Published April 30, 2025 | Version CC-BY-NC-ND 4.0
Journal article Open

Anomalies Detection in Wireless Sensor Networks with Exploring Various Machine Learning Techniques: Review

Creators

  • 1. Research Scholar, Department of Computer Science and Engineering, Jagannath University Bahadurgarh (Delhi NCR), India.

Contributors

Contact person:

Researcher:

  • 1. Research Scholar, Department of Computer Science and Engineering, Jagannath University Bahadurgarh (Delhi NCR), India.
  • 2. Associate Professor, Department of Computer Science and Engineering, Jagannath University Bahadurgarh (Delhi NCR), India.

Description

Abstract: Wireless Sensor Networks (WSNs) form the backbone of numerous critical applications, ranging from environmental monitoring to defense surveillance, necessitating highly reliable anomaly detection systems to ensure operational integrity and security. Traditional anomaly detection methods in WSNs often grapple with the high dimensionality of sensor data, dynamic environmental conditions, and resource constraints, leading to suboptimal performance. This research paper introduces a novel framework that leverages advanced machine learning techniques, focusing on utilizing deep learning techniques that can markedly improve the precision in identifying irregularities within Wireless Sensor Networks (WSNs). By employing a comprehensive methodology that encompasses data preprocessing, feature engineering, and the deployment of sophisticated Models based on deep learning, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), this study demonstrates a marked improvement in detecting abnormal events within sensor data streams. The proposed models are evaluated against traditional machine learning benchmarks on a collection of performance indicators such as correctness, exactness, sensitivity, and the F1 metric., showcasing their superior ability to generalize and detect anomalies under varied conditions. This research not only addresses the inherent challenges faced by WSNs but also sets a precedent for the integration of cutting-edge machine learning algorithms in enhancing network reliability and security. The outcomes of this research hold considerable importance for advancing anomaly detection within Wireless Sensor Networks (WSNs), setting the stage for developing more robust and smart systems.

Files

D458814040425.pdf

Files (419.6 kB)

Name Size Download all
md5:22bd3273fcfdb78ea5c45d0327ff13be
419.6 kB Preview Download

Additional details

Identifiers

DOI
10.35940/ijeat.D4588.14040425
EISSN
2249-8958

Dates

Accepted
2025-04-15
Manuscript received on 07 December 2024 | First Revised Manuscript received on 17 December 2024 | Second Revised Manuscript received on 27 March 2025 | Manuscript Accepted on 15 April 2025 | Manuscript published on 30 April 2025.

References

  • Poornima, I. G. A., & Paramasivan, B. (2020). Anomaly detection in wireless sensor network using a machine learning algorithm. Computer Communications. DOI: https://doi.org/10.1016/j.comcom.2020.01.005
  • Samir Ifzarne et al 2021 J. Phys.: Conf. Ser. 1743 012021 DOI: https://doi.org/10.1088/1742-6596/1743/1/012021.
  • An, J.H., Wang, Z. & Joe, I. A CNN-based automatic vulnerability detection. J Wireless Com Network 2023, 41 (2023). DOI: https://doi.org/10.1186/s13638-023-02255-2
  • Harer, J., Ozdemir, O., Lazovich, T., Reale, C., Russell, R., & Kim, L. (2018). Learning to repair software vulnerabilities with generative adversarial networks. Advances in neural information processing systems, 31. DOI: https://doi.org/10.48550/arXiv.1805.07475
  • Kilimci, Z. H., & Akyokuş, S. (2019, September). The evaluation of word embedding models and deep learning algorithms for Turkish text classification. In 2019 4th International Conference on Computer Science and Engineering (UBMK) (pp. 548-553). DOI: https://doi.org/10.1109/UBMK.2019.8907027
  • B. Zhou, A. Khosla, A. Lapedriza, A. Oliva and A. Torralba, "Learning Deep Features for Discriminative Localization," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 2921-2929, DOI: https://doi.ieeecomputersociety.org/10.1109/CVPR.2016.319.
  • Li, Z., Zou, D., Xu, S., Ou, X., Jin, H., Wang, S., ... & Zhong, Y. (2018). Vuldeepecker: A deep learning-based system for vulnerability detection. DOI: https://doi.org/10.48550/arXiv.1801.01681
  • An, J.H., Wang, Z. & Joe, I. A CNN-based automatic vulnerability detection. J Wireless Com Network 2023, 41 (2023). DOI: https://doi.org/10.1186/s13638-023-02255-2
  • F. Hussain, R. Hussain, S. A. Hassan and E. Hossain, "Machine Learning in IoT Security: Current Solutions and Future Challenges," in IEEE Communications Surveys & Tutorials, vol. 22, no. 3, pp. 1686- 1721, third quarter 2020, DOI: https://doi.org/10.1109/COMST.2020.2986444
  • H. H. Pajouh, R. Javidan, R. Khayami, A. Dehghantanha and K. -K. R. Choo, "A Two-Layer Dimension Reduction and Two-Tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks," in IEEE Transactions on Emerging Topics in Computing, vol. 7, no. 2, pp. 314-323, 1 April-June 2019, DOI: https://doi.org/10.1109/TETC.2016.2633228.
  • M. Kalash, M. Rochan, N. Mohammed, N. D. B. Bruce, Y. Wang and F. Iqbal, "Malware Classification with Deep Convolutional Neural Networks," 2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS), Paris, France, 2018, pp. 1- 5, DOI: https://doi.org/10.1109/NTMS.2018.8328749.
  • Russell, R., Kim, L., Hamilton, L., Lazovich, T., Harer, J., Ozdemir, O, & McConkey, M. (2018, December). Automated vulnerability detection in source code using deep representation learning. In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 757-762). DOI: https://doi.org/10.48550/arXiv.1807.04320.
  • Russell, R., Kim, L., Hamilton, L., Lazovich, T., Harer, J., Ozdemir, O., & McConkey, M. (2018, December). Automated vulnerability detection in source code using deep representation learning. In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 757-762). DOI: https://doi.org/10.48550/arXiv.1807.04320
  • Ahmadi, M., Ulyanov, D., Semenov, S., Trofimov, M., & Giacinto, G. (2016, March). Novel feature extraction, selection, and fusion for effective malware family classification. In Proceedings of the sixth ACM conference on data and application security and privacy (pp. 183-194). DOI: https://doi.org/10.48550/arXiv.1511.04317
  • An, J. H., Wang, Z., & Joe, I. (2023). A CNN-based automatic vulnerability detection. EURASIP Journal on Wireless Communications and Networking, 2023(1),41. DOI: https://doi.org/10.1186/s13638-023- 02255-2
  • Harer, J., Ozdemir, O., Lazovich, T., Reale, C., Russell, R., & Kim, L. (2018). Learning to repair software vulnerabilities with generative adversarial networks. Advances in neural information processing systems, 31. DOI: https://doi.org/10.48550/arXiv.1805.07475.
  • H. H. Pajouh, R. Javidan, R. Khayami, A. Dehghantanha and K. -K. R. Choo, "A Two-Layer Dimension Reduction and Two-Tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks," in IEEE Transactions on Emerging Topics in Computing, vol. 7, no. 2, pp. 314-323, 1 April-June 2019, DOI: https://doi.org/10.1109/TETC.2016.2633228
  • M. Kalash, M. Rochan, N. Mohammed, N. D. B. Bruce, Y. Wang and F. Iqbal, "Malware Classification with Deep Convolutional Neural Networks," 2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS), Paris, France, 2018, pp. 1- 5, DOI: https://doi.org/10.1109/NTMS.2018.8328749.
  • H. Jain, A. Vikram, Mohana, A. Kashyap and A. Jain, "Weapon Detection using Artificial Intelligence and Deep Learning for Security Applications," 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India, 2020, pp. 193-198, DOI: https://doi.org/10.1109/ICESC48915.2020.9155832.
  • Pathak, Ms. N. S., Patil, Dr. S., & Patil, Dr. P. (2019). Anomaly Detection in Engineering Structures using WSN and Machine Learning. In International Journal of Innovative Technology and Exploring Engineering (Vol. 9, Issue 1, pp. 3757–3760). DOI: https://doi.org/10.35940/ijitee.a4816.119119
  • Patil, Mrs. Suvarna. S., & Vidyavathi, Dr. B. M. (2022). Application o f Advanced Machine Learning and Artificial Neural Network Methods in Wireless Sensor Networks Based Applications. In International Journal of Engineering and Advanced Technology (Vol. 11, Issue 3, pp. 103–109). DOI: https://doi.org/10.35940/ijeat.c3394.0211322
  • Lalar, S., Bhushan, S., & A.P., S. (2019). Exploration of Detection Method of Clone Attack in Wireless Sensor Network. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 4, pp. 2440–2448). DOI: https://doi.org/10.35940/ijrte.d7192.118419
  • Chitransh, A., & Kalyan, B. S. (2021). ARM Microcontroller Based Wireless Industrial Automation System. In Indian Journal of Microprocessors and Microcontroller (Vol. 1, Issue 2, pp. 8–11). DOI: https://doi.org/10.54105/ijmm.b1705.091221
  • Pramod, K., Mrs. Durga, M., Apurba, S., & Shashank, S. (2023). An Efficient LEACH Clustering Protocol to Enhance the QoS of WSN. In Indian Journal of Artificial Intelligence and Neural Networking (Vol. 3, Issue 3, pp. 1–8). DOI: https://doi.org/10.54105/ijainn.a3822.043323