Dynamic Arithmetic Optimization Algorithm with Deep Learning-based Intrusion Detection System in Wireless Sensor Networks
Received: 14 August 2024 | Revised: 26 August 2024 | Accepted: 4 September 2024 | Online: 5 November 2024
Corresponding author: K. Nirmal
Abstract
A Wireless Sensor Network (WSN) encompasses interconnected Sensor Nodes (SNs) that interact wirelessly to collect and transfer data. Security in the context of WNS refers to protocols and measures implemented for the overall functionality of the network, along with protecting the availability, confidentiality, and integrity of data against tampering, unauthorized access, and other possible security risks. An Intrusion Detection System (IDS) utilizing Deep Learning (DL) and Feature Selection (FS) leverages advanced methods to enhance effectiveness in the detection of malicious activities in a network by enhancing relevant data features and leveraging the power of Deep Neural Networks (DNNs). This study presents a Dynamic Arithmetic Optimization Algorithm within a DL-based IDS (DAOADL-IDS) in WSNs. The purpose of DAOADL-IDS is to recognize and classify intrusions in a WSN using a metaheuristic algorithm and DL models. To accomplish this, the DAOADL-IDS technique utilizes a Z-score data normalization approach to resize the input dataset in a compatible format. In addition, DAOADL-IDS employs a DAOA-based FS (DAOA-FS) model to select an optimum set of features. A Stacked Deep Belief Network (SDBN) model is employed for the Intrusion Detection (ID) process. The hyperparameter selection of the SDBN model is accomplished using the Bird Swarm Algorithm (BSA). A wide experimental analysis of the proposed DAOADL-IDS method was performed on a benchmark dataset. The performance validation of the DAOADL-IDS technique showed an accuracy of 99.68%, demonstrating superior performance over existing techniques under various measures.
Keywords:
intrusion detection system, deep learning, bird swarm algorithm, wireless sensor network, feature selectionDownloads
References
W. Fang, W. Zhang, W. Chen, Y. Liu, and C. Tang, "TMSRS: trust management-based secure routing scheme in industrial wireless sensor network with fog computing," Wireless Networks, vol. 26, no. 5, pp. 3169–3182, Jul. 2020.
N. Sahar, R. Mishra, and S. Kalam, "Deep Learning Approach-Based Network Intrusion Detection System for Fog-Assisted IoT," in Proceedings of International Conference on Big Data, Machine Learning and their Applications, Singapore, 2021, pp. 39–50.
S. Pundir, M. Wazid, D. P. Singh, A. K. Das, J. J. P. C. Rodrigues, and Y. Park, "Intrusion Detection Protocols in Wireless Sensor Networks Integrated to Internet of Things Deployment: Survey and Future Challenges," IEEE Access, vol. 8, pp. 3343–3363, 2020.
M. P. Ramkumar, T. Daniya, P. Mano Paul, and S. Rajakumar, "Intrusion detection using optimized ensemble classification in fog computing paradigm," Knowledge-Based Systems, vol. 252, Sep. 2022, Art. no. 109364.
C. A. de Souza, C. B. Westphall, R. B. Machado, L. Loffi, C. M. Westphall, and G. A. Geronimo, "Intrusion detection and prevention in fog based IoT environments: A systematic literature review," Computer Networks, vol. 214, Sep. 2022, Art. no. 109154.
A. A. Abdussami and Dr. M. F. Farooqui, "Incremental deep neural network intrusion detection in fog based IoT environment: An optimization assisted framework," Indian Journal of Computer Science and Engineering, vol. 12, no. 6, pp. 1847–1859, Dec. 2021.
M. A. Rahman, A. T. Asyhari, L. S. Leong, G. B. Satrya, M. Hai Tao, and M. F. Zolkipli, "Scalable machine learning-based intrusion detection system for IoT-enabled smart cities," Sustainable Cities and Society, vol. 61, Oct. 2020, Art. no. 102324.
B. Mopuru and Y. Pachipala, "Advancing IoT Security: Integrative Machine Learning Models for Enhanced Intrusion Detection in Wireless Sensor Networks," Engineering, Technology & Applied Science Research, vol. 14, no. 4, pp. 14840–14847, Aug. 2024.
B. Mopuru and Y. Pachipala, "Enhanced Intrusion Detection in IoT with a Novel PRBF Kernel and Cloud Integration," Engineering, Technology & Applied Science Research, vol. 14, no. 4, pp. 14988–14993, Aug. 2024.
S. Subramani and M. Selvi, "Intrusion detection system using RBPSO and fuzzy neuro-genetic classification algorithms in wireless sensor networks," International Journal of Information and Computer Security, vol. 20, no. 3–4, pp. 439–461, Jan. 2023.
M. Maheswari and R. A. Karthika, "A Novel QoS Based Secure Unequal Clustering Protocol with Intrusion Detection System in Wireless Sensor Networks," Wireless Personal Communications, vol. 118, no. 2, pp. 1535–1557, May 2021.
P. S. Khot and U. Naik, "Taylor CMVO: Taylor Competitive Multi-Verse Optimizer for intrusion detection and cellular automata-based secure routing in WSN," International Journal of Intelligent Robotics and Applications, vol. 6, no. 2, pp. 306–322, Jun. 2022.
R. Zhang, J. Zhang, Q. Wang, and H. Zhang, "DOIDS: An Intrusion Detection Scheme Based on DBSCAN for Opportunistic Routing in Underwater Wireless Sensor Networks," Sensors, vol. 23, no. 4, Jan. 2023, Art. no. 2096.
T. Vaiyapuri, V. S. Parvathy, V. Manikandan, N. Krishnaraj, D. Gupta, and K. Shankar, "A Novel Hybrid Optimization for Cluster‐Based Routing Protocol in Information-Centric Wireless Sensor Networks for IoT Based Mobile Edge Computing," Wireless Personal Communications, vol. 127, no. 1, pp. 39–62, Nov. 2022.
D. R. K. Chougale, "Intrusion Detection System based on Energy Efficient Dynamic Clustering in a Heterogeneous Environment of Wireless Sensor Networks (WSNs)," Neuro Quantology, vol. 20, no. 9, pp. 4756–4766, 2022.
P. Aruchamy, S. Gnanaselvi, D. Sowndarya, and P. Naveenkumar, "An artificial intelligence approach for energy-aware intrusion detection and secure routing in internet of things-enabled wireless sensor networks," Concurrency and Computation: Practice and Experience, vol. 35, no. 23, 2023, Art. no. e7818.
V. K. Kalimuthu and R. Velumani, "Modeling of Intrusion Detection System Using Double Adaptive Weighting Arithmetic Optimization Algorithm with Deep Learning on Internet of Things Environment," Brazilian Archives of Biology and Technology, vol. 67, May 2024, Art. no. e24231010.
M. Sakthimohan, J. Deny, and G. E. Rani, "Secure deep learning-based energy efficient routing with intrusion detection system for wireless sensor networks," Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8587–8603, Jan. 2024.
J. Srivastava and J. Prakash, "Multi-modal for Energy Optimization and Intrusion Detection in Wireless Sensor Networks," Wireless Personal Communications, vol. 133, no. 1, pp. 289–321, Nov. 2023.
R. Devendiran and A. V. Turukmane, "Dugat-LSTM: Deep learning based network intrusion detection system using chaotic optimization strategy," Expert Systems with Applications, vol. 245, Jul. 2024, Art. no. 123027.
Y. Qiu, L. Wu, C. Zuo, R. Jia, and H. Zhang, "Optimal scheduling of solar-surface water source heat pump system based on an improved arithmetic optimization algorithm," Egyptian Informatics Journal, vol. 24, no. 4, Dec. 2023, Art. no. 100415.
M. Mafarja et al., "Classification framework for faulty-software using enhanced exploratory whale optimizer-based feature selection scheme and random forest ensemble learning," Applied Intelligence, vol. 53, no. 15, pp. 18715–18757, Aug. 2023.
F. A. Hashim, R. A. Khurma, D. Albashish, M. Amin, and A. G. Hussien, "Novel hybrid of AOA-BSA with double adaptive and random spare for global optimization and engineering problems," Alexandria Engineering Journal, vol. 73, pp. 543–577, Jul. 2023.
I. Almomani, B. Al-Kasasbeh, and M. AL-Akhras, "WSN-DS: A Dataset for Intrusion Detection Systems in Wireless Sensor Networks," Journal of Sensors, vol. 2016, no. 1, 2016, Art. no. 4731953.
G. Liu, H. Zhao, F. Fan, G. Liu, Q. Xu, and S. Nazir, "An Enhanced Intrusion Detection Model Based on Improved kNN in WSNs," Sensors, vol. 22, no. 4, Jan. 2022, Art. no. 1407.
P. Gite, K. Chouhan, K. Murali Krishna, C. Kumar Nayak, M. Soni, and A. Shrivastava, "ML Based Intrusion Detection Scheme for various types of attacks in a WSN using C4.5 and CART classifiers," Materials Today: Proceedings, vol. 80, pp. 3769–3776, Jan. 2023.
A. Singh, J. Amutha, J. Nagar, S. Sharma, and C. C. Lee, "AutoML-ID: automated machine learning model for intrusion detection using wireless sensor network," Scientific Reports, vol. 12, no. 1, May 2022, Art. no. 9074.
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