Distributed Denial of Service Attacks Detection in Internet of Things Using the Majority Voting Approach
Subject Areas : Electronics Engineering
Habibollah
Mazarei
1
(Department of Computer Engineering, Technical Engineering Faculty, Islamic Azad University, Bushehr branch, Iran)
Marziye
Dadvar
2
(Department of Computer Engineering, Technical Engineering Faculty, Islamic Azad University, Bushehr branch, Iran)
MohammadHadi
Atabakzadeh
3
(Department of Computer Engineering, Technical Engineering Faculty, Islamic Azad University, Bushehr branch, Iran)
Keywords: Intrusion Detection System, Internet of Things, Machine Learning, Distributed Denial of Service Attack, Majoraty Voting,
Abstract :
With the ever-increasing number of Internet of Things devices, their security is becoming a very worrying issue. Weak security measures enable attackers to attack IoT devices. One of these attacks is the distributed denial of service(DDOS) attack. Therefore, the existence of intrusion detection systems in the Internet of Things is of special importance. In this research, the majority voting group approach, which is a subset of machine learning, has been used to detect and predict attacks. The motivation for using this method is to achieve better detection accuracy and a very low false positive rate by combining several machine learning classification algorithms in heterogeneous Internet of Things networks. In this research, the new and improved CICDDOS2019 dataset has been used to evaluate the proposed method. The simulation results show that by applying the majority voting Ensemble method on five attacks from this data set, this method respectively has achieved accuracy of detection 99.9668%, 99.9670%, 100%, 99.9686% and 99.9674% in identifying DNS, NETBIOS, LDAP, UDP and SNMP attacks which better and more stable performance in detecting and predicting attacks have achieved than the basic models .
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_||_[1] J. Alsamiri and K. Alsubhi, "Internet of Things Cyber Attacks Detection using Machine Learning," International Journal of Advanced Computer Science and Applications, vol. 10, no. 12, pp. 627-634, 2019, doi: 10.14569/IJACSA.2019.0101280.
[2] Z. Shah, I. Ullah, H. Li, A. Levula and K. Khurshid, "Blockchain Based Solutions to Mitigate Distributed Denial of Service (DDoS) Attacks in the Internet of Things (IoT): A Survey," Multidisciplinary Digital Publishing Institute Sensors, vol. 22, no. 3, 2022, doi: 10.3390/s22031094.
[3] S. M. Tahsien, H. Karimipour and P. Spachos, "Machine learning based solutions for security of Internet of Things (IoT): A survey," Journal of Network and Computer Applications, vol. 161, 2020, doi: 10.1016/j.jnca.2020.102630 .
[4] M. Shurman, R. Khrais and A. Yateem, "DoS and DDoS Attack Detection Using Deep Learning and IDS," The International Arab Journal of Information Technology, vol. 17, no. 4A, pp. 655-661, 2020, doi: 10.34028/iajit/17/4A/10.
[5] D. K. Sharma, T. Dhankhar, G. Agrawal, S. K. Singh, D. Gupta, J. Nebhen and I. Razzak, "Anomaly detection framework to prevent DDoS attack in fog empowered IoT networks," Ad Hoc Networks, vol. 121, 2021, doi: 10.1016/j.adhoc.2021.102603 .
[6] A. K. Jain, H. Dhawan and B. Sowmiya, "DDoS Detection Using Machine Learning Ensemble," Turkish Journal of Computer and Mathematics Education (TURCOMAT), vol. 12, no. 12, pp. 1647-1655, 2021.
[7] A. Alhowaide, I. Alsmadi and J. Tang, "Ensemble Detection Model for IoT IDS," Internet of Things, vol. 16, p. 100435, 2021, doi: 10.1016/j.iot.2021.100435.
[8] S. Raschka, "Ensemble Methods," in Machine Learning, Department of Statistics University of Wisconsin-Madison, 2019.
[9] R. Alghamdi and M. Bellaiche, "Evaluation and Selection Models for Ensemble Intrusion Detection Systems in IoT," IoT, vol. 3, no. 2, pp. 285-314, 2022, doi: 10.3390/iot3020017.
[10] I. Sharafaldin, A. H. Lashkari, S. Hakak and A. A. Ghorbani, "Developing realistic distributed denial of service (DDoS) attack dataset and taxonomy," International Carnahan Conference on Security Technology (ICCST), 2019, pp. 1-8, doi: 10.1109/CCST.2019.8888419.
[11] M. Almiani, A. AbuGhazleh, Y. Jararweh and A. Razaque, "DDoS detection in 5G enabled IoT networks using deep Kalman backpropagation neural network," International Journal of Machine Learning and Cybernetics, vol. 12, no. 11, pp. 3337-3349, 2021, doi: 10.1007/s13042-021-01323-7 .
[12] F. F. Setiadi, M. W. A. Kesiman and K. Y. E. Aryanto, "Detection of dos attacks using naive bayes method based on internet of things (iot)," in Journal of Physics: Conference Series, vol. 1810, p. 012013, 2021, doi: 10.1088/1742-6596/1810/1/012013
.
[13] A. Churcher, R. Ullah, J. Ahmad, S. u. Rehman, F. Masood, M. Gogate, F. Alqahtani, B. Nour and W. J. Buchanan, "An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks," Multidisciplinary Digital Publishing Institute Sensors, vol. 21, no. 2, p. 446, 2021, doi: 10.3390/s21020446.
[14] S. Chesney, K. Roy and S. Khorsandroo, "Machine Learning Algorithms for Preventing IoT Cybersecurity Attacks," in Proceedings of SAI Intelligent Systems Conference, 2020, pp. 679-686.
[15] P. S. Samom and A. Taggu, "Distributed Denial of Service (DDoS) Attacks Detection: A Machine Learning Approach," Applied Soft Computing and Communication Networks, 2021, pp. 75-87.
[16] Y. W. Chen, J. P. Sheu, Y. C. Kuo and N. V. Cuong, "Design and Implementation of IoT DDoS Attacks Detection System based on Machine Learning," European Conference on Networks and Communications (EuCNC), Dubrovnik, Croatia, 2020, pp. 122-127, doi: 10.1109/EuCNC48522.2020.9200909.
[17] F. Hussain, S. G. Abbas, M. Husnain, U. U. Fayyaz, F. Shahzad and G. A. Shah, "IoT DoS and DDoS Attack Detection using ResNet," IEEE 23rd International Multitopic Conference (INMIC), Bahawalpur, Pakistan, 2020, pp. 1-6, doi: 10.1109/INMIC50486.2020.9318216.
[18] S. Evmorfos, G. Vlachodimitropoulos, N. Bakalos and E. Gelenbe, "Neural Network Architectures for the detection of SYN flood attacks in IoT systems," in Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments, 2020, pp. 1-4, doi: 10.1145/3389189.3398000.
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[20] M. Roopak, G. Y. Tian and J. Chambers, "An Intrusion Detection System Against DDoS Attacks in loT Networks," 10th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, 2020, pp. 0562-0567, doi: 10.1109/CCWC47524.2020.9031206.
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[22] A. Mubarakali, K. Srinivasan, R. Mukhalid, S. C. Jaganathan and N. Marina, "Security challenges in internet of things: Distributed denial of service attack detection using support vector machine-based expert systems," Computational Intelligence, vol. 36, no. 4, pp. 1580-1592, 2020, doi: 10.1111/coin.12293.
[23] P. Gokhale, O. Bhat and S. Bhat, "Introduction to IOT," International Advanced Research Journal in Science, Engineering and Technology, vol. 5, no. 1, pp. 41-44, 2018, doi: 10.17148/IARJSET.2018.517.
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[28] I. Romli, T. Pardamean, S. Butsianto and T. N. Wiyatno, "Naive Bayes Algorithm Implementation Based on Particle Swarm Optimization in Analyzing the Defect Product," Journal of Physics: Conference Series, vol. 1845, no. 1, p. 012020, 2021, doi: 10.1088/1742-6596/1845/1/012020.
[29] T. Xiuyi and G. Yuxia, "Research on Application of Machine Learning in Data Mining," 2018.
[30] A. Abbas, M. A. Khan, S. Latif, M. Ajaz, A. A. Shah and J. Ahmad, "A New Ensemble-Based Intrusion Detection System for Internet of Things," Arabian Journal for Science and Engineering, vol. 47, no. 2, pp. 1805-1819, 2022, doi: 10.1007/s13369-021-06086-5.
[31] M. Hofmann and R. Klinkenberg, RapidMiner: Data mining use cases and business analytics applications, CRC Press, 2016.
[32] D. H. Maulud and A. M. Abdulazeez, "A Review on Linear Regression Comprehensive in Machine Learning," Journal of Applied Science and Technology Trends, vol. 1, no. 4, pp. 140-147, 2020, doi: 10.38094/jastt1457.
[33] Q. Zhang, L. T. Yang, Z. Chen and P. Li, "A survey on deep learning for big data," Information Fusion, vol. 42, pp. 146-157, 2018, doi: 10.1016/j.inffus.2017.10.006.
[34] S. Pardo, Statistical Analysis of Empirical Data, Springer International Publishing, 2020.
[35] M. Aslam, D. Ye, A. Tariq, M. Asad, M. Hanif, D. Ndzi, S. A. Chelloug, M. A. Elaziz, M. A. A. Al-Qaness and S. F. Jilani, "Adaptive Machine Learning Based Distributed Denial-of-Services Attacks Detection and Mitigation System for SDN-Enabled IoT," sensors, vol. 22, no. 7, p. 2697, 2022, doi: 10.3390/s22072697.
[36] P. Golchin, R. Kundel, T. Steuer, R. Hark and R. Steinmetz, "Improving DDoS Attack Detection Leveraging a Multi-aspect Ensemble Feature Selection," NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium, pp. 1-5, 2022, doi: 10.1109/NOMS54207.2022.9789763.
[37] D. Kshirsagar and S. Kumar, "A feature reduction based reflected and exploited DDoS attacks detection system," Journal of Ambient Intelligence and Humanized Computing, vol. 13, no. 1, pp. 393-405, 2022, doi: 10.1007/s12652-021-02907-5.
[38] A. Mahfouz, A. Abuhussein, D. Venugopal and S. Shiva, "Ensemble Classifiers for Network Intrusion Detection Using a Novel Network Attack Dataset," Future Internet, vol. 12, no. 11, p. 180, 2020, doi: 10.3390/fi12110180.
[39] S.-A. N. ALEXANDROPOULOS, S. B. KOTSIANTIS and M. N. VRAHATIS, "Data preprocessing in predictive data mining," The Knowledge Engineering Review, p. 34, 2019, doi: 10.1017/S026988891800036X.
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[41] S. Yadav and S. Shukla, "Analysis of k-fold cross-validation over hold-out validation on colossal datasets for quality classification," IEEE 6th International conference on advanced computing (IACC), 2016, pp. 78-83, doi: 10.1109/IACC.2016.25..
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