Penggunaan Metode Backpropagation Pada Jaringan Syaraf Tiruan Untuk Intrusion Detection System
DOI:
https://doi.org/10.29408/jit.v3i2.2317Keywords:
backpropagation network, IDS, deep learning, machine learningAbstract
Convenience is the main goal of existence of technology this days, but over time these technological advancements have made user privacy increasingly difficult and become a cause of concern. The existence of IDS (Intrusion Detection System ) is believed to help in achieving security in network usage where this detection system work by observing abnormal network behavior. IDS emphasizes the need to use artificial neural network to detect these attack. From the research tht has been done, the use of artificial neural network with a learning rate of 0.1 has been applied and the KDDCup-99 dataset has been used to train and make network benchmarks. To conduct a comparison, training has also been carried out on the same dataset using several other learning algorithms. The number of layer used in this artificial neural network start from 1 to 5, and the result have been compared so that the conclusion said that the artificial neural network with 3 layers has a superior performance compare to other machine learning algorithms.
DOI : 10.29408/jit.v3i2.2317
References
H. R. A. Talompo, A. S. Ahmad, Y. S. Gondokaryono, and S. Sutikno, “NAIDS design using ChiMIC-KGS,†in International Symposium on Electronics and Smart Devices, 2017.
Y. Yang, K. Zheng, C. Wu, and Y. Yang, “Improving the classification effectiveness of intrusion detection by using improved conditional variational autoencoder and deep neural network,†Sensors (Switzerland), 2019.
D. A. Mohammad Kazim Hooshmand, “Machine Learning Based Network Anomaly Detection,†Int. J. Recent Technol. Eng., 2019.
L. Rettig, M. Khayati, P. Cudre-Mauroux, and M. Piorkowski, “Online anomaly detection over Big Data streams,†Proc. - 2015 IEEE Int. Conf. Big Data, 2015.
V. TimÄenko and S. Gajin, “Machine Learning based Network Anomaly Detection for IoT environments,†Icist, 2018.
N. Moustafa and J. Slay, “The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set,†Inf. Secur. J., 2016.
D. G. Mogal, S. R. Ghungrad, and B. B. Bhusare, “NIDS using Machine Learning Classifiers on UNSW-NB15 and KDDCUP99 Datasets,†Ijarcce, 2017.
R. Vinayakumar, M. Alazab, K. P. Soman, P. Poornachandran, A. Al-Nemrat, and S. Venkatraman, “Deep Learning Approach for Intelligent Intrusion Detection System,†IEEE Access, 2019.
S. Alhamouz and A. Abu-Shareha, “Hybrid Classification Approach Using Self-Organizing Map and Back Propagation Artificial Neural Networks for Intrusion Detection,†Proc. - Int. Conf. Dev. eSystems Eng. DeSE, 2018.
Z. Wang, “Deep Learning-Based Intrusion Detection with Adversaries,†IEEE Access, 2018.
S. M. Kasongo and Y. Sun, “A deep learning method with wrapper based feature extraction for wireless intrusion detection system,†Comput. Secur., 2020.
O. Faker and E. Dogdu, “Intrusion detection using big data and deep learning techniques - UNSW-NB15 - CICIDS2017,†ACMSE 2019 - Proc. 2019 ACM Southeast Conf., 2019.
J. Yan, D. Jin, C. W. Lee, and P. Liu, “A Comparative Study of Off-Line Deep Learning Based Network Intrusion Detection,†in International Conference on Ubiquitous and Future Networks, ICUFN, 2018.
S. Choudhary and N. Kesswani, “Analysis of KDD-Cup’99, NSL-KDD and UNSW-NB15 Datasets using Deep Learning in IoT,†Procedia Comput. Sci., 2020.
T. Janarthanan and S. Zargari, “Feature selection in UNSW-NB15 and KDDCUP’99 datasets,†in IEEE International Symposium on Industrial Electronics, 2017.
N. Moustafa and J. Slay, “The significant features of the UNSW-NB15 and the KDD99 data sets for Network Intrusion Detection Systems,†Proc. - 2015 4th Int. Work. Build. Anal. Datasets Gather. Exp. Returns Secur., 2017.
M. V, “A Survey on Performance Analysis through Dimensional Reduction and Classification Algorithm using KDD Cup and UNSW-NB15 Dataset,†Int. J. Res. Appl. Sci. Eng. Technol., 2019.
C. Xu, J. Shen, X. Du, and F. Zhang, “An Intrusion Detection System Using a Deep Neural Network with Gated Recurrent Units,†IEEE Access, 2018.
Downloads
Additional Files
Published
How to Cite
Issue
Section
License
Semua tulisan pada jurnal ini menjadi tanggung jawab penuh penulis. Jurnal Infotek memberikan akses terbuka terhadap siapapun agar informasi dan temuan pada artikel tersebut bermanfaat bagi semua orang. Jurnal Infotek ini dapat diakses dan diunduh secara gratis, tanpa dipungut biaya sesuai dengan lisense creative commons yang digunakan.
Jurnal Infotek is licensed under a Creative Commons Attribution 4.0 International License.
Statistik Pengunjung
