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A COMPARATIVE EVALUATION OF THE BOOSTING ALGORITHMS FOR NETWORK ATTACK CLASSIFICATION

Year 2022, , 102 - 112, 30.04.2022
https://doi.org/10.46519/ij3dptdi.1030539

Abstract

The security of information resources is an extremely critical problem. The network infrastructure that enables internet access, in particular, may be targeted by attackers from a variety of national and international locations, resulting in losses for institutions that utilize it. Anomaly detection systems, sometimes called Intrusion Detection Systems (IDSs), are designed to identify abnormalities in such networks. The success of IDSs, however, is limited by the algorithms and learning capacity used in the background. Because of the complex behavior of malicious entities, it is critical to adopt effective techniques that assure high performance while being time efficient. The success rate of the boosting algorithms in identifying malicious network traffic was studied in this study. The boosting approach, one of the most used Ensemble Learning techniques, is accepted as a way to cope with this challenge. In this work, Google Colab has been used to model well-known boosting algorithms. The AdaBoost, CatBoost, GradientBoost, LightGBM, and XGBoost models have been applied to the CICID2017 dataset. The performance of the classifiers has been evaluated with accuracy, precision, recall, f1-score, kappa value, ROC curve and AUC. As a result of the investigation, it was discovered that the XGBoost algorithm produced the greatest results in terms of f1-score, with 99.89 percent, and the AUC values were extremely near to 1, with 0.9989. LightGBM and GradientBoost models, on the other hand, have been shown to be less effective in detecting attack types with little data.

References

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  • 2. Kanimozhi, V. and Jacob, T.P, “Artificial Intelligence based Network Intrusion Detection with hyper-parameter optimization tuning on the realistic cyber dataset CSE-CIC-IDS2018 using cloud computing”, ICT Express, Vol. 5, Issue 3, Pages 211-214, 2019.
  • 3. Saranya, T., Sridevi, S., Deisy, C., Chung, T.D., Ahamed, K.M., “Performance Analysis of Machine Learning Algorithms in Intrusion Detection System: A Review”, Third IC on Computing and Network Communications (CoCoNet'19), Trivandrum, 2020.
  • 4. Ghurab, M., Gaphari, G., Alshami, F., Alshamy, R., Othman, S., “A Detailed Analysis of Benchmark Datasets for Network Intrusion Detection System” Asian Journal of Research in Computer Science, Vol. 7, Issue 4, Pages 14-33, 2021.
  • 5. Sharafaldin, I., Lashkari, A., Ghorbani, A., “Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization”, 4th International Conference on Information Systems Security and Privacy, Portugal, 2018.
  • 6. Özekes, S. and Karakoç, E.N., “Makine Öğrenmesi Yöntemleriyle Anormal Ağ Trafiğinin Tespit Edilmesi”, Düzce Üniversitesi Bilim ve Teknoloji Dergisi, Vol. 7, Issue 1, Pages 566-576, 2019.
  • 7. Tama, B.A., Nkenyereye, L., Islam, S.R., Kwak, K.S., “An Enhanced Anomaly Detection in Web Traffic Using a Stack of Classifier Ensemble”, IEEE Access, Vol. 8, Pages 24120 – 24134, 2020.
  • 8. Abdulrahman, A.A. and Ibrahem, M.K., “Toward Constructing a Balanced Intrusion Detection Dataset Based on CICIDS2017”, Samarra Journal of Pure and Applied Science, Vol. 2, Issue 3, Pages 132-142, 2020.
  • 9. Hosseini, S. and Seilani, H., “Anomaly process detection using negative selection algorithm and classification techniques”, Evolving Systems, Vol. 12, Pages 769–778, 2021.
  • 10. Hongle, D., Yan, Z., Gang, K., Lin, Z., Chen, Y.C., “Online ensemble learning algorithm for imbalanced data stream”, Applied Soft Computing, Vol. 107, Pages 1-12, 2021.
  • 11. Schapire, R.E., “The Boosting Approach to Machine Learning an Overview”, In: Denison DD, Hansen MH, Holmes CC et al editors, Nonlinear Estimation and Classification. Lecture Notes in Statistics, Vol. 171, Springer, New York, Pages 1-23, 2003.
  • 12. Pham, X.T. and Ho, T.H., “Using boosting algorithms to predict bank failure: An untold story”, International Review of Economics & Finance, Vol. 76, Pages 40-54, 2021.
  • 13. Shahraki, A., Abbasi, M., Haugen, Q., “Boosting algorithms for network intrusion detection: A comparative evaluation of Real AdaBoost, Gentle AdaBoost and Modest AdaBoost”, Engineering Applications of Artificial Intelligence, Vol. 94, Pages 1-14, 2020.
  • 14. Li, Y., Shi, H., Duan, Z., Liu, H., “Smart wind speed forecasting approach using various boosting algorithms, big multi-step forecasting strategy”, Renewable Energy, Vol. 135, Pages 540-553, 2019.
  • 15. Ma, B., Meng, F., Yan, G., Yan, H., Chai, B., Song, F., “Diagnostic classification of cancers using extreme gradient boosting algorithm and multi-omics data”, Computers in Biology and Medicine, Vol. 121, Pages 1-10, 2020.
  • 16. Abro, A.A, Taşcı, E., Uğur, A.A., “Stacking-based Ensemble Learning Method for Outlier Detection”, Balkan Journal of Electrical & Computer Engineering, Vol. 8, Issue 2, Pages 191-185, 2020.
  • 17. Wen, L., Hughes, M., “Coastal Wetland Mapping Using Ensemble Learning Algorithms: A Comparative Study of Bagging, Boosting and Stacking Techniques”, Remote Sensing, Vol. 12, Issue 10, Pages 1-18, 2020.
  • 18. Xia, T., Zhuo, P., Xiao, L., Du, S., Wang, D., Lifeng, X. “Multi-stage fault diagnosis framework for rolling bearing based on OHF Elman AdaBoost-Bagging algorithm”, Neurocomputing, Vol. 433, Pages 237-251, 2021.
  • 19. Andiojaya, A. and Demirhan, H., “A bagging algorithm for the imputation of missing values in time series”, Expert Systems with Applications, Vol. 129, Pages 10-26, 2019.
  • 20. Yin, S., Liu, H., Duan, Z., “Hourly PM2.5 concentrations multi-step forecasting method based on extreme learning machine, boosting algorithm and error correction model”, Digital Signal Processing, Vol. 118, Pages 1-21, 2021.
  • 21. Freund, Y. and Schapire, R.E., “A decision-theoretic generalization of on- line learning and an application to boosting”, Journal of Computer and System Sciences, Vol. 55, Issue 1, Pages 119-139, 1997.
  • 22. Chengsheng, T., Huacheng, L., Xu, B., “AdaBoost typical Algorithm and its application research”, MATEC Web of Conferences, Vol. 139, Issue 2, France, 2017.
  • 23. Qi, C., Wang, Y., Tian, W., Wang, Q., “Multiple kernel boosting framework based on information measure for classification”, Chaos, Solutions and Fractals, Vol. 89, Pages 175-186, 2016.
  • 24. Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V., Gulin, A., “CatBoost: unbiased boosting with categorical features”, NeurIPS - 32nd Conference on Neural Information Processing Systems, Montreal, 2018.
  • 25. Friedman J.H., “Greedy function approximation: a gradient boosting machine”, Annals of statistics, Vol. 29, Issue 5, Page s1189-1232, 2001.
  • 26. Kearns, M. and Valiant, L., “Cryptographic limitations on learning Boolean formulae and finite automata”, Journal of the ACM, Vol. 41, Issue 1, Pages 67-95, 1994.
  • 27. Friedman, J.H. “Stochastic gradient boosting”, Computational Statistics & Data Analysis, Vol. 38, Issue 4, Page 367-378, 2002.
  • 28. Dahiya, N., Saini, B., Chalak, H.D., “Gradient boosting-based regression modelling for estimating the time period of the irregular precast concrete structural system with cross bracing”, Journal of King Saud University - Engineering Sciences, Pages 1-8, 2021.
  • 29. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., Liu, T.Y., “LightGBM: a highly efficient gradient boosting decision tree”, NIPS'17: Proceedings of the 31st International Conference on Neural Information Processing Systems, Curran Associates Inc. California, 2017.
  • 30. Shehadeh, A., Alshboul, O., Al Mamlook, R.E., Hamedat, O., “Machine learning models for predicting the residual value of heavy construction equipment: An evaluation of modified decision tree, LightGBM, and XGBoost regression”, Automation in Construction, Vol. 129, Pages 1-16, 2021.
  • 31. Chen, T. and Guestrin, C., “XGboost: A scalable tree boosting system”, 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Pages 785–794, San Francisco, 2016.
  • 32. Ma, J., Zhongqi, Y., Qu, Y., Xu, J., Cao, Y., “Application of the XGBoost Machine Learning Method in PM2.5 Prediction: A Case Study of Shanghai”, Aerosol and Air Quality Research, Vol. 20, Issue 1, Pages 128-138, 2019.
  • 33. Sharma, N.V. and Yadav, N.S., “An optimal intrusion detection system using recursive feature elimination and ensemble of classifiers”, Microprocessors and Microsystems, Vol. 85, Pages 1-11, 2021.
  • 34. Aksoy, B., Usta, U., Karadağ, G., Kaya, A.R., Ömür, M., “Classification of Environmental Sounds with Deep Learning”, Advances in Artificial Intelligence Research, Vol. 2, Issue 1, Pages 20-28, 2022.
  • 35. Aksoy, B. and Salman, O.K.M., “Detection of COVID-19 Disease in Chest X-Ray Images with capsul networks: application with cloud computing”, Journal of Experimental & Theoretical Artificial Intelligence, Vol. 33, Issue 3, Pages 527-541, 2021.
Year 2022, , 102 - 112, 30.04.2022
https://doi.org/10.46519/ij3dptdi.1030539

Abstract

References

  • 1. Perez, S.I., Moral-Rubio, S., Criado, R., “A new approach to combine multiplex networks and time series attributes: Building intrusion detection systems (IDS) in cybersecurity”, Chaos, Solutions and Fractals, Vol. 150, Pages 1-11, 2021.
  • 2. Kanimozhi, V. and Jacob, T.P, “Artificial Intelligence based Network Intrusion Detection with hyper-parameter optimization tuning on the realistic cyber dataset CSE-CIC-IDS2018 using cloud computing”, ICT Express, Vol. 5, Issue 3, Pages 211-214, 2019.
  • 3. Saranya, T., Sridevi, S., Deisy, C., Chung, T.D., Ahamed, K.M., “Performance Analysis of Machine Learning Algorithms in Intrusion Detection System: A Review”, Third IC on Computing and Network Communications (CoCoNet'19), Trivandrum, 2020.
  • 4. Ghurab, M., Gaphari, G., Alshami, F., Alshamy, R., Othman, S., “A Detailed Analysis of Benchmark Datasets for Network Intrusion Detection System” Asian Journal of Research in Computer Science, Vol. 7, Issue 4, Pages 14-33, 2021.
  • 5. Sharafaldin, I., Lashkari, A., Ghorbani, A., “Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization”, 4th International Conference on Information Systems Security and Privacy, Portugal, 2018.
  • 6. Özekes, S. and Karakoç, E.N., “Makine Öğrenmesi Yöntemleriyle Anormal Ağ Trafiğinin Tespit Edilmesi”, Düzce Üniversitesi Bilim ve Teknoloji Dergisi, Vol. 7, Issue 1, Pages 566-576, 2019.
  • 7. Tama, B.A., Nkenyereye, L., Islam, S.R., Kwak, K.S., “An Enhanced Anomaly Detection in Web Traffic Using a Stack of Classifier Ensemble”, IEEE Access, Vol. 8, Pages 24120 – 24134, 2020.
  • 8. Abdulrahman, A.A. and Ibrahem, M.K., “Toward Constructing a Balanced Intrusion Detection Dataset Based on CICIDS2017”, Samarra Journal of Pure and Applied Science, Vol. 2, Issue 3, Pages 132-142, 2020.
  • 9. Hosseini, S. and Seilani, H., “Anomaly process detection using negative selection algorithm and classification techniques”, Evolving Systems, Vol. 12, Pages 769–778, 2021.
  • 10. Hongle, D., Yan, Z., Gang, K., Lin, Z., Chen, Y.C., “Online ensemble learning algorithm for imbalanced data stream”, Applied Soft Computing, Vol. 107, Pages 1-12, 2021.
  • 11. Schapire, R.E., “The Boosting Approach to Machine Learning an Overview”, In: Denison DD, Hansen MH, Holmes CC et al editors, Nonlinear Estimation and Classification. Lecture Notes in Statistics, Vol. 171, Springer, New York, Pages 1-23, 2003.
  • 12. Pham, X.T. and Ho, T.H., “Using boosting algorithms to predict bank failure: An untold story”, International Review of Economics & Finance, Vol. 76, Pages 40-54, 2021.
  • 13. Shahraki, A., Abbasi, M., Haugen, Q., “Boosting algorithms for network intrusion detection: A comparative evaluation of Real AdaBoost, Gentle AdaBoost and Modest AdaBoost”, Engineering Applications of Artificial Intelligence, Vol. 94, Pages 1-14, 2020.
  • 14. Li, Y., Shi, H., Duan, Z., Liu, H., “Smart wind speed forecasting approach using various boosting algorithms, big multi-step forecasting strategy”, Renewable Energy, Vol. 135, Pages 540-553, 2019.
  • 15. Ma, B., Meng, F., Yan, G., Yan, H., Chai, B., Song, F., “Diagnostic classification of cancers using extreme gradient boosting algorithm and multi-omics data”, Computers in Biology and Medicine, Vol. 121, Pages 1-10, 2020.
  • 16. Abro, A.A, Taşcı, E., Uğur, A.A., “Stacking-based Ensemble Learning Method for Outlier Detection”, Balkan Journal of Electrical & Computer Engineering, Vol. 8, Issue 2, Pages 191-185, 2020.
  • 17. Wen, L., Hughes, M., “Coastal Wetland Mapping Using Ensemble Learning Algorithms: A Comparative Study of Bagging, Boosting and Stacking Techniques”, Remote Sensing, Vol. 12, Issue 10, Pages 1-18, 2020.
  • 18. Xia, T., Zhuo, P., Xiao, L., Du, S., Wang, D., Lifeng, X. “Multi-stage fault diagnosis framework for rolling bearing based on OHF Elman AdaBoost-Bagging algorithm”, Neurocomputing, Vol. 433, Pages 237-251, 2021.
  • 19. Andiojaya, A. and Demirhan, H., “A bagging algorithm for the imputation of missing values in time series”, Expert Systems with Applications, Vol. 129, Pages 10-26, 2019.
  • 20. Yin, S., Liu, H., Duan, Z., “Hourly PM2.5 concentrations multi-step forecasting method based on extreme learning machine, boosting algorithm and error correction model”, Digital Signal Processing, Vol. 118, Pages 1-21, 2021.
  • 21. Freund, Y. and Schapire, R.E., “A decision-theoretic generalization of on- line learning and an application to boosting”, Journal of Computer and System Sciences, Vol. 55, Issue 1, Pages 119-139, 1997.
  • 22. Chengsheng, T., Huacheng, L., Xu, B., “AdaBoost typical Algorithm and its application research”, MATEC Web of Conferences, Vol. 139, Issue 2, France, 2017.
  • 23. Qi, C., Wang, Y., Tian, W., Wang, Q., “Multiple kernel boosting framework based on information measure for classification”, Chaos, Solutions and Fractals, Vol. 89, Pages 175-186, 2016.
  • 24. Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V., Gulin, A., “CatBoost: unbiased boosting with categorical features”, NeurIPS - 32nd Conference on Neural Information Processing Systems, Montreal, 2018.
  • 25. Friedman J.H., “Greedy function approximation: a gradient boosting machine”, Annals of statistics, Vol. 29, Issue 5, Page s1189-1232, 2001.
  • 26. Kearns, M. and Valiant, L., “Cryptographic limitations on learning Boolean formulae and finite automata”, Journal of the ACM, Vol. 41, Issue 1, Pages 67-95, 1994.
  • 27. Friedman, J.H. “Stochastic gradient boosting”, Computational Statistics & Data Analysis, Vol. 38, Issue 4, Page 367-378, 2002.
  • 28. Dahiya, N., Saini, B., Chalak, H.D., “Gradient boosting-based regression modelling for estimating the time period of the irregular precast concrete structural system with cross bracing”, Journal of King Saud University - Engineering Sciences, Pages 1-8, 2021.
  • 29. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., Liu, T.Y., “LightGBM: a highly efficient gradient boosting decision tree”, NIPS'17: Proceedings of the 31st International Conference on Neural Information Processing Systems, Curran Associates Inc. California, 2017.
  • 30. Shehadeh, A., Alshboul, O., Al Mamlook, R.E., Hamedat, O., “Machine learning models for predicting the residual value of heavy construction equipment: An evaluation of modified decision tree, LightGBM, and XGBoost regression”, Automation in Construction, Vol. 129, Pages 1-16, 2021.
  • 31. Chen, T. and Guestrin, C., “XGboost: A scalable tree boosting system”, 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Pages 785–794, San Francisco, 2016.
  • 32. Ma, J., Zhongqi, Y., Qu, Y., Xu, J., Cao, Y., “Application of the XGBoost Machine Learning Method in PM2.5 Prediction: A Case Study of Shanghai”, Aerosol and Air Quality Research, Vol. 20, Issue 1, Pages 128-138, 2019.
  • 33. Sharma, N.V. and Yadav, N.S., “An optimal intrusion detection system using recursive feature elimination and ensemble of classifiers”, Microprocessors and Microsystems, Vol. 85, Pages 1-11, 2021.
  • 34. Aksoy, B., Usta, U., Karadağ, G., Kaya, A.R., Ömür, M., “Classification of Environmental Sounds with Deep Learning”, Advances in Artificial Intelligence Research, Vol. 2, Issue 1, Pages 20-28, 2022.
  • 35. Aksoy, B. and Salman, O.K.M., “Detection of COVID-19 Disease in Chest X-Ray Images with capsul networks: application with cloud computing”, Journal of Experimental & Theoretical Artificial Intelligence, Vol. 33, Issue 3, Pages 527-541, 2021.
There are 35 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence
Journal Section Research Article
Authors

Koray Çoşkun 0000-0001-7859-0547

Gürcan Çetin 0000-0003-3186-2781

Publication Date April 30, 2022
Submission Date November 30, 2021
Published in Issue Year 2022

Cite

APA Çoşkun, K., & Çetin, G. (2022). A COMPARATIVE EVALUATION OF THE BOOSTING ALGORITHMS FOR NETWORK ATTACK CLASSIFICATION. International Journal of 3D Printing Technologies and Digital Industry, 6(1), 102-112. https://doi.org/10.46519/ij3dptdi.1030539
AMA Çoşkun K, Çetin G. A COMPARATIVE EVALUATION OF THE BOOSTING ALGORITHMS FOR NETWORK ATTACK CLASSIFICATION. IJ3DPTDI. April 2022;6(1):102-112. doi:10.46519/ij3dptdi.1030539
Chicago Çoşkun, Koray, and Gürcan Çetin. “A COMPARATIVE EVALUATION OF THE BOOSTING ALGORITHMS FOR NETWORK ATTACK CLASSIFICATION”. International Journal of 3D Printing Technologies and Digital Industry 6, no. 1 (April 2022): 102-12. https://doi.org/10.46519/ij3dptdi.1030539.
EndNote Çoşkun K, Çetin G (April 1, 2022) A COMPARATIVE EVALUATION OF THE BOOSTING ALGORITHMS FOR NETWORK ATTACK CLASSIFICATION. International Journal of 3D Printing Technologies and Digital Industry 6 1 102–112.
IEEE K. Çoşkun and G. Çetin, “A COMPARATIVE EVALUATION OF THE BOOSTING ALGORITHMS FOR NETWORK ATTACK CLASSIFICATION”, IJ3DPTDI, vol. 6, no. 1, pp. 102–112, 2022, doi: 10.46519/ij3dptdi.1030539.
ISNAD Çoşkun, Koray - Çetin, Gürcan. “A COMPARATIVE EVALUATION OF THE BOOSTING ALGORITHMS FOR NETWORK ATTACK CLASSIFICATION”. International Journal of 3D Printing Technologies and Digital Industry 6/1 (April 2022), 102-112. https://doi.org/10.46519/ij3dptdi.1030539.
JAMA Çoşkun K, Çetin G. A COMPARATIVE EVALUATION OF THE BOOSTING ALGORITHMS FOR NETWORK ATTACK CLASSIFICATION. IJ3DPTDI. 2022;6:102–112.
MLA Çoşkun, Koray and Gürcan Çetin. “A COMPARATIVE EVALUATION OF THE BOOSTING ALGORITHMS FOR NETWORK ATTACK CLASSIFICATION”. International Journal of 3D Printing Technologies and Digital Industry, vol. 6, no. 1, 2022, pp. 102-1, doi:10.46519/ij3dptdi.1030539.
Vancouver Çoşkun K, Çetin G. A COMPARATIVE EVALUATION OF THE BOOSTING ALGORITHMS FOR NETWORK ATTACK CLASSIFICATION. IJ3DPTDI. 2022;6(1):102-1.

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