Deteksi Serangan pada Jaringan Kompleks IoT menggunakan Recurrent Neural Network

 Eko Arip Winanto (ekoaripwinanto@unama.ac.id, Indonesia)
 (*)Kurniabudi Kurniabudi Mail (Universitas Dinamika Bangsa, Jambi, Indonesia)
 Sharipuddin Sharipuddin (Universitas Dinamika Bangsa, Jambi, Indonesia)
 Ibnu Sani Wijaya (Universitas Dinamika Bangsa, Jambi, Indonesia)
 Dodi Sandra (Universitas Dinamika Bangsa, Jambi, Indonesia)

(*) Corresponding Author

Abstract

The complex network in the Internet of Things is challenging to maintain network security. With network complexity including data, protocols, sizes, communications, standards, and more, it becomes difficult to implement an intrusion detection system (IDS). One way to improve IDS on complex IoT networks is by using deep learning to detect attacks that occur on complex IoT networks. Recurrent neural network (RNN) is a deep learning method that enhances the detection of complex IoT networks because it takes into account the current input as well as what has been learned from previously received inputs. When making decisions about RNNs, consider current information as well as what has been learned from previous input. Therefore, this study proposes the RNN method to improve the performance of attack detection systems on complex IoT networks. The results of this experiment show satisfactory results by increasing the performance of the accuracy detection system in complex IoT networks which reaches 87%.

Keywords


IDS; deep learning; RNN; Complex networks; IoT

Full Text:

PDF


Article Metrics

Abstract view : 937 times
PDF - 366 times

References

W. Li, S. Tug, W. Meng, and Y. Wang, “Designing collaborative blockchained signature-based intrusion detection in IoT environments,” Futur. Gener. Comput. Syst., vol. 96, pp. 481–489, 2019, doi: 10.1016/j.future.2019.02.064.

E. A. Winanto, M. Y. Idris, D. Stiawan, and M. S. Nurfatih, “Designing Consensus Algorithm for Collaborative Signature- based Intrusion Detection System,” vol. 1, 2020.

E. A. Winanto, M. Y. bin Idris, D. Stiawan, M. S. N. Fatih, and Sharipuddin, “PoAS: Enhanced Consensus Algorithm for Collaborative Blockchain Intrusion Detection System,” in 2020 3rd International Conference on Information and Communications Technology (ICOIACT), Nov. 2020, pp. 513–518, doi: 10.1109/ICOIACT50329.2020.9332078.

E. A. Winanto, D. Stiawan, and A. Heryanto, “Visualisasi Serangan Remote to Local ( R2L ) Dengan Clustering K-Means,” Pros. Annu. Res. Semin. 2016, vol. 2, no. 1, pp. 359–362, 2016.

Y. Li, R. Ma, and R. Jiao, “A hybrid malicious code detection method based on deep learning,” Int. J. Secur. its Appl., vol. 9, no. 5, pp. 205–216, 2015, doi: 10.14257/ijsia.2015.9.5.21.

I. Sohn, “Deep belief network based intrusion detection techniques: A survey,” Expert Syst. Appl., vol. 167, p. 114170, 2021, doi: 10.1016/j.eswa.2020.114170.

S. Mahdavifar and A. A. Ghorbani, “Application of deep learning to cybersecurity: A survey,” Neurocomputing, vol. 347, pp. 149–176, 2019, doi: 10.1016/j.neucom.2019.02.056.

S. Sharipuddin et al., “Intrusion detection with deep learning on internet of things heterogeneous network,” IAES Int. J. Artif. Intell., vol. 10, no. 3, p. 735, 2021, doi: 10.11591/ijai.v10.i3.pp735-742.

N. Balakrishnan, A. Rajendran, D. Pelusi, and V. Ponnusamy, “Deep Belief Network enhanced intrusion detection system to prevent security breach in the Internet of Things,” Internet of Things, no. xxxx, p. 100112, 2019, doi: 10.1016/j.iot.2019.100112.

S. Sharipuddin et al., “Enhanced Deep Learning Intrusion Detection in IoT Heterogeneous Network with Feature Extraction,” Int. J. Electr. Eng. Informatics, vol. 9, no. 3, pp. 747–755, 2021, doi: 10.52549/ijeei.v9i3.3134.

A. Bhardwaj, V. Mangat, and R. Vig, “Hyperband Tuned Deep Neural Network With Well Posed Stacked Sparse AutoEncoder for Detection of DDoS Attacks in Cloud,” IEEE Access, vol. 8, pp. 181916–181929, 2020, doi: 10.1109/access.2020.3028690.

C. Yin, Y. Zhu, J. Fei, and X. He, “A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks,” IEEE Access, vol. 5, pp. 21954–21961, 2017, doi: 10.1109/ACCESS.2017.2762418.

H. HaddadPajouh, A. Dehghantanha, R. Khayami, and K. K. R. Choo, “A deep Recurrent Neural Network based approach for Internet of Things malware threat hunting,” Futur. Gener. Comput. Syst., vol. 85, pp. 88–96, 2018, doi: 10.1016/j.future.2018.03.007.

M. M. Hassan, A. Gumaei, A. Alsanad, M. Alrubaian, and G. Fortino, “A hybrid deep learning model for efficient intrusion detection in big data environment,” Inf. Sci. (Ny)., vol. 513, pp. 386–396, 2019, doi: https://doi.org/10.1016/j.ins.2019.10.069.

B. Yan and G. Han, “Effective Feature Extraction via Stacked Sparse Autoencoder to Improve Intrusion Detection System,” IEEE Access, vol. 6, no. c, pp. 41238–41248, 2018, doi: 10.1109/ACCESS.2018.2858277.

Y. N. Kunang, S. Nurmaini, D. Stiawan, A. Zarkasi, and F. Jasmir, “Automatic Features Extraction Using Autoencoder in Intrusion Detection System,” Proc. 2018 Int. Conf. Electr. Eng. Comput. Sci. ICECOS 2018, vol. 17, pp. 219–224, 2019, doi: 10.1109/ICECOS.2018.8605181.

Kurniabudi, D. Stiawan, Darmawijoyo, M. Y. Bin Bin Idris, A. M. Bamhdi, and R. Budiarto, “CICIDS-2017 Dataset Feature Analysis with Information Gain for Anomaly Detection,” IEEE Access, vol. 8, pp. 132911–132921, 2020, doi: 10.1109/ACCESS.2020.3009843.

S. Sharipuddin et al., “Features Extraction on IoT Intrusion Detection System Using Principal Components Analysis (PCA),” Proc. EECSI 2020 - 1-2 Oct. 2020, pp. 114–118, 2020.

T. Kumar, P. Kumar, M. Nappi, and S. Bakshi, “Satellite IoT Based Road Extraction from VHR Images Through Superpixel-CNN Architecture,” Big Data Res., vol. 30, p. 100334, 2022, doi: 10.1016/j.bdr.2022.100334.

Y. Meidan, M. Bohadana, and D. Breitenbacher, “N-BaIoT — Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders,” IEEE Pervasive Comput., no. September, pp. 12–22, 2018.

T. K. Behera, P. K. Sa, M. Nappi, and S. Bakshi, “Satellite IoT Based Road Extraction from VHR Images Through Superpixel-CNN Architecture,” Big Data Res., vol. 30, p. 100334, 2022, doi: https://doi.org/10.1016/j.bdr.2022.100334.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Deteksi Serangan pada Jaringan Kompleks IoT menggunakan Recurrent Neural Network

Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 Eko Arip Winanto, Kurniabudi, Sharipuddin, Ibnu Sani Wijaya, Dodi Sandra

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

JURIKOM (Jurnal Riset Komputer)
Publish by Universitas Budi Darma (before STMIK BUDI DARMA (P3M))
Email: jurikom.stmikbd@gmail.com

Creative Commons License
 This work is licensed under a Creative Commons Attribution 4.0 International.