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Print ISSN: 1582-7445
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 HIGH-IMPACT PAPER 

A Hybrid Deep Learning Approach for Intrusion Detection in IoT Networks

EMEC, M. See more information about EMEC, M. on SCOPUS See more information about EMEC, M. on IEEExplore See more information about EMEC, M. on Web of Science, OZCANHAN, M. H. See more information about OZCANHAN, M. H. on SCOPUS See more information about OZCANHAN, M. H. on SCOPUS See more information about OZCANHAN, M. H. on Web of Science
 
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Download PDF pdficon (1,449 KB) | Citation | Downloads: 1,900 | Views: 2,485

Author keywords
hybrid intelligent systems, Internet of Things, intrusion detection, learning systems, prediction methods

References keywords
learning(19), detection(17), deep(15), network(14), intrusion(13), link(7), system(6), security(6), neural(6), model(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2022-02-28
Volume 22, Issue 1, Year 2022, On page(s): 3 - 12
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2022.01001
Web of Science Accession Number: 000762769600002
SCOPUS ID: 85126801506

Abstract
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Internet of Things (IoT) devices have flocked the whole world through the Internet. With increasing mission-critical IoT data traffic, attacks on IoT networks have also increased. Many newly crafted attacks on IoT communication require equally intelligent intrusion detection methods to form the first step of countering the attacks. Our work contributes to intrusion detection in IoT networks, by putting state-of-the-art Deep learning methods into service. A BLSTM-GRU Hybrid (BGH) model has been designed to detect eight known IoT network attacks, based on two well-accepted CIC-IDS-2018 and BoT-IoT IoT network traffic datasets. The results of our BGH model in IoT network traffic intrusion detection have been auspicious. The accuracies of prediction on the two datasets are 98.78% and 99.99%. The f1-scores are 98.64% and 99.99%, respectively. The comparison of our results with similar previous studies showed that our BGH model has the best performance ratio (time/accuracy, time/f1-score), where time is the training time of the model. The performance of our proposed model is proof that hybrid Deep Learning methods can prove to be an innovative perspective on Intrusion Detection in IoT networks.


References | Cited By  «-- Click to see who has cited this paper

[1] B. Javed, M. W. Iqbal and H. Abbas, "Internet of Things (IoT) design considerations for developers and manufacturers," 2017 IEEE International Conference on Communications Workshops (ICC Workshops), IEEE, 2017, pp. 834-839.
[CrossRef] [SCOPUS Times Cited 26]


[2] K. Xu, X. Wang, W. Wei, H. Song and B. Mao, "Toward software defined smart home," IEEE Communications Magazine, vol. 54, no. 5, May 2016, pp. 116-122.
[CrossRef] [Web of Science Times Cited 96] [SCOPUS Times Cited 111]


[3] A. Q. Mobark and A. Sidorova, "Consumer acceptance of Internet of Things (IoT): Smart home context," Journal of Computer Information Systems, vol. 60, no. 6, Nov. 2020, pp. 507-517.
[CrossRef] [Web of Science Times Cited 89] [SCOPUS Times Cited 112]


[4] M. Sendhil, and J. Spiess, "Machine learning: An applied econometric approach," Journal of Economic Perspectives, vol. 31, no. 2, May 2017, pp. 87-106.
[CrossRef] [Web of Science Times Cited 742] [SCOPUS Times Cited 914]


[5] "Number of IoT Devices 2015-2025," Statista, [Online] Available: Temporary on-line reference link removed - see the PDF document

[6] ITU. "ARM Predicts 1 Trillion IoT Devices by 2035 with New End-to-End Platform," ITU News, 6 Aug. 2018, [Online] Available: Temporary on-line reference link removed - see the PDF document

[7] J. Wurm, K. Hoang, O. Arias, A. Sadeghi and Y. Jin , "Security analysis on consumer and industrial IoT devices," 2016 21st Asia and South Pacific Design Automation Conference (ASP-DAC), IEEE, 2016, pp. 519-524.
[CrossRef] [SCOPUS Times Cited 235]


[8] What You Need to Know about the Mirai Botnet behind Recent Major DDoS Attacks. [Online] Available: Temporary on-line reference link removed - see the PDF document

[9] IDS 2018 | Datasets | Research | Canadian Institute for Cybersecurity | UNB. [Online] Available: Temporary on-line reference link removed - see the PDF document

[10] Moustafa, Nour. The Bot-IoT Dataset. Oct. 2019. [Online] Available: Temporary on-line reference link removed - see the PDF document

[11] Y. LeCun, et al., "Deep Learning," Nature, vol. 521, no. 7553, May 2015, pp. 436-444.
[CrossRef] [Web of Science Times Cited 23923] [SCOPUS Times Cited 59381]


[12] M. T. Mahmud et al., "Using machine learning to secure IOT systems," 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), IEEE, 2020, pp. 1-7.
[CrossRef] [SCOPUS Times Cited 11]


[13] "A New Botnet Attack Just Mozied Into Town," Security Intelligence, [Online] Available: Temporary on-line reference link removed - see the PDF document

[14] McAfee Labs Threats Report | November 2020. [Online] Available: Temporary on-line reference link removed - see the PDF document

[15] J. Kim, et al., "CNN-Based network intrusion detection against denial-of-service attacks," Electronics, vol. 9, no. 6, June 2020, p. 916.
[CrossRef] [Web of Science Times Cited 178] [SCOPUS Times Cited 258]


[16] S. Aljawarneh M. Aldwairi, M. B. Yassein, "Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model," Journal of Computational Science, vol. 25, Mar. 2018, pp. 152-160.
[CrossRef] [Web of Science Times Cited 296] [SCOPUS Times Cited 472]


[17] M. Esmalifalak, Nam Tuan Nguyen, R. Zheng and Z. Han, "Detecting stealthy false data injection using machine learning in smart grid," 2013 IEEE Global Communications Conference (GLOBECOM), IEEE, 2013, pp. 808-813.
[CrossRef] [SCOPUS Times Cited 40]


[18] B. Yan, and H. Guodong, "LA-GRU: Building combined intrusion detection model based on imbalanced learning and gated recurrent unit neural network," Security and Communication Networks, vol. 2018, Aug. 2018, pp. 1-13.
[CrossRef] [Web of Science Times Cited 36] [SCOPUS Times Cited 51]


[19] M. A. Altuncu, F. K. Gulagiz, H. Ozcan, O. F. Bayir, A. Gezgın, A. Niyazov, M. A. Cavuslu, S. Sahin, "Deep learning based DNS tunneling detection and blocking system," Advances in Electrical and Computer Engineering, vol. 21, no. 3, 2021, pp. 39-48.
[CrossRef] [Full Text] [SCOPUS Times Cited 6]


[20] T. Su, H. Sun, J. Zhu, S. Wang and Y. Li, "BAT: Deep learning methods on network intrusion detection using NSL-KDD dataset," IEEE Access, vol. 8, 2020, pp. 29575-29585.
[CrossRef] [Web of Science Times Cited 199] [SCOPUS Times Cited 284]


[21] P. Lin, K. Ye, C.-Z. Xu, "Dynamic network anomaly detection system by using deep learning techniques," Cloud Computing - CLOUD 2019, edited by Dilma Da Silva et al., vol. 11513, Springer International Publishing, 2019, pp. 161-176.
[CrossRef] [Web of Science Times Cited 85] [SCOPUS Times Cited 126]


[22] M. A. Ferrag, M. Leandros, M. Sotiris, J. Helge, "Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study," Journal of Information Security and Applications, vol. 50, Feb. 2020, p. 102419.
[CrossRef] [Web of Science Times Cited 450] [SCOPUS Times Cited 691]


[23] M. Tao, et al., "A hybrid spectral clustering and deep neural network ensemble algorithm for intrusion detection in sensor networks," Sensors, vol. 16, no. 10, Oct. 2016, p. 1701.
[CrossRef] [Web of Science Times Cited 139] [SCOPUS Times Cited 187]


[24] P. Liu, and J. Liu, "Combined effect of multiple performance shaping factors on human reliability: Multiplicative or additive?," International Journal of Human-Computer Interaction, vol. 36, no. 9, May 2020, pp. 828-838. Taylor and Francis+NEJM,
[CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 6]


[25] Y. Li, eJ. Huang, H. Chen, "Time series prediction of wireless network traffic flow based on wavelet analysis and BP neural network," Journal of Physics: Conference Series, vol. 1533, no. 3, Apr. 2020, p. 032098.
[CrossRef] [SCOPUS Times Cited 12]


[26] Q. R. S. Fitni, and R. Kalamullah, "Implementation of ensemble learning and feature selection for performance improvements in anomaly-based intrusion detection systems," 2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT), IEEE, 2020, pp. 118-124.
[CrossRef] [SCOPUS Times Cited 93]


[27] F. Zhao, H. Zhang, J. Peng, et al., "A semi-self-taught network intrusion detection system," Neural Computing and Applications, vol. 32, no. 23, Dec. 2020, pp. 17169-79.
[CrossRef] [Web of Science Times Cited 21] [SCOPUS Times Cited 28]


[28] X. Li, W. Chen, Q. Zhang, L. Wu, "Building auto-encoder intrusion detection system based on random forest feature selection," Computers & Security, vol. 95, Aug. 2020, p. 101851.
[CrossRef] [Web of Science Times Cited 181] [SCOPUS Times Cited 245]


[29] G. Sunanda, and J. Samarabandu, "Deep learning methods in network intrusion detection: A survey and an objective comparison," Journal of Network and Computer Applications, vol. 169, Nov. 2020, p. 102767.,
[CrossRef] [Web of Science Times Cited 162] [SCOPUS Times Cited 245]


[30] M. Catillo, M. Rak, U. Villano, "2L-ZED-IDS: A two-level anomaly detector for multiple attack classes," Web, Artificial Intelligence and Network Applications, edited by Leonard Barolli et al., vol. 1150, Springer International Publishing, 2020, pp. 687-696.
[CrossRef] [SCOPUS Times Cited 41]


[31] R. I. Farhan A. T. Maolood, N. Hassan, "Performance analysis of flow-based attacks detection on CSE-CIC-IDS2018 dataset using deep learning," Indonesian Journal of Electrical Engineering and Computer Science, vol. 20, no. 3, Dec. 2020, p. 1413-1418.
[CrossRef] [SCOPUS Times Cited 2]


[32] G. C. Amaizu, C. I. Nwakanma, J. -M. Lee and D. -S. Kim, "Investigating network intrusion detection datasets using machine learning," 2020 International Conference on Information and Communication Technology Convergence (ICTC), IEEE, 2020, pp. 1325-1328.
[CrossRef] [SCOPUS Times Cited 25]


[33] S. K. Wanjau, G. M. Wambugu, G. N. Kamau,,"SSH-Brute force attack detection model based on deep learning," International Journal of Computer Applications Technology and Research, vol. 10, no. 01, 2021, pp. 42-50

[34] M. Chhetri, S. Kumar, P. P. Roy, B.-G. Kim, "Deep BLSTM-GRU model for monthly rainfall prediction: A case study of Simtokha, Bhutan," Remote Sensing, vol. 12, no. 19, Sept. 2020, p. 3174.
[CrossRef] [Web of Science Times Cited 57] [SCOPUS Times Cited 84]


[35] P. Kaushik, A. Gupta, P. P. Roy and D. P. Dogra, "EEG-Based age and gender prediction using deep BLSTM-LSTM network model," IEEE Sensors Journal, vol. 19, no. 7, Apr. 2019, pp. 2634-2641.
[CrossRef] [Web of Science Times Cited 48] [SCOPUS Times Cited 66]


[36] N. Koroniotis, N. Moustafa, E. Sitnikova, B. Turnbull,, "Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset," Future Generation Computer Systems, vol. 100, Nov. 2019, pp. 779-796.
[CrossRef] [Web of Science Times Cited 738] [SCOPUS Times Cited 1018]


[37] I. Idrissi, et al., "Toward a deep learning-based intrusion detection system for IoT against botnet attacks," IAES International Journal of Artificial Intelligence (IJ-AI), vol. 10, no. 1, Mar. 2021, p. 110-120.
[CrossRef]


[38] M. Ge, X. Fu, N. Syed, Z. Baig, G. Teo and A. Robles-Kelly, "Deep learning-based intrusion detection for IoT networks," IEEE 24th Pacific Rim International Symposium on Dependable Computing (PRDC), IEEE, 2019, pp. 256-25609.
[CrossRef] [Web of Science Times Cited 106] [SCOPUS Times Cited 180]


[39] M. A. Ferrag and L. Maglaras, "DeepCoin: A novel deep learning and blockchain-based energy exchange framework for smart grids," IEEE Transactions on Engineering Management, vol. 67, no. 4, Nov. 2020, pp. 1285-1297.
[CrossRef] [Web of Science Times Cited 188] [SCOPUS Times Cited 224]


[40] M. Buda, A. Maki, M. A. Mazurowschi, "A systematic study of the class imbalance problem in convolutional neural networks," Neural Networks, vol. 106, Oct. 2018, pp. 249-259.
[CrossRef] [Web of Science Times Cited 1402] [SCOPUS Times Cited 1696]


[41] A. Mittal, P. Kumar, P. P. Roy, R. Balasubramanian and B. B. Chaudhuri, "A modified LSTM model for continuous sign language recognition using leap motion," IEEE Sensors Journal, vol. 19, no. 16, Aug. 2019, pp. 7056-7063.
[CrossRef] [Web of Science Times Cited 96] [SCOPUS Times Cited 176]


[42] M. Khan, H. Wang, A. Ngueilbaye, et al., "End-to-end multivariate time series classification via hybrid deep learning architectures," Personal and Ubiquitous Computing, Sept. 2020.
[CrossRef] [Web of Science Times Cited 13] [SCOPUS Times Cited 12]


[43] K. Cho, et al., "Learning phrase representations using RNN encoder-decoder for statistical machine translation," Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, 2014, pp. 1724-1734.
[CrossRef] [SCOPUS Times Cited 11018]


[44] D. Chicco, and J. Giuseppe, "The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation," BMC Genomics, vol. 21, no. 1, Dec. 2020, p. 6.
[CrossRef] [Web of Science Times Cited 2548] [SCOPUS Times Cited 3022]




References Weight

Web of Science® Citations for all references: 31,797 TCR
SCOPUS® Citations for all references: 81,098 TCR

Web of Science® Average Citations per reference: 707 ACR
SCOPUS® Average Citations per reference: 1,802 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-11-03 01:10 in 241 seconds.




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