Abstract
A distributed denial of service (DDoS) attack targets at hindering authorized individuals from accessing a server or website by flooding it with traffic from many sources. To avoid a DDoS attack from damaging the target system, detection is required. The system becomes unsafe as a result of this attack. The paper provides an ensemble machine learning technique-based DDoS attack detection model. To choose the most significant characteristics from the Kaggle dataset, three feature selection techniques-ANOVA, mutual information, and feature importance are applied. The traditional machine learning methods K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Naive Bayes (NB) are then used with the chosen features. Then, four ensemble methods were created by combining three models from these four traditional machine learning algorithm using hard ensemble voting. By evaluating precision, recall, F1-score, and accuracy, the experiment’s outcome is determined. After all the experiments, the result shows that the features selected by feature importance technique give the highest accuracy, 98.86% with the ensemble voting classifier by the combinations of KNN, SVM, and DT.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Savita TS, Sharma MR (2023) DDoS attack detection using soft voting classifier. J Comput 52(3):66–79
Anthi E, Williams L, Javed A, Burnap P (2021) Hardening machine learning denial of service (DoS) defences against adversarial attacks in IoT smart home networks. Comput Secur 108:102352
Kumar K, Barver A (2021) A DDoS attack detection using deep learning—a review. IJFMR Int J Multidiscip Res 5(3):1–11
Samat NA (2022) Intrusion detection system: challenges in network security and machine learning. Easy Chair Preprint no. 8578
Tuan TA, Long HV, Son LH, Kumar R, Priyadarshini I, Son NTK (2020) Performance evaluation of Botnet DDoS attack detection using machine learning. Evol Intell 13:283–294
Polat H, Polat O, Cetin A (2020) Detecting DDoS attacks in software-defined networks through feature selection methods and machine learning models. Sustainability 12(3):1035. https://doi.org/10.3390/su12031035
Azmi MAH, Foozy CFM, Sukri KAM, Abdullah NA, Hamid IRA, Amnur H (2021) Feature selection approach to detect DDoS attack using machine learning algorithms. JOIV: Int J Inform Visual 5(4):395–401. https://doi.org/10.30630/joiv.5.4.734
Beulah M, Pitchai Manickam B (2022) Detection of DDoS attack using ensemble machine learning techniques. In: Soft computing for security applications: proceedings of ICSCS 2021. Springer, pp 889–903
Janiesch C, Zschech P, Heinrich K (2021) Machine learning and deep learning. Electron Markets 31(3):685–695
Liu H, Lang B (2019) Machine learning and deep learning methods for intrusion detection systems: a survey. Appl Sci 9(20):4396
Tekleselassie H (2021) A deep learning approach for DDoS attack detection using supervised learning. In: MATEC web of conferences, vol 348. EDP Sciences, p 01012. https://doi.org/10.1051/matecconf/202134801012
Bhardwaj A, Mangat V, Vig R (2020) Hyperband tuned deep neural network with well-posed stacked sparse autoencoder for detection of DDoS attacks in cloud. IEEE Access 8:181916–181929. https://doi.org/10.1109/ACCESS.2020.3028690
de Araujo PHHN, Silva A, Junior NF, Cabrini F, Santiago A, Guelfi A, Kofuji S (2021) Impact of feature selection methods on the classification of DDoS attacks using XGBoost. J Commun Inf Syst 36(1):200–214. https://doi.org/10.14209/jcis.2021.22
Kumar YV, Kamatchi K (2020) Anomaly based network intrusion detection using ensemble machine learning technique. Int J Res Eng 3:290–297
Krishna R. Datasets/Kaggle. https://www.kaggle.com/datasets/ramakrishna0810/ddos-classification. Accessed 10 Jul 2023
Kabir MH, Mahmood S, Al Shiam A, Musa Miah AS, Shin J, Molla MKI (2023) Investigating feature selection techniques to enhance the performance of EEG-based motor imagery tasks classification. Mathematics 11(8):1921. https://doi.org/10.3390/math11081921
Bagherzadeh F, Mehrani MJ, Basirifard M, Roostaei J (2021) Comparative study on total nitrogen prediction in wastewater treatment plant and effect of various feature selection methods on machine learning algorithms’ performance. J Water Process Eng 41:102033. https://doi.org/10.1016/j.jwpe.2021.102033
Zaini NAM, Awang MK (2023) Hybrid feature selection algorithm and ensemble stacking for heart disease prediction. Int J Adv Comput Sci Appl 14(2):158–165
Azhar M, Ullah S, Ullah K, Shah H, Namoun A, Rahman KU (2023) A three-dimensional real-time gait-based age detection system using machine learning. CMC Comput Mater Contin 75(1):165–182. https://doi.org/10.32604/cmc.2023.034605
Ma G, Zhang J, Liu J, Wang L, Yu Y (2023) A multi-parameter fusion method for cuffless continuous blood pressure estimation based on electrocardiogram and photoplethysmogram. Micromachines 14(4):804
Hashim MS, Yassin AA. Using Pearson correlation and mutual information (PC-MI) to select features for accurate breast cancer diagnosis based on a soft voting classifier. Iraqi J Electr Electron Eng 43–53 (2023). https://doi.org/10.37917/ijeee.19.2.6
Pierzyna M, Saathof R, Basu S (2023) Pi-ML: a dimensional analysis-based machine learning parameterization of optical turbulence in the atmospheric surface layer. ar**v—PHYS—Atmospheric and Oceanic Physics, pp 1–8. ar**v:2304.12177
Tikhe SA, Rana DP (2023) Fine-tuned predictive models for forecasting severity level of COVID-19 patient using epidemiological data. In: Frontiers of ICT in healthcare: proceedings of EAIT 2022. Springer, pp 431–442
Akhtar MS, Feng T (2022) Comparison of classification model for the detection of cyber-attack using ensemble learning models. EAI Endors Trans Scalable Inf Syst 9(5). https://doi.org/10.4108/eai.1-2-2022.173293
Solano ES, Affonso CM (2023) Solar irradiation forecasting using ensemble voting based on machine learning algorithms. Sustainability 15(10):7943. https://doi.org/10.3390/su15107943
Atif M, Anwer F, Talib F (2022) An ensemble learning approach for effective prediction of diabetes mellitus using hard voting classifier. Indian J Sci Technol 15(39):1978–1986. https://doi.org/10.17485/IJST/v15i39.1520
Karim A, Shahroz M, Mustofa K, Belhaouari SB, Joga SRK (2023) Phishing detection system through hybrid machine learning based on URL. IEEE Access 11:36805–36822. https://doi.org/10.1109/ACCESS.2023.3252366
Söğüt E, Erdem OA (2023) A multi-model proposal for classification and detection of DDoS attacks on SCADA systems. Appl Sci 13(10):5993. https://doi.org/10.3390/app13105993
Saravanakumar G, Naveen VM, Koushik PH, Sneha C et al (2023) A DDoS attack categorization and prediction method based on machine learning. J Popul Ther Clin Pharmacol 30(9):300–307. https://doi.org/10.47750/jptcp.2023.30.09.030
Das S, Venugopal D, Shiva S (2020) A holistic approach for detecting DDoS attacks by using ensemble unsupervised machine learning. In: Advances in information and communication: proceedings of the 2020 future of information and communication conference (FICC), vol 2. Springer, pp 721–738
Das S, Mahfouz AM, Venugopal D, Shiva S (2019) DDoS intrusion detection through machine learning ensemble. In: 2019 IEEE 19th international conference on software quality, reliability and security companion (QRS-C). IEEE, pp 471–477. https://doi.org/10.1109/QRS-C.2019.00090
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Anis, A., Shohrab Hossain, M. (2024). DDoS Attack Detection Using Ensemble Machine Learning. In: Pandit, M., Gaur, M.K., Kumar, S. (eds) Artificial Intelligence and Sustainable Computing. ICSISCET 2023. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-97-0327-2_39
Download citation
DOI: https://doi.org/10.1007/978-981-97-0327-2_39
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-0326-5
Online ISBN: 978-981-97-0327-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)