Deteksi Serangan ICMP Flood pada Internet of Things dengan Feature Selection dan Machine Learning
DOI:
https://doi.org/10.30865/jurikom.v12i4.8554Keywords:
Serangan ICMP Flood, Random Forest, Support Vector Machine, Seleksi Fitur, Forward SelectionAbstract
IoT devices have played an important role in driving DDoS attacks, and are a threat to IoT networks. One of them is the ICMP Flood attack. To overcome attacks on IoT, one of them uses an Intrusion Detection System (IDS). However, on the other hand, IDS has challenges in handling the complexity of high-dimensional data. One of the suggested solutions to overcome the problem of data dimensions is the use of feature selection techniques. The Forward Selection feature selection technique is used to eliminate irrelevant features. This study compares the performance of the Random Forest and SVM algorithms. For experimental purposes, this study used the CICIoT2023 dataset, which represents IoT traffic. The use of Forward Selection obtained 11 selected features that will be used in the machine learning process using the Random Forest and SVM methods. Feature selection affects the computation time or processing time, because the fewer features used, the more the system's workload in carrying out the classification process. The test results show that the use of feature selection improves the performance of random forest with an accuracy of 100%. Meanwhile, the SVM model gets better accuracy by using feature selection with the highest accuracy of 99.4508% in the supplied test set test.
References
M. Roopak, G. Y. Tian, and Chambers Jonathon, “An Intrusion Detection System Against DDoS Attacks in loT Networks,” in 10th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas: IEEE, 2020, pp. 562–567. doi: 10.1109/CCWC47524.2020.9031206.
S. S. Bhunia and M. Gurusamy, “Dynamic Attack Detection and Mitigation in IoT using SDN,” in 27th International Telecommunication Networks and Applications Conference (ITNAC), Melbourne: IEEE, 2017. doi: 10.1109/ATNAC.2017.8215418.
U. Javaid, A. K. Siang, M. N. Aman, and B. Sikdar, “Mitigating IoT Device based DDoS Attacks using Blockchain,” in CRYBLOCK 2018 - Proceedings of the 1st Workshop on Cryptocurrencies and Blockchains for Distributed Systems, Part of MobiSys 2018: The 16th Annual International Conference on Mobile Systems, Applications, and Services, Association for Computing Machinery, Inc, Jun. 2018, pp. 71–76. doi: 10.1145/3211933.3211946.
J. Bhayo, R. Jafaq, A. Ahmed, S. Hameed, and S. A. Shah, “A Time-Efficient Approach Toward DDoS Attack Detection in IoT Network Using SDN,” IEEE Internet Things J, vol. 9, no. 5, pp. 3612–3630, Mar. 2022, doi: 10.1109/JIOT.2021.3098029.
D. K. Sharma et al., “Anomaly detection framework to prevent DDoS attack in fog empowered IoT networks,” Ad Hoc Networks, vol. 121, Oct. 2021, doi: 10.1016/j.adhoc.2021.102603.
M. Zidane, “Klasifikasi Serangan Distributed Denial-of-Service (DDoS) menggunakan Metode Data Mining Naïve Bayes,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 6, no. 1, pp. 172–180, 2022, [Online]. Available: http://j-ptiik.ub.ac.id
Harshita, “Detection and Prevention of ICMP Flood DDOS Attack,” International Journal of New Technology and Research (IJNTR), vol. 3, no. 3, pp. 63–69, 2017, [Online]. Available: www.ijntr.org?
V. Chauhan and P. Saini, “ICMP flood attacks: A vulnerability analysis,” in Advances in Intelligent Systems and Computing, Springer Verlag, 2018, pp. 261–268. doi: 10.1007/978-981-10-8536-9_26.
E. Hodo et al., “Threat analysis of IoT networks Using Artificial Neural Network Intrusion Detection System,” in International Symposium on Networks, Computers and Communications (ISNCC), Yasmine Hammamet: [IEEE], 2016. doi: 10.1109/ISNCC.2016.7746067.
M. Aamir and S. M. Ali Zaidi, “Clustering based semi-supervised machine learning for DDoS attack classification,” Journal of King Saud University - Computer and Information Sciences, vol. 33, no. 4, pp. 436–446, May 2021, doi: 10.1016/j.jksuci.2019.02.003.
E. O. Nasution and A. Basuki, “Implementasi Algoritme C5.0 Untuk Klasifikasi Serangan DDoS,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 5, no. 1, pp. 389–395, 2021, [Online]. Available: http://j-ptiik.ub.ac.id
H. Polat, O. Polat, and A. Cetin, “Detecting DDoS Attacks in Software-Defined Networks Through Feature Selection Methods and Machine Learning Models,” Sustainability (Switzerland), vol. 12, no. 3, pp. 1–16, Feb. 2020, doi: 10.3390/su12031035.
S. Hosseini and M. Azizi, “The hybrid technique for DDoS detection with supervised learning algorithms,” Computer Networks, vol. 158, pp. 35–45, Jul. 2019, doi: 10.1016/j.comnet.2019.04.027.
K. Kurniabudi, A. Harris, and V. Veronica, “Komparasi Performa Tree-Based Classifier Untuk Deteksi Anomali Pada Data Berdimensi Tinggi dan Tidak Seimbang,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 6, no. 1, p. 370, Jan. 2022, doi: 10.30865/mib.v6i1.3473.
K. Kurniabudi, A. Harris, and A. E. Mintaria, “Komparasi Information Gain, Gain Ratio, CFs-Bestfirst dan CFs-PSO Search Terhadap Performa Deteksi Anomali,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 5, no. 1, p. 332, Jan. 2021, doi: 10.30865/mib.v5i1.2258.
Y. Chen, J. Hou, Q. Li, and H. Long, “DDoS Attack Detection Based on Random Forest,” in International Conference on Progress in Informatics and Computing (PIC), Institute of Electrical and Electronics Engineers Inc., Dec. 2020, pp. 328–334. doi: 10.1109/PIC50277.2020.9350788.
L. Ikhwanul Uzlah, R. Adi Saputra, and Isnawaty, “DETEKSI SERANGAN SIBER PADA JARINGAN KOMPUTER MENGGUNAKAN METODE RANDOM FOREST,” Jurnal Mahasiswa Teknik Informatika, vol. 8, no. 3, pp. 2787–2793, Jun. 2024, doi: https://doi.org/10.36040/jati.v8i3.8891.
S. Sivaranjani, S. Ananya, J. Aravinth, and R. Karthika, “Diabetes Prediction using Machine Learning Algorithms with Feature Selection and Dimensionality Reduction,” in 7th International Conference on Advanced Computing and Communication Systems (ICACCS)), Institute of Electrical and Electronics Engineers Inc., Mar. 2021, pp. 141–146. doi: 10.1109/ICACCS51430.2021.9441935.
S. M. Tseng, Y. Q. Wang, and Y. C. Wang, “Multi-Class Intrusion Detection Based on Transformer for IoT Networks Using CIC-IoT-2023 Dataset,” Future Internet, vol. 16, no. 8, pp. 1–25, Aug. 2024, doi: 10.3390/fi16080284.
E. C. P. Neto, S. Dadkhah, R. Ferreira, A. Zohourian, R. Lu, and A. A. Ghorbani, “CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment,” Sensors, vol. 23, no. 13, pp. 1–26, Jul. 2023, doi: 10.3390/s23135941.
J. Cai, J. Luo, S. Wang, and S. Yang, “Feature selection in machine learning: A new perspective,” Neurocomputing, vol. 300, pp. 70–79, Jul. 2018, doi: 10.1016/j.neucom.2017.11.077.
F. Kamalov, S. Elnaffarr, A. Cherukuri, and A. Jonnalagadda, “Forward feature selection: empirical analysis,” Journal of Intelligent Systems and Internet of Things, vol. 11, no. 1, pp. 44–54, 2024, doi: 10.54216/JISIoT.110105.
A. R. Wani, Q. P. Rana, U. Saxena, and N. Pandey, “Analysis and Detection of DDoS Attacks on Cloud Computing Environment using Machine Learning Techniques,” in Amity International Conference on Artificial Intelligence (AICAI), Institute of Electrical and Electronics Engineers, 2019, pp. 870–875. doi: https://doi.org/10.1109/AICAI.2019.8701238.
Additional Files
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Harid Harid, Kurniabudi Kurniabudi, Abdul Harris
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
