Perbandingan Algoritma Machine Learning Dalam Mendeteksi Serangan DDOS
DOI:
https://doi.org/10.38204/tematik.v9i2.1070Keywords:
DdoS Attack, Machine Learning, Decision Tree, XGBoost, ANNAbstract
Ddos is an attack method by sending a lot of packets into a network that causes the device not to run according to its function. This attack will result in machine or network resources cannot be accessed or used by the user. Various methods are used to detect DDOS attacks on SDN [4] , namely statistical methods, machine learning, SDN architecture, blockchain, Network Function Virtualization, honeynets, network slicing, and moving target defense. Because so many people use machine learning to detect DDoS attacks, it is necessary to do further research to find out which one is the best and has high accuracy. Therefore, a research entitled “Comparison of Machine Learning Algorithms in Detecting DDoS Attacks was made. In this study, three machine learning algorithms will be compared, namely XGBoost, Decision Tree and ANN. The methods used are data acquisition, data understanding, data preparation, modeling, performance evaluation, and conclusions. In this study it can be said that for accuracy, the highest model is XGBoost in determining attacks, but to execute it requires the longest time among other models tested. While Decision tree also has high accuracy, slightly below XGBoost, but the time required to execute is fast or short. Therefore, in this study it can be said that the Decision Tree is the best model in detecting and classifying DDoS attacks.Keywords: Ddos Attack, Machine Learning, Decision Tree, XGBoost, ANN.
Downloads
References
R. F. Junaedi, D. Stiawan and A. Heryanto, "Deteksi Serangan DDoS (Distributed Denial Of Service) Di Cloud Computing Dengan Menggunakan Metode Rule Base," Undergraduate Thesis, Universitas Sriwijaya, 2019.
I. Riadi, R. Umar and F. D. Aini, "Analisis Perbandingan Detecsion Traffic Anomaly Dengan Metode Naïve Bayes dan Support Vector Machine (SVM)," ILKOM Jurnal Ilmiah, vol. 11, p. 17, 2019.
P. Darryl and M. Subali, "Perbandingan Algoritma SVM dan Algoritma KNN dalam Menghasilkan Klasifikasi DDoS dan Benign.," Jurnal Ilmiah KOMPUTASI, vol. 20, no. https://doi.org/http://dx.doi.org/10.32409/jikstik.20.4.2799, pp. 491-500, 2021.
B. B. Gupta and A. Dahiya, Distributed Denial of Service (DDoS) Attacks, CRC Press, 2021.
F. Rahmatullah, "Deteksi Distributed Denial Of Service (DDOS) Dalam Jaringan Software Defined Network Dengan Metode Support Vector Machine," UPN "Veteran", Yogyakarta, 2022.
I. A. Valdinos, J. A. Perez-Diaz, K.-K. R. Choo and J. F. Botero, "Emerging DDOS attack detection and mitigation strategies in software-defined network: Taxonomy, Challenges and Future Directions," ELSEVIER:Journal of Network and Computer Applications, vol. 187, 2021.
G. Usha, M. Narang and A. Kumar, "Detection and Classification of Distributed DoS Attacks Using Machine Learning," in Computer Networks and Inventive Communication Technologies, India, 2021.
M. N. Faiz, O. Somantri, A. R. Supriyono and A. W. MUhammad, "Impact of Feature Selection Methods on Machine Learning-based for Detecting DDoS Attacks : Literature Review," JITE (Journal of Informatics and Telecommunication Engineering), p. 305, 2022.
I. Saputra and D. A. Kristiyanti, "Machine Learning Untuk Pemula".
M. Sadikin, Teknik Machine Learning Untuk Menangani Permasalahan Data Bias Pada Transaksi Aplikasi POS, Jawa Tengah: Zahira Media Publisher, 2022.
A. Kuswantori, Memahami ANN, Deep Learning, CNN dan YOLO, Malang: Medza Media, 2022.
W. Setiawan, Deep Learning Menggunakan Convolutional Neural Network: Teori dan Aplikasi, Malang: Media Nusa Creative, 2020.
N. Vandeput, Data Science for Supply Chain Forecasting, Boston: Walter de Gruyter GmbH, 2021.
N. L. W. S. R. Ginantra and dkk., Data Mining dan Penerapan Algoritma, Yayasan Kita Menulis, 2021.
N. W. Wardani, Penerapan Data Mining Dalam Analytic CRM, Yayasan Kita Menulis, 2020.
