Advancing IoT Security: Integrative Machine Learning Models for Enhanced Intrusion Detection in Wireless Sensor Networks
Received: 26 April 2024 | Revised: 5 May 2024 | Accepted: 11 May 2024 | Online: 3 June 2024
Corresponding author: Bhargavi Mopuru
Abstract
This paper introduces a breakthrough approach to enhancing intrusion detection capabilities within Wireless Sensor Networks (WSNs) by implementing the Enhanced Wireless Intrusion Detection System (EW-IDS). Leveraging a sophisticated blend of Machine Learning (ML) algorithms, including Principal Component Analysis (PCA) and Singular Value Decomposition (SVD), the proposed model effectively streamlines feature selection, resulting in a robust detection framework. Extensive evaluations demonstrate that EW-IDS not only achieves a high accuracy rate of 96%, but also consistently surpasses traditional models in precision, recall, and F1 Score metrics. These achievements underscore the model’s superior ability to differentiate between benign and malicious network activities. The implementation of EW-IDS marks a significant advance in securing the Internet of Things (IoT) environments against a diverse range of cyber threats, enhancing both the security protocols and operational efficiency of WSNs. This study provides a novel intrusion detection solution and offers valuable insights into the application of ML in complex security settings.
Keywords:
wireless sensor networks, intrusion detection systems, machine learning, PCA, enhanced security protocols, cyber rthreat detection, IoTDownloads
References
R. Doriguzzi-Corin, L. A. D. Knob, L. Mendozzi, D. Siracusa, and M. Savi, "Introducing Packet-Level Analysis in Programmable Data Planes to Advance Network Intrusion Detection." arXiv, Jan. 04, 2024.
M. A. Talukder et al., "Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction." arXiv, Jan. 22, 2024.
C.-F. Tsai, Y.-F. Hsu, C.-Y. Lin, and W.-Y. Lin, "Intrusion detection by machine learning: A review," Expert Systems with Applications, vol. 36, no. 10, pp. 11994–12000, Dec. 2009.
M. Karthikeyan, D. Manimegalai, and K. RajaGopal, "Firefly algorithm based WSN-IoT security enhancement with machine learning for intrusion detection," Scientific Reports, vol. 14, no. 1, Jan. 2024, Art. no. 231.
A. Paya, S. Arroni, V. García-Díaz, and A. Gómez, "Apollon: A robust defense system against Adversarial Machine Learning attacks in Intrusion Detection Systems," Computers & Security, vol. 136, Jan. 2024, Art. no. 103546.
A. M. S. Saleh, "A Power-Aware Method for IoT Networks with Mobile Stations and Dynamic Power Management Strategy," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12108–12114, Dec. 2023.
C. SaiTeja and J. B. Seventline, "A hybrid learning framework for multi-modal facial prediction and recognition using improvised non-linear SVM classifier," AIP Advances, vol. 13, no. 2, Feb. 2023, Art. no. 025316.
Y. Ahmed, M. A. Azad, and T. Asyhari, "Rapid Forecasting of Cyber Events Using Machine Learning-Enabled Features," Information, vol. 15, no. 1, Jan. 2024, Art. no. 36.
S. Zafar, G. Miraj, R. Baloch, D. Murtaza, and K. Arshad, "An IoT Based Real-Time Environmental Monitoring System Using Arduino and Cloud Service," Engineering, Technology & Applied Science Research, vol. 8, no. 4, pp. 3238–3242, Aug. 2018.
S. Chopparapu and J. B. Seventline, "An Efficient Multi-modal Facial Gesture-based Ensemble Classification and Reaction to Sound Framework for Large Video Sequences," Engineering, Technology & Applied Science Research, vol. 13, no. 4, pp. 11263–11270, Aug. 2023.
Downloads
How to Cite
License
Copyright (c) 2024 Bhargavi Mopuru, Yellamma Pachipala
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
