Mathematical modeling for fault detection and anomaly identification in IoT networks
*V. Dankan GowdaCorresponding authordankan.v@bmsit.inDepartment of Electronics and Communication EngineeringBMS Institute of Technology and ManagementBangalore, Karnataka, 560119, IndiaView full profile → , Prashant A Athavaleprashanth@bmsit.inDepartment of Electrical and Electronics EngineeringBMS Institute of Technology and ManagementBangalore, Karnataka, 560119, IndiaView full profile → , Shilpa K GowdaShilpakramesh15@gmail.comDepartment of Electronics and communicationNo. 67, BGS Health & Education CitySJB institute of TechnologyBengaluru, Karnataka, 560060, IndiaView full profile → , Annepu Arudrayarudra@gmail.comDepartment of Computer Science and EngineeringRajiv Gandhi Institute of TechnologyBangalore, Karnataka, 560032, IndiaView full profile → , Hariprasad Sonihr.soni@sibmhyd.edu.inAffiliation 1Department of FinanceSymbiosis Institute of Business ManagementHyderabad, Telangana, 509217, IndiaAffiliation 2Symbiosis International (Deemed University)Pune, Maharashtra, 412115, IndiaView full profile → , R. Shekharshekhar.r@alliance.edu.inDepartment of Computer Science and EngineeringAlliance UniversityBangalore, Karnataka, 562106, IndiaView full profile → , Raghavendra Mraghavendrabgsit@gmail.comDepartment of MBAFaculty of Engineering, Management and Technology (BGSIT-ACU)Mandya, Karnataka, 571448, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 04 Mar 2025
- Published Online:
- 31 Oct 2025
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2131
- Pages:
- 2279–2289
Abstract
Keywords
Subject Classifications
References
[1] M. Ibrahim and K. Saleem, “IoT Network Anomaly Detection in Smart Homes Using Machine Learning,” IEEE Access, vol. 11, pp. 119462–119480 (2023).
[2] K. Mithran and C. Gopi, “Anomaly Detection in IoT Sensor Networks Using Machine Learning,” in Proc. 2022 Int. Conf. Computing, Communication, Security and Intelligent Systems (IC3SIS), Kochi, India, pp. 1–7 (2022).
[3] S. A. Schober, C. Carbonelli, and R. Wille, “An IoT-Based Anomaly Detection and Identification Approach for Gas Sensor Networks,” in Proc. 2023 IEEE Int. Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT), Brescia, Italy, pp. 415–420 (2023).
[4] S. F. Chevtchenko, E. D. S. Rocha, M. C. M. Dos Santos, R. L. Mota, D. M. Vieira, E. C. de Andrade, and D. R. B. de Araújo, “Anomaly Detection in Industrial Machinery Using IoT Devices and Machine Learning: A Systematic Mapping,” IEEE Access, vol. 11, pp. 128288–128305 (2023).
[5] M. A. Shoorehdeli and A. Jolfaei, “Detection of Anomalies in Industrial IoT Systems by Data Mining: Study of CHRIST Osmotron Water Purification System,” IEEE Internet of Things Journal, vol. 8, no. 13, pp. 10280–10287 (2021).
[6] G. Manivasagam and K. D. V. Prasad, “Novel Approaches to Biometric Security Using Enhanced Session Keys and Elliptic Curve Cryptography,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 27, no. 2-A, pp. 477–488 (2024).
[7] R. Senthil Kumar and K. D. V. Prasad, “Securing Digital Authentication with Cryptographic Innovations in Intrinsic Verification Systems,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 27, no. 2-A, pp. 317–328 (2024).
[8] T. Thiruvenkadam and K. D. V. Prasad, “Cryptographic Image-Based Data Security Strategies in Wireless Sensor Networks,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 27, no. 2-A, pp. 293–304 (2024).
[9] P. Sarma and M. Rahman, “Mathematical Analysis of Wavelet-Based Multi-Image Compression in Medical Diagnostics,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 27, no. 2-B, pp. 675–687 (2024).
[10] S. Kumar and A. K. Gupta, “A Novel Approach of Unsupervised Feature Selection Using Iterative Shrinking and Expansion Algorithm,” Journal of Interdisciplinary Mathematics, vol. 26, no. 3, pp. 519–530 (2023).
[11] Naziya and V. S. Chinamuttevi, “A Novel RF-SMOTE Model to Enhance the Definite Apprehensions for IoT Security Attacks,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 26, no. 3, pp. 861–873 (2023).
[12] R. Shekhar and A. Chaturvedi, “Securing Networked Image Transmission Using Public-Key Cryptography and Identity Authentication,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 26, no. 3, pp. 779–791 (2023).
[13] H. G. Govardhana Reddy and K. Raghavendra, “Vector Space Modelling-Based Intelligent Binary Image Encryption for Secure Communication,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 25, no. 4, pp. 1157–1171 (2022).
[14] N. K. Sahu and I. Mukherjee, “Machine Learning-Based Anomaly Detection for IoT Network: (Anomaly Detection in IoT Network),” in Proc. 2020 4th Int. Conf. Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, pp. 787–794 (2020).
[15] V.-D. Nguyen and W. Heyne, “A Comprehensive Study of Anomaly Detection Schemes in IoT Networks Using Machine Learning Algorithms,” Sensors, vol. 21, no. 24, p. 8320 (2021).
[16] S. Ganesan and R. Patan, “Effective Attack Detection in Internet of Medical Things Smart Environment Using a Deep Belief Neural Network,” IEEE Access, vol. 8, pp. 77396–77404 (2020).
[17] M. Mosbah, A. Zemmari, C. Sauvignac, and P. Faruki, “Network Intrusion Detection for IoT Security Based on Learning Techniques,” IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2671–2701 (2019).
[18] M. A. Khan and K. Salah, “IoT Security: Review Blockchain Solutions and Open Challenges,” Future Generation Computer Systems, pp. 395–411 (2018).
