Ensembled Gradient Boosting Technique with Decision Tree for Intrusion Detection System


  • V. S. Stency, N. Mohanasundaram, R. Santhosh


Support Vector Machine, Naïve Biase, Adaboost, Convolutional Neural Network, Recurrent Neural Network., Hybrid Classifier, Ensemble Classifier, Ensemble Neural Network


There is a rising need for network attack analysis in today's world as cyber threats and assaults multiply. Cloud computing is chosen by companies all over the world because of the scalability and versatility of its internet-based computer capabilities. Scientists are increasingly concentrating on the security of cloud data, and one of their key priorities is safeguarding hosts, businesses, and data against more sophisticated digital attacks. Numerous approaches have been developed as a consequence of researchers' experiments with Intrusion Detection (ID) architecture during the past few decades. But eventually, the intrusion detection framework won't be able to use these techniques. The goal of this study is to classify whether or not a framework interruption has happened using an ensemble model of an effective gradient-boosting decision tree (EGDT-boost). Using a Gradient Boosting classifier and a Decision tree, this model produced an ensemble classifier. The Decision Tree classifier performs better thanks to the gradient boosting techniques since fewer mistakes are recognised. The suggested classifier is examined in this article together with several other well-established classification methods. In comparison to previous approaches, the suggested model yields better outcomes in terms of Precision, Recall, F-Measure, and Accuracy.


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Bilge, Leyla, and Tudor Dumitraş. "Before we knew it: an empirical study of zero-day attacks in the real world." In Proceedings of the 2012 A.C.M. conference on Computer and communications security, pp. 833-844. 2012.

Holm, Hannes. "Signature-based intrusion detection for zero-day attacks:(not) a closed chapter?." In 2014 47th Hawaii international conference on system sciences, pp. 4895-4904. IEEE, 2014.

Lamba, Anil, Satinderjeet Singh, and Singh Balvinder. "Mitigating zero-day attacks in IoT using a strategic framework." International Journal for Technological Research in Engineering 4, no. 1 (2016).

Zhang, Mengyuan, Lingyu Wang, SushilJajodia, Anoop Singhal, and Massimiliano Albanese. "Network diversity: a security metric for evaluating the resilience of networks against zero-day attacks." IEEE Transactions on Information Forensics and Security 11, no. 5 (2016): 1071-1086.

Portokalidis, Georgios, Asia Slowinska, and Herbert Bos. "Argos: an emulator for fingerprinting zero-day attacks for advertised honeypots with automatic signature generation." ACM SIGOPS Operating Systems Review 40, no. 4 (2006): 15-27.

Boetto, Erik, Maria Pia Fantini, Aldo Gangemi, DavideGolinelli, Manfredi Greco, Andrea Giovanni Nuzzolese, Valentina Presutti, and Flavia Rallo. "Using altmetrics for detecting impactful research in quasi-zero-day time-windows: the case of COVID-19." Scientometrics (2021): 1-27.

Sohi, Soroush M., Jean-Pierre Seifert, and FatemehGanji. "RNNIDS: Enhancing network intrusion detection systems through deep learning." Computers & Security 102 (2021): 102151.

Kamati, Toivo Herman, Dharm Singh Jat, and SaurabhChamotra. "Design and Development of System for Post-infection Attack Behavioral Analysis." In Proceedings of Fifth International Congress on Information and Communication Technology, pp. 554-565. Springer, Singapore, 2021.

Garcia, Norberto, Tomas Alcaniz, Aurora González-Vidal, Jorge Bernal Bernabe, Diego Rivera, and Antonio Skarmeta. "Distributed real-time slowdowns attacks detection over encrypted traffic using artificial intelligence." Journal of Network and Computer Applications 173 (2021): 102871.

Bokka, Raveendranadh, and TamilselvanSadasivam. "Deep Learning Model for Detection of Attacks in the Internet of Things Based Smart Home Environment." In Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, pp. 725-735. Springer, Singapore, 2021.

Singh, Geeta, and NeeluKhare. "A survey of intrusion detection from the perspective of intrusion datasets and machine learning techniques." International Journal of Computers and Applications (2021): 1-11.

Aksoy, Muhammet, OrhanOzdemir, GuneyGuner, BarisBaspinar, and EmreKoyuncu. "Flight Trajectory Pattern Generalization and Abnormal Flight Detection with Generative Adversarial Network." In AIAA Scitech 2021 Forum, p. 0775. 2021.

AdtiyaNurCahyo, RisanuriHidayat, and Dani Adhipta, "Performance Comparison of Intrusion Detection System based Anomaly Detection using Artificial Neural Network and Support Vector Machine", Advances of Science and technology for Society,978-0-7354-1413-6,doi-10.10631/1.4958506,2016.

Salima Omar, AsriNgadi, and Hamid H. Jebur , "Machine Learning Techniques for Anomaly detection: An Overview", Internation Journal of Computer Application,ISSN: 0975-8887, Volume 79-No.2 October, 2013.

Sergay Andropov, Alexei Guirik, Mikhail Budko and Marina Budko, "Network Anomaly Detection using Artificial Neural Network", Open Innovation Association(FRUCT) 20th Conference,2017, ISSN NO:2305-7254,IEEE 2017.

Mrutyunjaya Panda and Manas Ranjan Patra, "Network Intrusion Detection Using Naïve Bayes", International Journal of Computer science and Network Security, Vol. 7, No. 12, December 2007.

Manjiri V. Kotpalliwar and RakhiWajgi, "Classification of Attacks Using Support Vector Machine (SVM) on KDDCUP'99 IDS Database", Fifth International Conference on Communication Systems and Network Technologies, 978-1-4799-1797-6, pp: 987-990, April

Khoei, TalaTalaei, GhilasAissou, When Chen Hu, and Naima Kaabouch. "Ensemble learning methods for anomaly intrusion detection system in smart grid." In 2021 IEEE International Conference on Electro Information Technology (E.I.T.), pp. 129-135. IEEE, 2021.

Tama, BayuAdhi, and Sunghoon Lim. "Ensemble learning for intrusion detection systems: A systematic mapping study and cross-benchmark evaluation." Computer Science Review 39 (2021): 100357.

Deshpande, A., & Sharma, R. (2018). Multilevel Ensemble Classifier using Normalized Feature based Intrusion Detection System. International Journal of Advanced Trends in Computer Science and Engineering, 7(5), 72-76. https://doi.org/10.30534/ijatcse/2018/02752015

Divekar, A., Parekh, M., Savla, V., Mishra, R., &Shirole, M. (2018). Benchmarking datasets for anomaly-based network intrusion detection: KDD CUP 99 alternatives. In IEEE 3rd International Conference on Computing, Communication and Security (ICCCS), 1-8.

Hooshmand, M.K. (2020). Using Ensemble Learning Approach To Identify Rare Cyber-Attacks In Network Traffic Data. In International Conference on Advanced Computer Science and Information Systems (ICACSIS), 141-146.

Mogal, D.G., Ghungrad, S.R., &Bhusare, B.B. (2017). NIDS using machine learning classifiers on UNSW-NB15 and KDDCUP99 datasets. International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), 6(4), 533-537.

Ullah, F., & Babar, M.A. (2018). Architectural tactics for big data cybersecurity analytics systems: a review. Journal of Systems and Software, 151, 81-118.

Yin, C., Zhu, Y., Fei, J., & He, X. (2017). A deep learning approach for intrusion detection using recurrent neural networks. Ieee Access, 5, 21954-21961.

Dua, S., & Du, X. (2016). Data mining and machine learning in cybersecurity. C.R.C. press. Buczak, A.L., &Guven, E. (2015). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications surveys & tutorials, 18(2), 1153-1176.

Anwer, H.M., Farouk, M., & Abdel-Hamid, A. (2018). A framework for efficient network anomaly intrusion detection with features selection. In 9th International Conference on Information and Communication Systems (ICICS), 157-162.

Gharaee, H., &Hosseinvand, H. (2016). A new feature selection I.D.S. based on genetic algorithm and SVM. In 8th International Symposium on Telecommunications (I.S.T.), 139-144.

Belouch, M., El Hadaj, S., &Idhammad, M. (2017). A two-stage classifier approach using reptree algorithm for network intrusion detection. International Journal of Advanced Computer Science and Applications, 8(6), 389-394.

Belouch, M., El Hadaj, S., &Idhammad, M. (2018). Performance evaluation of intrusion detection based on machine learning using Apache Spark. Procedia Computer Science, 127, 1-6.

Idhammad, M., Afdel, K., &Belouch, M. (2017). Dos detection method based on artificial neural networks. International Journal of Advanced Computer Science and Applications, 8(4), 465-471.

Hooshmand, M.K., & Gad, I. (2020). Feature selection approach using ensemble learning for network anomaly detection. CAAI Transactions on Intelligence Technology, 5(4), 283-293.

Sheikhan, M., Jadidi, Z., &Farrokhi, A. (2012). Intrusion detection using reduced-size R.N.N. based on feature grouping. Neural Computing and Applications, 21(6), 1185-1190.

Alom, M.Z., Bontupalli, V., & Taha, T.M. (2015). Intrusion detection using deep belief networks. In National Aerospace and Electronics Conference (NAECON), 339-344.

Moustafa, N. (2017). Designing an online and reliable statistical anomaly detection framework for dealing with large high-speed network traffic (Doctoral dissertation, University of New South Wales, Canberra, Australia).

Moustafa, N., & Slay, J. (2015). A hybird feature selection for network intrusion detection systems: central points and association rules. In Australian Information Warfare Conference, 5-13.

Moustafa, N., & Slay, J. (2016). The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set. Information Security Journal: A Global Perspective, 25(1-3), 18-31.

Dahiya, P., & Srivastava, D.K. (2018). Network intrusion detection in big dataset using spark. Procedia computer science, 132, 253-262.

Heshmati, B., Hashempour, L., Saberi, M.K., Fattahi, A., &Sahebi, S. (2020). Global research trends of public libraries from 1968 to 2017: A bibliometric and visualization analysis. Webology, 17(1), 140-157.

Le, Thi-Thu-Huong, et al. "Classification and Explanation for Intrusion Detection System Based on Ensemble Trees and SHAP Method." Sensors 22.3 (2022): 1154.

Ponmalar, A., and V. Dhanakoti. "An intrusion detection approach using ensemble Support Vector Machine based Chaos Game Optimization algorithm in big data platform." Applied Soft Computing 116 (2022): 108295.

Krishnaveni, Sivamohan, et al. "Network intrusion detection based on ensemble classification and feature selection method for cloud computing." Concurrency and Computation: Practice and Experience 34.11 (2022): e6838.




How to Cite

V. S. Stency. (2024). Ensembled Gradient Boosting Technique with Decision Tree for Intrusion Detection System. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 2571–2583. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5859



Research Article