Securing IoT Networks: A Deep Learning Strategy Against RPL Selective Forwarding Attacks
Securing IoT Networks: A Deep Learning Strategy Against RPL Selective Forwarding Attacks |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-8 |
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| Year of Publication : 2024 | ||
| Author : Ayoub Krari, Abdelmajid Hajami, Ayoub Toubi, Soukaina Mihi |
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| DOI : 10.14445/22315381/IJETT-V72I8P120 | ||
How to Cite?
Ayoub Krari, Abdelmajid Hajami, Ayoub Toubi, Soukaina Mihi,"Securing IoT Networks: A Deep Learning Strategy Against RPL Selective Forwarding Attacks," International Journal of Engineering Trends and Technology, vol. 72, no. 8, pp. 197-211, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I8P120
Abstract
In the dynamic and rapidly evolving domain of the Internet of Things (IoT), the imperative to safeguard networks against sophisticated cyber threats, notably selective forwarding attacks, has become increasingly urgent. This research introduces a novel strategy leveraging the capabilities of a Multilayer Perceptron (MLP), a sophisticated form of feedforward artificial neural network celebrated for its pattern recognition efficacy, to significantly bolster IoT network security. Motivated by the escalating complexity and subtlety of cyber threats, this study aims to develop a robust model capable of discerning and neutralizing selective forwarding attacks with high accuracy. The methodology employed encompasses the emulation of IoT environments using the Cooja Simulator for comprehensive data acquisition, focusing on network attributes essential for effective MLP analysis. The preprocessing of this data, including normalization and missing value imputation, is critical to refining the dataset for optimal analysis by the MLP. The architecture and training of the MLP are detailed, emphasizing feature selection and hyperparameter optimization to mitigate the risk of overfitting while maximizing detection capabilities. The efficacy of the proposed model is validated through empirical evaluation, employing a suite of performance metrics such as accuracy, precision, recall, and the F1 score. These metrics confirm the model's effectiveness in distinguishing between benign network behavior and potential attack scenarios, underscoring its applicability to IoT network security. Additionally, the study considers the practical integration of the MLP model within real-world IoT infrastructures, addressing the unique challenges and operational demands of such networks. Given the continuous advancement of cyber threats targeting IoT networks, the urgency of this research is evident. The proposed MLP model not only demonstrates significant potential in detecting selective forwarding attacks but also serves as a scalable and adaptable framework for enhancing the security posture of IoT networks. This investigation contributes to the cybersecurity field by offering a potent solution for protecting IoT infrastructures against an ever-evolving threat landscape, thereby ensuring their resilience and integrity in the face of sophisticated cyber threats.
Keywords
IoT, Multilayer Perceptron (MLP), Selective Forwarding Attacks, Network Security, Artificial Neural Networks (ANN), Cooja Simulator, Deep Learning, Cybersecurity, Attack Detection, Performance Metrics, Model Training and Validation.
References
[1] Ruth Ande et al., “Internet of Things: Evolution and Technologies from a Security Perspective,” Sustainable Cities and Society, vol. 54, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Abhishek Khanna, and Sanmeet Kaur, “Internet of Things (IoT), Applications and Challenges: A Comprehensive Review,” Wireless Personal Communications, vol. 114, pp. 1687-1762, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Cristina Stolojescu-Crisan, Calin Crisan, and Bogdan-Petru Butunoi, “An IoT-Based Smart Home Automation System,” Sensors, vol. 21, no. 11, pp. 1-23, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Usman Tariq et al., “A Critical Cybersecurity Analysis and Future Research Directions for the Internet of Things: A Comprehensive Review,” Sensors, vol. 23, no. 8, pp. 1-46, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Hussam Shamseldin Zenalabdin, Abudhahir Buhari, and Tadiwa Elisha Nyamasvisva, “Performance Analysis of IoT Protocol Stack over Dense and Sparse Mote Network Using COOJA Simulator,” The 2nd Joint International Conference on Emerging Computing Technology and Sports, Bandung, Indonesia, vol. 1529, pp. 1-8, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Jun Jiang, and Yuhong Liu, “Secure IoT Routing: Selective Forwarding Attacks and Trust-Based Defenses in RPL Network,” arXiv, pp. 1-12, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Haitham Y. Adarbah et al., “Security Challenges of Selective Forwarding Attack and Design a Secure ECDH-Based Authentication Protocol to Improve RPL Security,” IEEE Access, vol. 11, pp. 11268-11280, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Behnam Farzaneh et al., “An Anomaly-Based IDS for Detecting Attacks in RPL-Based Internet of Things,” 2019 5th International Conference on Web Research, Tehran, Iran, pp. 61-66, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Andrea Agiollo et al., “DETONAR: Detection of Routing Attacks in RPL-Based IoT,” IEEE Transactions on Network and Service Management, vol. 18, no. 2, pp. 1178-1190, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Nitin Shivaraman, “Clustering-Based Solutions for Energy Efficiency, Adaptability and Resilience in IoT Networks,” IGS Theses, Nanyang Technological University, pp. 1-144, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Amsale Zelalem Bayih et al., “Utilization of Internet of Things and Wireless Sensor Networks for Sustainable Smallholder Agriculture,” Sensors, vol. 22, no. 9, pp. 1-31, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Mudhafar Nuaimi, Lamia Chaari Fourati, and Bassem Ben Hamed, “Intelligent Approaches Toward Intrusion Detection Systems for Industrial Internet of Things: A Systematic Comprehensive Review,” Journal of Network and Computer Applications, vol. 215, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Abujassar Radwan, “Improving Energy Efficiency Using IoT Technology through the Development of a Smart Network Clustering Path Determined by the Distance between Nodes,” Research Square, pp. 1-35, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Alazab Ammar et al., “Routing Attacks Detection in 6LoWPAN-Based Internet of Things,” Electronics, vol. 12, no. 6, pp. 1-19, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Pintu Kumar Sadhu, Venkata P. Yanambaka, and Ahmed Abdelgawad, “Internet of Things: Security and Solutions Survey,” Sensors, vol. 22, no. 19, pp. 1-51, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Mohammad Al-Fawa’reh et al., “MalBoT-DRL: Malware Botnet Detection Using Deep Reinforcement Learning in IoT Networks,” IEEE Internet of Things Journal, vol. 11, no. 6, pp. 9610-9629, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Rashmi Sahay, G. Geethakumari, and Barsha Mitra, “A Novel Network Partitioning Attack Against Routing Protocol in Internet of Things,” Ad Hoc Networks, vol. 121, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Anum Talpur, and Mohan Gurusamy, “Machine Learning for Security in Vehicular Networks: A Comprehensive Survey,” IEEE Communications Surveys & Tutorials, vol. 24, no. 1, pp. 346-379, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Syeda M. Muzammal, Raja Kumar Murugesan, and N.Z. Jhanjhi, “A Comprehensive Review on Secure Routing in Internet of Things: Mitigation Methods and Trust-Based Approaches,” IEEE Internet of Things Journal, vol. 8, no. 6, pp. 4186-4210, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Jarmouni Ezzitouni et al., “Management of Battery Charging and Discharging in a Photovoltaic System with Variable Power Demand Using Artificial Neural Networks,” The 4th International Conference of Computer Science and Renewable Energies, vol. 297, pp. 1-5, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Rubén Juárez, and Borja Bordel, “Augmenting Vehicular Ad Hoc Network Security and Efficiency with Blockchain: A Probabilistic Identification and Malicious Node Mitigation Strategy,” Electronics, vol. 12, no. 23, pp. 1-35, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Mohamed Massaoudi et al., “A Novel Stacked Generalization Ensemble-Based Hybrid LGBM-XGB-MLP Model for Short-Term Load Forecasting,” Energy, vol. 214, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Ishak Pacal, “A Novel Swin Transformer Approach Utilizing Residual Multi-Layer Perceptron for Diagnosing Brain Tumors in MRI Images,” International Journal of Machine Learning and Cybernetics, pp. 1-46, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Lei Deng et al., “Model Compression and Hardware Acceleration for Neural Networks: A Comprehensive Survey,” Proceedings of the IEEE, vol. 108, no. 4, pp. 485-532, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Shahid Allah Bakhsh et al., “Enhancing IoT Network Security through Deep Learning-Powered Intrusion Detection System,” Internet of Things, vol. 24, pp. 1-36, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Seyed-Ahmad Ahmadi et al., “Modern Machine-Learning Can Support Diagnostic Differentiation of Central and Peripheral Acute Vestibular Disorders,” Journal of Neurology, vol. 267, pp. 143-152, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Ezzitouni Jarmouni et al., “The Implementation of an Optimized Neural Network in a Hybrid System for Energy Management,” International Journal of Power Electronics and Drive Systems, vol. 15, no. 2, pp. 815-823, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Theogene Rizinde, Innocent Ngaruye, and Nathan D. Cahill, “Comparing Machine Learning Classifiers for Predicting Hospital Readmission of Heart Failure Patients in Rwanda,” Journal of Personalized Medicine, vol. 13, no. 9, pp. 1-20, 2023.
[CrossRef] [Google Scholar] [Publisher Link]