Dataset Using Deep Learning”. Information Technology in Industry9.2 (2021): 821-827.
14. Ferriyan A., et al. “Generating Network Intrusion Detection Dataset Based on Real and Encrypted Synthetic Attack Traffic”. Applied Sciences11.17 (2021): 7868.‏
15. Giménez C T., et al. “HTTP data set CSIC 2010”. Information Security Institute of CSIC (Spanish Research National Council) (2010).‏‏
16. Hancock J T and Khoshgoftaar T M. “Survey on categorical data for neural networks”. Journal of Big Data7.1 (2020): 1-41.‏
17. Pal M. “Random forest classifier for remote sensing classification”. International Journal of Remote Sensing1 (2005): 217-222.‏
18. Farnaaz N and Jabbar M A. “Random forest modeling for network intrusion detection system”. Procedia Computer Science89 (2016): 213-217.‏
19. 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).
20. Kingsford C and Salzberg SL. “What are decision trees?”. Nature Biotechnology9 (2008): 1011-1013.‏
21. Quinlan J R. “Induction of decision trees”. Machine Learning1 (1986) 81-106.‏
22. De Ville B. “Decision trees”. Wiley Interdisciplinary Reviews: Computational Statistics6 (2013): 448-455.‏
23. Kotsiantis SB. “Decision trees: a recent overview”. Artificial Intelligence Review39.4 (2013): 261-283.‏
24. 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.‏
25. Noriega L. “Multilayer perceptron tutorial”. School of Computing. Staffordshire University (2005).‏
26. Tang J., et al. “Extreme learning machine for multilayer perceptron”. IEEE transactions on neural networks and learning systems 27.4 (2015): 809-821.‏
27. Ramchoun H., et al. “Multilayer perceptron: Architecture optimization and training” (2016).
28. Mitchell R and Frank E. “Accelerating the XGBoost algorithm using GPU computing”. Peer Journal of Computer Science3 (2017): e127.‏
29. Pan B. “Application of XGBoost algorithm in hourly PM2. 5 concentration prediction”. In IOP conference series: earth and environmental science 113.1 (2018): 012127.‏
30. Dong W., et al. “XGBoost algorithm-based prediction of concrete electrical resistivity for structural health monitoring”. Automation in Construction114 (2020): 103155.‏
31. 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.‏
32. 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.‏
33. Yuan L and Zhang F. “Ear detection based on improved adaboost algorithm”. In 2009 International Conference on Machine Learning and Cybernetics (2009): 2414-2417.‏
34. 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.‏
35. Peter S., et al. “Cost efficient gradient boosting”. Advances in Neural Information Processing Systems 30 (2017).
36. Lusa L. “Gradient boosting for high-dimensional prediction of rare events”. Computational Statistics and Data Analysis113 (2017): 19-37.‏
37. 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.‏
38. El-Kenawy E S M., et al. “Novel feature selection and voting classifier algorithms for COVID-19 classification in CT images.” IEEE Access8 (2020): 179317-179335.‏
39. Khan M A., et al. “Voting classifier-based intrusion detection for iot networks”. In Advances on Smart and Soft Computing (2022): 313-328.
40. Mahabub A. “A robust technique of fake news detection using Ensemble Voting Classifier and comparison with other classifiers”. SN Applied Sciences 2.4 (2020): 1-9.‏
41. Dalianis H. “Evaluation metrics and evaluation”. In Clinical text mining (2018): 45-53.

Citation

Citation: Saif Rawashdeh. “Cybersecurity in Machine Learning Techniques: Detecting Network Attacks".Acta Scientific Computer Sciences 5.11 (2023): 21-29.

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.