DSNFyS: Deep Stacked Neuro Fuzzy System for Attack Detection and Mitigation in RPL based IoT

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Author(s)

Prashant Maurya 1,* Vandana Kushwaha 1

1. Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi, 221005, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijieeb.2025.03.05

Received: 27 Dec. 2024 / Revised: 13 Jan. 2025 / Accepted: 7 Feb. 2025 / Published: 8 Jun. 2025

Index Terms

Internet of Things, Routing, Attack Detection, Deep Learning, Attack Mitigation

Abstract

The Routing Protocol for Low-Power and Lossy Networks (RPL) is a widely adopted protocol for managing and optimizing routing in resource-constrained Internet of Things (IoT) environments.  RPL operates by constructing a Destination-Oriented Directed Acyclic Graph (DODAG) to establish efficient routes between nodes. This protocol is designed to address the unique challenges of IoT networks, such as limited energy resources, unreliable wireless links, and frequent topology changes. RPL's adaptability and scalability render it particularly suitable for large-scale IoT deployments in various applications, including smart cities, industrial automation, and environmental monitoring. However, the protocol's vulnerability to various security attacks poses significant threats to the reliability and confidentiality of IoT networks. To address this issue, a novel deep-stacked neuro-fuzzy system (DSNFyS) has been developed for attack detection in RPL-based IoT. The proposed approach begins with simulating RPL routing in IoT, followed by attack detection processing at the Base Station (BS) using log data. Data normalization is accomplished through the application of min-max normalization techniques. The most crucial features are then identified through feature selection, utilizing information gain and Support Vector Machine-Recursive Feature Elimination (SVM-RFE). Attack detection is subsequently performed using DSNFyS, which integrates a Deep Stacked Autoencoder (DSA) with an Adaptive Neuro-Fuzzy Inference System (ANFIS). Upon detection of an attack, mitigation is carried out employing a DSA trained using the Hiking Optimization Algorithm (HOA). The proposed DSNFyS demonstrated exceptional performance, achieving the better accuracy of 97.41%, True Positive Rate (TPR) of 97.60%, and True Negative Rate (TNR) of 97.12%.

Cite This Paper

Prashant Maurya, Vandana Kushwaha, "DSNFyS: Deep Stacked Neuro Fuzzy System for Attack Detection and Mitigation in RPL based IoT", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.17, No.3, pp. 62-83, 2025. DOI:10.5815/ijieeb.2025.03.05

Reference

[1]M. Rouissat, M. Belkehir, A. Mokaddem, M. Bouziani, and I. S. Alsukayti, “Exploring and mitigating hybrid rank attack in RPL-based IoT networks,” J. Electr. Eng., vol. 75, no. 3, pp. 204–213, Jun. 2024, doi: 10.2478/jee-2024-0025.
[2]D. Ray, P. Bhale, S. Biswas, P. Mitra, and S. Nandi, “A Novel Energy-Efficient Scheme for RPL Attacker Identification in IoT Networks Using Discrete Event Modeling,” IEEE Access, vol. 11, pp. 77267–77291, 2023, doi: 10.1109/ACCESS.2023.3296558.
[3]B. Alabsi, M. Anbar, and S. Rihan, “CNN-CNN: Dual Convolutional Neural Network Approach for Feature Selection and Attack Detection on Internet of Things Networks,” Sensors, vol. 23, no. 14, p. 6507, Jul. 2023, doi: 10.3390/s23146507.
[4]K. Kowsalyadevi and N. V. Balaji, “IoBTSec-RPL: A Novel RPL Attack Detecting Mechanism Using Hybrid Deep Learning Over Battlefield IoT Environment,” Int. J. Comput. Netw. Appl., vol. 10, no. 4, p. 637, Aug. 2023, doi: 10.22247/ijcna/2023/223317.
[5]L. C. Sejaphala, V. Malele, and F. Lugayizi, “High-Level Defence Model against Routing Attacks on the Internet-of-Things,” Indones. J. Comput. Sci., vol. 13, no. 1, Mar. 2024, doi: 10.33022/ijcs.v13i1.3744.
[6]I. S. Alsukayti and A. Singh, “A Lightweight Scheme for Mitigating RPL Version Number Attacks in IoT Networks,” IEEE Access, vol. 10, pp. 111115–111133, 2022, doi: 10.1109/ACCESS.2022.3215460.
[7]U. Farooq, M. Asim, N. Tariq, T. Baker, and A. I. Awad, “Multi-Mobile Agent Trust Framework for Mitigating Internal Attacks and Augmenting RPL Security,” Sensors, vol. 22, no. 12, p. 4539, Jun. 2022, doi: 10.3390/s22124539.
[8]S. M. Muzammal, R. K. Murugesan, N. Z. Jhanjhi, M. Humayun, A. O. Ibrahim, and A. Abdelmaboud, “A Trust-Based Model for Secure Routing against RPL Attacks in Internet of Things,” Sensors, vol. 22, no. 18, p. 7052, Sep. 2022, doi: 10.3390/s22187052.
[9]E. Garcia Ribera, B. Martinez Alvarez, C. Samuel, P. P. Ioulianou, and V. G. Vassilakis, “An Intrusion Detection System for RPL-Based IoT Networks,” Electronics, vol. 11, no. 23, p. 4041, Dec. 2022, doi: 10.3390/electronics11234041.
[10]R. Bokka and T. Sadasivam, “Securing IoT Networks: RPL Attack Detection with Deep Learning GRU Networks,” Int. J. Recent Eng. Sci., vol. 10, no. 2, pp. 13–21, Apr. 2023, doi: 10.14445/23497157/IJRES-V10I2P103.
[11]W. Choukri, H. Lamaazi, and N. Benamar, “RPL rank attack detection using Deep Learning,” in 2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT), Sakheer, Bahrain: IEEE, Dec. 2020, pp. 1–6. doi: 10.1109/3ICT51146.2020.9311983.
[12]R. Vatambeti and G. Mamidisetti, “Routing Attack Detection Using Ensemble Deep Learning Model for IIoT,” Inf. Dyn. Appl., vol. 2, no. 1, pp. 31–41, Mar. 2023, doi: 10.56578/ida020104.
[13]N. Moustafa, “The Bot-IoT dataset.” IEEE DataPort, Oct. 16, 2019. doi: 10.21227/R7V2-X988.
[14]M. Albishari, M. Li, R. Zhang, and E. Almosharea, “Deep learning-based early stage detection (DL-ESD) for routing attacks in Internet of Things networks,” J. Supercomput., vol. 79, no. 3, pp. 2626–2653, Feb. 2023, doi: 10.1007/s11227-022-04753-4.
[15]D. Arshad, M. Asim, N. Tariq, T. Baker, H. Tawfik, and D. Al-Jumeily Obe, “THC-RPL: A lightweight Trust-enabled routing in RPL-based IoT networks against Sybil attack,” PLOS ONE, vol. 17, no. 7, p. e0271277, Jul. 2022, doi: 10.1371/journal.pone.0271277.
[16]A. Seyfollahi, M. Moodi, and A. Ghaffari, “MFO-RPL: A secure RPL-based routing protocol utilizing moth-flame optimizer for the IoT applications,” Comput. Stand. Interfaces, vol. 82, p. 103622, Aug. 2022, doi: 10.1016/j.csi.2022.103622.
[17]T. D. Nguyen, J. Y. Khan, and D. T. Ngo, “An effective energy-harvesting-aware routing algorithm for WSN-based IoT applications,” in 2017 IEEE International Conference on Communications (ICC), Paris, France: IEEE, May 2017, pp. 1–6. doi: 10.1109/ICC.2017.7996888.
[18]Z. Ye, T. Wen, Z. Liu, X. Song, and C. Fu, “An Efficient Dynamic Trust Evaluation Model for Wireless Sensor Networks,” J. Sens., vol. 2017, pp. 1–16, 2017, doi: 10.1155/2017/7864671.
[19]M. Salehi, A. Boukerche, A. Darehshoorzadeh, and A. Mammeri, “Towards a novel trust-based opportunistic routing protocol for wireless networks,” Wirel. Netw., vol. 22, no. 3, pp. 927–943, Apr. 2016, doi: 10.1007/s11276-015-1010-4.
[20]N. A. Khalid, “Distributed Trust-based Routing Decision Making for WSN,” 2019.
[21]J. Zhu, “Wireless Sensor Network Technology Based on Security Trust Evaluation Model,” Int. J. Online Biomed. Eng. IJOE, vol. 14, no. 04, pp. 211–226, Apr. 2018, doi: 10.3991/ijoe.v14i04.8590.
[22]H. Henderi, “Comparison of Min-Max normalization and Z-Score Normalization in the K-nearest neighbor (kNN) Algorithm to Test the Accuracy of Types of Breast Cancer,” IJIIS Int. J. Inform. Inf. Syst., vol. 4, no. 1, pp. 13–20, Mar. 2021, doi: 10.47738/ijiis.v4i1.73.
[23]Y. Zhang, Q. Deng, W. Liang, and X. Zou, “An Efficient Feature Selection Strategy Based on Multiple Support Vector Machine Technology with Gene Expression Data,” BioMed Res. Int., vol. 2018, pp. 1–11, Aug. 2018, doi: 10.1155/2018/7538204.
[24]B. Prasetiyo, Alamsyah, M. A. Muslim, and N. Baroroh, “Evaluation of feature selection using information gain and gain ratio on bank marketing classification using naïve bayes,” J. Phys. Conf. Ser., vol. 1918, no. 4, p. 042153, Jun. 2021, doi: 10.1088/1742-6596/1918/4/042153.
[25]A. Dairi, F. Harrou, Y. Sun, and M. Senouci, “Obstacle Detection for Intelligent Transportation Systems Using Deep Stacked Autoencoder and $k$ -Nearest Neighbor Scheme,” IEEE Sens. J., vol. 18, no. 12, pp. 5122–5132, Jun. 2018, doi: 10.1109/JSEN.2018.2831082.
[26]M. Ragab et al., “A novel metaheuristics with adaptive neuro-fuzzy inference system for decision making on autonomous unmanned aerial vehicle systems,” ISA Trans., vol. 132, pp. 16–23, Jan. 2023, doi: 10.1016/j.isatra.2022.04.006.
[27]S. O. Oladejo, S. O. Ekwe, and S. Mirjalili, “The Hiking Optimization Algorithm: A novel human-based metaheuristic approach,” Knowl.-Based Syst., vol. 296, p. 111880, Jul. 2024, doi: 10.1016/j.knosys.2024.111880.
[28]H. T. Sadeeq and A. M. Abdulazeez, “Improved Northern Goshawk Optimization Algorithm for Global Optimization,” in 2022 4th International Conference on Advanced Science and Engineering (ICOASE), Zakho, Iraq: IEEE, Sep. 2022, pp. 89–94. doi: 10.1109/ICOASE56293.2022.10075576.
[29]S. He, Q. H. Wu, and J. R. Saunders, “Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior,” IEEE Trans. Evol. Comput., vol. 13, no. 5, pp. 973–990, Oct. 2009, doi: 10.1109/TEVC.2009.2011992.
[30]Natanael Sousa, José V.V. Sobral, Joel J.P.C. Rodrigues, Ricardo A.L. Rabêlo, and Petar Solic, “ERAOF: A new RPL protocol objective function for Internet of Things applications,” presented at the 2nd International Multidisciplinary Conference on Computer and Energy Science, SpliTech 2017, in 2017 2nd International Multidisciplinary Conference on Computer and Energy Science, SpliTech 2017. Split, Croatia: Institute of Electrical and Electronics Engineers Inc., Jul. 2017.
[31]Y. Yu, J. Li, J. Li, Y. Xia, Z. Ding, and B. Samali, “Automated damage diagnosis of concrete jack arch beam using optimized deep stacked autoencoders and multi-sensor fusion,” Dev. Built Environ., vol. 14, p. 100128, Apr. 2023, doi: 10.1016/j.dibe.2023.100128.
[32]P. R. Bhaladhare and D. C. Jinwala, “A Clustering Approach for the l -Diversity Model in Privacy Preserving Data Mining Using Fractional Calculus-Bacterial Foraging Optimization Algorithm,” Adv. Comput. Eng., vol. 2014, pp. 1–12, Sep. 2014, doi: 10.1155/2014/396529.
[33]O. Chamorro-Atalaya et al., “K-Fold Cross-Validation through Identification of the Opinion Classification Algorithm for the Satisfaction of University Students,” Int. J. Online Biomed. Eng. IJOE, vol. 19, no. 11, Aug. 2023, doi: 10.3991/ijoe.v19i11.39887.