تشخیص حملات منع سرویس توزیع شده در اینترنت اشیاء با استفاده از رویکرد رأی گیری اکثریت
محورهای موضوعی : مهندسی الکترونیک
حبیب اله مزارعی
1
(گروه کامپیوتر، واحد بوشهر، دانشگاه آزاد اسلامی، بوشهر،ایران)
مرضیه دادور
2
(گروه کامپیوتر، واحد بوشهر، دانشگاه آزاد اسلامی، بوشهر،ایران)
محمدهادی اتابک زاده
3
(گروه ریاضی، واحد بوشهر، دانشگاه آزاد اسلامی، بوشهر،ایران)
کلید واژه: سیستم تشخیص نفوذ, اینترنت اشیاء, رأی گیری اکثریت, یادگیری ماشین, حمله منع سرویس توزیع شده,
چکیده مقاله :
با افزایش روزافزون دستگاه های اینترنت اشیاء، امنیت آنها به موضوعی بسیار نگران کننده تبدیل شده است. اقدامات امنیتی ضعیف، مهاجمان را قادر می سازد تا دستگاه‌های اینترنت اشیاء را مورد حمله قرار دهند. یکی از این حملات، حمله منع سرویس توزیع شده است. بنابراین وجود سیستمهای تشخیص نفوذ در اینترنت اشیاء، از اهمیت ویژه ای برخوردار است. در این پژوهش، از رویکرد گروهی رأی گیری اکثریت که زیرمجموعه یادگیری ماشین است جهت تشخیص و پیش بینی حملات استفاده شده است. انگیزه استفاده از این روش، دستیابی به دقت تشخیص بهتر و نرخ مثبت کاذب بسیار پایین با ترکیب چند الگوریتم طبقه بندی یادگیری ماشین، در شبکه‌های ناهمگن اینترنت اشیاء است. در این پژوهش از مجموعه داده جدید و بهبود یافته CICDDOS2019 برای ارزیابی روش پیشنهادی استفاده شده است. نتایج شبیه‌سازی نشان می‌دهد که با اعمال روش گروهی رأی گیری اکثریت روی پنج حمله از این مجموعه داده، این روش به ترتیب به دقت تشخیص 99.9668%، 99.9670%، 100%، 99.9686% و 99.9674% در شناسایی حملات DNS، NETBIOS، LDAP، UDP و SNMP دست یافت که نسبت به مدلهای پایه، عملکرد بهتر و پایدارتری در تشخیص و پیش بینی حملات، از خود نشان داده است.
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 .
[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.
[19] T. Saranya, S. Sridevi, C. Deisy, T. D. Chung and M. A. Khan, "Performance Analysis of Machine Learning Algorithms in Intrusion Detection System: A Review," Procedia Computer Science, vol. 171, pp. 1251-1260, 2020, doi: 10.1016/j.procs.2020.04.133.
[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.
[21] A. Dushimimana, T. Tao, R. Kindong and A. Nishyirimbere, "Bi-directional Recurrent Neural network for Intrusion Detection System (IDS) in the internet of things (IoT)," International Journal of Advanced Engineering Research and Science (IJAERS), vol. 7, no. 3, pp. 524-539, 2020, doi: 10.22161/ijaers.73.68.
[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.
[24] J. S. Kumar and D. R. Patel, "A Survey on Internet of Things: Security and Privacy Issues," International Journal of Computer Applications, vol. 90, no. 11, 2014, doi: 10.5120/15764-4454.
[25] A. Seifousadati, S. Ghasemshirazi and M. Fathian, "A Machine Learning Approach for DDoS Detection on IoT Devices," Computer Science, 2021, doi: 10.48550/arXiv.2110.14911.
[26] A. Khraisat, I. Gondal, P. Vamplew and J. Kamruzzaman, "Survey of intrusion detection systems: techniques, datasets and challenges," Cybersecurity, vol. 2, no. 1, pp. 1-22, 2019, doi: 10.1186/s42400-019-0038-7.
[27] S. Sambangi and L. Gondi, "A Machine Learning Approach for DDoS (Distributed Denial of Service) Attack Detection Using Multiple Linear Regression," Multidisciplinary Digital Publishing Institute Proceedings, vol. 63, no. 1, p. 51, 2020, doi: 10.3390/proceedings2020063051.
[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.
[40] J. Han, M. Kamber and J. Pei, Data mining concepts and techniques, ELSEVIER, 2016.
[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..
[42] J. Brownlee, Imbalanced classification with Python: better metrics, balance skewed classes, cost-sensitive learning, Machine Learning Mastery, 2020.
[43] S. Senthilnathan, "USEFULNESS OF CORRELATION ANALYSIS," Available at SSRN 3416918, 2019.
[44] K. N. Mallikarjunan, A. Bhuvaneshwaran, K. Sundarakantham and S. M. Shalinie, "DDAM: Detecting DDoS Attacks Using Machine Learning Approach," Computational Intelligence: Theories, Applications and Future Directions,vol. 1, pp. 261-273, 2019, doi: 10.1007/978-981-13-1132-1_21.
[45] D. Chicco, M. . J. Warrens and G. Jurman, "The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation," PeerJ Computer Science, vol. 7, p. e623, 2021, doi: 10.7717/peerj-cs.623.
[46] B. K. Chae, "The evolution of the Internet of Things (IoT): A computational text analysis," Telecommunications Policy, vol. 43, no. 10, p. 101848, 2019, doi: 10.1016/j.telpol.2019.101848.
[47] V. R. Joseph, "Optimal ratio for data splitting," Statistical Analysis and Data Mining: The ASA Data Science Journal, 2022.
[48] F. Rodríguez-Torres, J. F. Martínez-Trinidad and J. A. Carrasco-Ochoa, "An Oversampling Method for Class Imbalance Problems on Large Datasets," Applied Sciences, vol. 12, no. 2, p. 3424, 2022, doi: 10.3390/app12073424.
[49] T. Hasanin, T. M. Khoshgoftaar, J. L. Leevy and R. A. Bauder, "Severely imbalanced Big Data challenges: investigating data sampling approaches," Journal of Big Data, vol. 6, no. 1, pp. 1-25, 2019, doi: 10.1186/s40537-019-0274-4.
[50] M. Á. Canela, I. Alegre and A. Ibarra, "Out-of-Sample Validation," in Quantitative Methods for Management, Springer, 2019, pp. 83-89, doi: 10.1007/978-3-030-17554-2_9.
[51] H. Roohi and Q. Usman, "An Approach to Detect Spam Emails by Using Majority Voting," in Proceedings of the International Conference on Data Mining, Internet Computing and Big Data (BigData2014), 2014.
[52] A. Sifaunajah and R. D. Wahyuningtyas, "The Classification of Prospective Students with Rapid Miner," NEWTON: Networking and Information Technology, vol. 2, no. 2, pp. 72-78, 2022.
[53] S. Bashir, I. U. Khattak, A. Khan, F. H. Khan, A. Gani and M. Shiraz, "A Novel Feature Selection Method for Classification of Medical Data Using Filters, Wrappers, and Embedded Approaches," Complexity, vol. 2022, 2022, doi: 10.1155/2022/8190814.
[54] R. Rizal, M. Martanto and Y. A. Wijaya, "ANALISA DATASET SOFTWARE DEFINED NETWORK INTRUSION MENGGUNAKAN ALGORITMA DEEP LEARNING H2O," JATI (Jurnal Mahasiswa Teknik Informatika, vol. 6, no. 2, pp. 747-757, 2022, doi: 10.36040/jati.v6i2.5724.
[55] D. J. Arunadevi, S. Ramya and M. R. Raja, "A study of classification algorithms using RapidMiner," International Journal of Pure and Applied Mathematics, vol. 119, no. 12, pp. 15977-15988, 2018.
[56] Y. B. Lasotte, E. J. Garba, Y. M. Malgwi and M. A. Buhari, "An Ensemble Machine Learning Approach for Fake News Detection and Classification Using a Soft Voting Classifier," European Journal of Electrical Engineering and Computer Science, vol. 6, no. 2, pp. 1-7, 2022, doi: 10.24018/ejece.2022.6.2.409.
[57] O. Thorat, N. Parekh and R. Mangrulkar, "TaxoDaCML: Taxonomy based Divide and Conquer using machine learning approach for DDoS attack classification," International Journal of Information Management Data Insights, vol. 1, no. 2, p. 100048, 2021, doi: 10.1016/j.jjimei.2021.100048.
[58] A. Verma and V. Ranga, "Machine Learning Based Intrusion Detection Systems for IoT Applications," Wireless Personal Communications, vol. 111, pp. 2287-2310, 2019, doi: 10.1007/s11277-019-06986-8.
_||_[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.
[19] T. Saranya, S. Sridevi, C. Deisy, T. D. Chung and M. A. Khan, "Performance Analysis of Machine Learning Algorithms in Intrusion Detection System: A Review," Procedia Computer Science, vol. 171, pp. 1251-1260, 2020, doi: 10.1016/j.procs.2020.04.133.
[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.
[21] A. Dushimimana, T. Tao, R. Kindong and A. Nishyirimbere, "Bi-directional Recurrent Neural network for Intrusion Detection System (IDS) in the internet of things (IoT)," International Journal of Advanced Engineering Research and Science (IJAERS), vol. 7, no. 3, pp. 524-539, 2020, doi: 10.22161/ijaers.73.68.
[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.
[24] J. S. Kumar and D. R. Patel, "A Survey on Internet of Things: Security and Privacy Issues," International Journal of Computer Applications, vol. 90, no. 11, 2014, doi: 10.5120/15764-4454.
[25] A. Seifousadati, S. Ghasemshirazi and M. Fathian, "A Machine Learning Approach for DDoS Detection on IoT Devices," Computer Science, 2021, doi: 10.48550/arXiv.2110.14911.
[26] A. Khraisat, I. Gondal, P. Vamplew and J. Kamruzzaman, "Survey of intrusion detection systems: techniques, datasets and challenges," Cybersecurity, vol. 2, no. 1, pp. 1-22, 2019, doi: 10.1186/s42400-019-0038-7.
[27] S. Sambangi and L. Gondi, "A Machine Learning Approach for DDoS (Distributed Denial of Service) Attack Detection Using Multiple Linear Regression," Multidisciplinary Digital Publishing Institute Proceedings, vol. 63, no. 1, p. 51, 2020, doi: 10.3390/proceedings2020063051.
[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.
[40] J. Han, M. Kamber and J. Pei, Data mining concepts and techniques, ELSEVIER, 2016.
[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..
[42] J. Brownlee, Imbalanced classification with Python: better metrics, balance skewed classes, cost-sensitive learning, Machine Learning Mastery, 2020.
[43] S. Senthilnathan, "USEFULNESS OF CORRELATION ANALYSIS," Available at SSRN 3416918, 2019.
[44] K. N. Mallikarjunan, A. Bhuvaneshwaran, K. Sundarakantham and S. M. Shalinie, "DDAM: Detecting DDoS Attacks Using Machine Learning Approach," Computational Intelligence: Theories, Applications and Future Directions,vol. 1, pp. 261-273, 2019, doi: 10.1007/978-981-13-1132-1_21.
[45] D. Chicco, M. . J. Warrens and G. Jurman, "The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation," PeerJ Computer Science, vol. 7, p. e623, 2021, doi: 10.7717/peerj-cs.623.
[46] B. K. Chae, "The evolution of the Internet of Things (IoT): A computational text analysis," Telecommunications Policy, vol. 43, no. 10, p. 101848, 2019, doi: 10.1016/j.telpol.2019.101848.
[47] V. R. Joseph, "Optimal ratio for data splitting," Statistical Analysis and Data Mining: The ASA Data Science Journal, 2022.
[48] F. Rodríguez-Torres, J. F. Martínez-Trinidad and J. A. Carrasco-Ochoa, "An Oversampling Method for Class Imbalance Problems on Large Datasets," Applied Sciences, vol. 12, no. 2, p. 3424, 2022, doi: 10.3390/app12073424.
[49] T. Hasanin, T. M. Khoshgoftaar, J. L. Leevy and R. A. Bauder, "Severely imbalanced Big Data challenges: investigating data sampling approaches," Journal of Big Data, vol. 6, no. 1, pp. 1-25, 2019, doi: 10.1186/s40537-019-0274-4.
[50] M. Á. Canela, I. Alegre and A. Ibarra, "Out-of-Sample Validation," in Quantitative Methods for Management, Springer, 2019, pp. 83-89, doi: 10.1007/978-3-030-17554-2_9.
[51] H. Roohi and Q. Usman, "An Approach to Detect Spam Emails by Using Majority Voting," in Proceedings of the International Conference on Data Mining, Internet Computing and Big Data (BigData2014), 2014.
[52] A. Sifaunajah and R. D. Wahyuningtyas, "The Classification of Prospective Students with Rapid Miner," NEWTON: Networking and Information Technology, vol. 2, no. 2, pp. 72-78, 2022.
[53] S. Bashir, I. U. Khattak, A. Khan, F. H. Khan, A. Gani and M. Shiraz, "A Novel Feature Selection Method for Classification of Medical Data Using Filters, Wrappers, and Embedded Approaches," Complexity, vol. 2022, 2022, doi: 10.1155/2022/8190814.
[54] R. Rizal, M. Martanto and Y. A. Wijaya, "ANALISA DATASET SOFTWARE DEFINED NETWORK INTRUSION MENGGUNAKAN ALGORITMA DEEP LEARNING H2O," JATI (Jurnal Mahasiswa Teknik Informatika, vol. 6, no. 2, pp. 747-757, 2022, doi: 10.36040/jati.v6i2.5724.
[55] D. J. Arunadevi, S. Ramya and M. R. Raja, "A study of classification algorithms using RapidMiner," International Journal of Pure and Applied Mathematics, vol. 119, no. 12, pp. 15977-15988, 2018.
[56] Y. B. Lasotte, E. J. Garba, Y. M. Malgwi and M. A. Buhari, "An Ensemble Machine Learning Approach for Fake News Detection and Classification Using a Soft Voting Classifier," European Journal of Electrical Engineering and Computer Science, vol. 6, no. 2, pp. 1-7, 2022, doi: 10.24018/ejece.2022.6.2.409.
[57] O. Thorat, N. Parekh and R. Mangrulkar, "TaxoDaCML: Taxonomy based Divide and Conquer using machine learning approach for DDoS attack classification," International Journal of Information Management Data Insights, vol. 1, no. 2, p. 100048, 2021, doi: 10.1016/j.jjimei.2021.100048.
[58] A. Verma and V. Ranga, "Machine Learning Based Intrusion Detection Systems for IoT Applications," Wireless Personal Communications, vol. 111, pp. 2287-2310, 2019, doi: 10.1007/s11277-019-06986-8.