2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 20 , Issue 1 , PP: 114-130, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Anushri Narendra Pathak 1 * , Arvind R. Yadav 2
Doi: https://doi.org/10.54216/FPA.200109
In applications related to military operations, Wireless Sensor Military Networks (WSMNs) aid a critical function by deploying a distributed group of sensor nodes. Such sensor networks lift the overall effectiveness of military activities by situational alertness and permitting instantaneous decision-making processes. This deployment also rises noteworthy challenges, namely scalability, energy efficiency, and security vulnerabilities. Ensuring the accessibility, trustfulness and confidentiality of the data sensed by sensor nodes is prime important challenge. It could lead to disastrous consequences on the military field. Looking into this shortfall, ongoing research is mainly targeted at obtaining advanced solutions to such challenges, such as secure and energy-efficient routing algorithms. However, one of the considerable challenges in WSNs is anomaly detection and the existence of false alarms. This can affect the dependability and effectiveness of the system. The ongoing research in this field focuses on exploring the condition of WSMN, mainly their applications, challenges, and future directions. Authors propose an adaptive and hybrid Machine Learning (ML) approach to reduce false alarms and anomaly detection along considering mutual authentication system. ML approaches offer reliable solutions by improving the data classification accuracy and detection of anomalies. These algorithms have better capability to distinguish between normal and abnormal events, which ultimately reduces false triggers. The authors propose a hybrid approach of k-Nearest Neighbors (KNN) and Decision Tree (DT), which results in a powerful method for improved classification accurateness and robustness in WSN. The effectiveness of KNN in local decision-making and better clear interpretability of Decision Tree to handle feature interactions are combined together in this strategy, to increase overall performance.
Wireless Sensor Military Networks (WSMN) , False Alarms , Energy-Aware , Machine learning , Decision-Tree , K-nearest neighbor
[1] M. P. Đurišić, Z. Tafa, G. Dimić, and V. Milutinović, "A survey of military applications of wireless sensor networks," in 2012 Mediterranean Conference on Embedded Computing (MECO), 2012, pp. 196-199.
[2] G. Singh, "Security attacks and defense mechanisms in wireless sensor network: A survey," International Journal of Innovative Science, Engineering & Technology, vol. 3, no. 4, pp. 129-136, Apr. 2016.
[3] F. T. Giuntini, D. M. Beder, and J. Ueyama, "Exploiting self-organization and fault tolerance in wireless sensor networks: A case study on wildfire detection application," International Journal of Distributed Sensor Networks, vol. 13, no. 4, pp. 1550147717704120, Apr. 2017.
[4] S. Pragadeswaran, S. Madhumitha, and S. Gopinath, "Certain investigation on military applications of wireless sensor network," International Journal of Advanced Research in Science, Communication and Technology, vol. 3, no. 1, pp. 14-19, Mar. 2021.
[5] M. A. Talukder, M. M. Islam, M. A. Uddin, A. Akhter, K. F. Hasan, and M. A. Moni, "Machine learning-based lung and colon cancer detection using deep feature extraction and ensemble learning," Expert Systems with Applications, vol. 205, p. 117695, Nov. 2022.
[6] M. A. Talukder, K. F. Hasan, M. M. Islam, M. A. Uddin, A. Akhter, M. A. Yousuf, F. Alharbi, and M. A. Moni, "A dependable hybrid machine learning model for network intrusion detection," Journal of Information Security and Applications, vol. 72, p. 103405, Feb. 2023.
[7] A. Heidari and M. A. Jabraeil Jamali, "Internet of Things intrusion detection systems: a comprehensive review and future directions," Cluster Computing, vol. 26, no. 6, pp. 3753-3780, Dec. 2023.
[8] A. Sezgin and A. Boyacı, "AID4I: An Intrusion Detection Framework for Industrial Internet of Things Using Automated Machine Learning," Computers, Materials & Continua, vol. 76, no. 2, 2023.
[9] T. M. Ghazal, "Data Fusion-based machine learning architecture for intrusion detection," Computers, Materials & Continua, vol. 70, no. 2, pp. 3399-3413, 2022.
[10] S. Ifzarne, H. Tabbaa, I. Hafidi, and N. Lamghari, "Anomaly detection using machine learning techniques in wireless sensor networks," Journal of Physics: Conference Series, vol. 1743, p. 012021, 2021.
[11] S. Sharmin, I. Ahmedy, and R. Md Noor, "An energy-efficient data aggregation clustering algorithm for wireless sensor networks using hybrid PSO," Energies, vol. 16, no. 5, p. 2487, Mar. 2023.
[12] X. Tan, S. Su, Z. Huang, X. Guo, Z. Zuo, X. Sun, and L. Li, "Wireless sensor networks intrusion detection based on SMOTE and the random forest algorithm," Sensors, vol. 19, no. 1, p. 203, Jan. 2019.
[13] N. M. Alruhaily and D. M. Ibrahim, "A multi-layer machine learning-based intrusion detection system for wireless sensor networks," International Journal of Advanced Computer Science and Applications, vol. 12, no. 4, pp. 281-288, 2021.
[14] R. Rathore and M. Hussain, "Simple, secure, efficient, lightweight and token based protocol for mutual authentication in wireless sensor networks," in Emerging Research in Computing, Information, Communication and Applications: ERCICA 2015, vol. 1, 2015, pp. 451-462.
[15] F. T. Giuntini, D. M. Beder, and J. Ueyama, "Exploiting self-organization and fault tolerance in wireless sensor networks: A case study on wildfire detection application," International Journal of Distributed Sensor Networks, vol. 13, no. 4, pp. 1550147717704120, Apr. 2017.
[16] D. Sudaroli Vijayakumar and S. Ganapathy, "Machine learning approach to combat false alarms in wireless intrusion detection system," Computer and Information Science, vol. 11, no. 3, pp. 67-81, 2018.
[17] I. G. Poornima and B. Paramasivan, "Anomaly detection in wireless sensor network using machine learning algorithm," Computer Communications, vol. 151, pp. 331-337, Feb. 2020.
[18] R. F. Leppänen and T. Hämäläinen, "Network anomaly detection in wireless sensor networks: A review," in Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 19th International Conference, NEW2AN 2019, and 12th Conference, ruSMART 2019, St. Petersburg, Russia, Aug. 26–28, 2019, pp. 196-207.
[19] T. Allaoui, M. H. Jeridi, and T. Ezzedine, "False alarm reduction in WSN surveillance application through ML techniques," in 2023 International Wireless Communications and Mobile Computing (IWCMC), 2023, pp. 996-1001.
[20] L. Cui, Y. Qu, L. Gao, G. Xie, and S. Yu, "Detecting false data attacks using machine learning techniques in smart grid: A survey," Journal of Network and Computer Applications, vol. 170, p. 102808, Nov. 2020.
[21] S. Rosset and A. Inger, "KDD-Cup 99: Knowledge Discovery in a Charitable Organization’s Donor Database," SIGKDD Explorations, vol. 1, pp. 85-90, 2000.
[22] N. Paulauskas and J. Auskalnis, "Analysis of data pre-processing influence on intrusion detection using NSL-KDD dataset," in 2017 Open Conference of Electrical, Electronic and Information Sciences (eStream), 2017, pp. 1-5.
[23] M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, "A detailed analysis of the KDD CUP 99 data set," in 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, 2009, pp. 1-6.
[24] DARPA Intrusion Detection Evaluation. Available online: https://archive.ll.mit.edu/ideval/data/2000data.
[25] J. Zhang, R. Shankaran, M. A. Orgun, A. Sattar, and V. Varadharajan, "A dynamic authentication scheme for hierarchical wireless sensor networks," in International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services, 2010, pp. 186-197.
[26] L. Cheng, J. Niu, J. Cao, S. K. Das, and Y. Gu, "QoS aware geographic opportunistic routing in wireless sensor networks," IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 7, pp. 1864-1875, Jul. 2014.
[27] D. R. Raymond and S. F. Midkiff, "Denial-of-service in wireless sensor networks: Attacks and defenses," IEEE Pervasive Computing, vol. 7, no. 1, pp. 74-81, Jan. 2008.
[28] H. Om and A. Kundu, "A hybrid system for reducing the false alarm rate of anomaly intrusion detection system," in 2012 1st International Conference on Recent Advances in Information Technology (RAIT), 2012, pp. 131-136.
