Network traffic analysis with machine learning for faster detection of distributed denial of service attack

Document Type : -

Authors

1 Master's student, Shahid Madani University of Azerbaijan, Tabriz, Iran

2 PhD student, Tabriz University, Tabriz, Iran

3 Professor, Tabriz University, Tabriz, Iran

Abstract

In this research, four machine learning algorithms for detecting DDoS attacks have been investigated and analyzed. For this purpose, the Intrusion Detection Evaluation Dataset (CIC-IDS2017) database, which includes botnet traffic samples, has been used. KNN, RF, Naive Bayes, and J48 algorithms were trained using the selected features with the SelectKBest function and the scikit-learn library. The results show that the RF, KNN, and J48 algorithms are very close in terms of accuracy and have performed well in identifying botnet traffic and normal traffic. The RF algorithm with a higher F1-score compared to KNN has provided more accuracy in identifying botnet traffic. On the other hand, the Naive Bayes algorithm, despite its high overall accuracy, has performed poorly in identifying botnet traffic, and its precision and recall are very low for botnet classification. The J48 algorithm has also performed relatively well, but due to the low recall value, a significant part of the attack traffic has been mistakenly identified as normal traffic. This research emphasizes that to deal with DDoS attacks, the use of modern machine learning algorithms can improve the accuracy and speed of identification. In the future, it will be necessary to test the models in real-world conditions with more diverse data in order to increase the accuracy and capabilities of Internet attack detection models.

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References

[1]  Asadi, M.; BagherI, Z. “Detection of Denial of Service Attacks by Ensemble Learning Method”; J. Adv. Defense Sci. & Technol. 2023, 14, 51-68 (In Persian). Dor:20.1001.1.26762935.1402. 14.1.5.5 
[2] Wang, B.; He, Y.; Shui, Z.; Xin, Q.; Lei, H. “Predictive Optimization of DDoS Attack Mitigation in Distributed Systems using Machine Learning”;  Appl. Comput. Eng. 2024, 64, 95-100. DOI:10.54254/2755 2721/64/20241350 
 [3] Mittal, M.; Kumar, K.; Behal, S. “Deep Learning Approaches for Detecting DDoS Attacks: A Systematic Review”; Soft computing 2023, 27, 13039-13075. DOI:10.1007/s00500-021-06608-1 
 [4] Kumari, P.; Jain, A. K. “A Comprehensive Study of DDoS Attacks Over IoT Network and Their Countermeasures”; Computers & Security. 2023, 127, 103096. DOI:10.1016/j.cose.2023.103096 
 [5] Mittal, M.; Kumar, K.; Behal, S. “Deep Learning Approaches for Detecting DDoS Attacks: A Systematic Review”; Soft Computing 2023, 27,  1-37. [v1] DOI:10.1007/s00500-021-06608-1 
 [6] Sattari, M. T.; Bagheri, R.; Shirini, K.; Allahverdipour, P. “Modeling Daily and Monthly Rainfall in Tabriz using Ensemble Learning Models and Decision Tree Regression”; Scientific Journal of Golestan University, 2024, 5, 31-48 DOI: 10.30488/CCR.2024.433394.1192 
[7] Saleh Esfehani, M.; Abo Ali, M. “An IDS for Detection of Active Attacks Against Routing in Mobile Ad Hoc Networks”; J. Adv. Defense Sci. & Technol. 2010, 1, 15-22 (In Persian). Dor: 20.1001.1.26762935.1389.1.1.2.1 
[8] Behal, S.; Kumar, K. “Characterization and Comparison of DDoS Attack Tools and Traffic Generators: A Review”; Int. J. Netw. Secur. 2017, 19,383-393DOI:10.6633/IJNS.201703.19(3).07
[9] Michelena, Á,; Aveleira‐Mata, J.; Jove, E.; Bayón‐Gutiérrez, M.; Novais, P.; Romero, O. F.; Aláiz‐Moretón, H. “A Novel Intelligent Approach for Man‐in‐the‐Middle Attacks Detection Over Internet of Things Environments Based on Message Queuing Telemetry Transport”; Expert Systems 2024, 41, e13263. DOI:10.1111/exsy.13263. 
[10] Choorod, P.; George, W.; Anil Fernando. “Classifying for Traffic Encrypted Payload using Machine Learning”; IEEE Access. 2024. DOI: 10.1109/ACCESS.2024.3356073 
[11] Priya, S. S.; Sivaram, M.; Yuvaraj, D.; Jayanthiladevi, A. “Machine Learning Based DDoS Detection”; International Conference on Emerging Smart Computing and Informatics. 2020, 234-237. DOI: 10.1109/ESCI48226.2020.9167642 
[12] Kazemitabar, J.; Taheri, R.; Kheradmandian, H. “A Novel Technique for Improvement of Intrusion Detection via Combining Random Forrest and Genetic Algorithm”; J. Adv. Defense Sci. & Technol. 2019, 287-296 (In Persian). Dor: 20.1001.1.26762935.1398.10.3.9.5 [v2] 
[13] Das, S.; Mahfouz, A. M.; Venugopal, D.; Shiva, S. “DDoS Intrusion Detection Through Machine Learning Ensemble”; IEEE 19th international conference on software Quality, Reliability and Security Companion. 2019, 471-477. DOI: 10.1109/QRS-C.2019.00090 
[14] Asgharian, H.; Ahmad A.; Raahemi, B. “Engineered Feature Set to Detect Flooding Attacks in SIP Based VoIP’; J. Adv. Defense Sci. & Technol. 2019, 8, 61-69 (In Persian). Dor: 20.1001.1.26762935.1396.8.1.7.5 
[15] Pande, S.; Khamparia, A.; Gupta, D.; Thanh, D. N. “DDOS Detection Using Machine Learning Technique”; Recent Studies on Computational Intelligence: Doctoral Symposium on Computational Intelligence, Springer Singapore 2021, 59-68. DOI:10.1007/978-981-15-8469-5_5 
[16] Abdulla, N. N.; Hasoun, R. K. “Review of Detection Denial of Service Attacks Using Machine Learning Through Ensemble Learning”; Iraqi Journal for Computers and Informatics. 2022, 48, 13-20. DOI:10.25195/ijci.v48i1.349  
[17] Sattari, M. T.; Shirini, K.; Javidan, S.” Evaluating the Efficiency of Dimensionality Reduction Methods in Improving the Accuracy of Water Quality Index Modeling using Machine Learning Algorithms”; Water and Soil Management and Modelling. 2024,4, 89-104. DOI: 10.22098/mmws.2023.12434.1241
 
 
Journal of Advanced Defense Science & Technology
Volume 14, Issue 4 - Serial Number 54
January 2024
Pages 273-282

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History

  • Receive Date: 18 October 2023
  • Revise Date: 11 December 2023
  • Accept Date: 25 December 2023
  • Publish Date: 21 January 2024

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