A Web Based IoT Security System Using Machine Learning-Based Anomaly Detection

Abstract

The rapid growth of Internet of Things (IoT) devices has increased exposure to cybersecurity threats due to weak authentication mechanisms, limited device resources, and lack of centralized monitoring. This paper presents a web-based IoT security system that integrates device management, threat visualization, and machine learning for anomaly detection. The system analyzes IoT device behavior and identification suspicious activities using Random Forest supervised learning algorithm. A dashboard Interface provides users with security scores, device status, and threat alerts. The proposed framework shows how intelligent monitoring and anomaly detection can improve visibility, threat detection and response capabilities in IoT security environment.

Introduction

This paper presents a web-based IoT cybersecurity monitoring system that uses machine learning to detect anomalies in IoT device behavior. Since IoT devices are often resource-constrained and insecure, traditional rule-based security methods are insufficient against modern cyber threats. To address this, the system simulates IoT device behavior instead of relying on real network traffic and applies ML-based analysis to identify suspicious activity.

The motivation for the study is the increasing security risks in rapidly growing IoT ecosystems and the need for a lightweight, easy-to-use system that can detect anomalies and visualize security insights. The main problem identified is that existing IoT security solutions are often complex, lack integration, and are not suitable for small-scale or educational use.

The proposed system contributes a centralized web-based platform that combines device management, anomaly detection, and visualization dashboards. It uses supervised machine learning models trained on simulated IoT data to classify normal and abnormal behavior, improving security awareness and offering potential scalability to real-world systems.

The methodology follows a pipeline consisting of data simulation, preprocessing, ML-based classification, anomaly detection, and visualization. Results are displayed through dashboards showing device status, threat frequency, and behavioral patterns.

The system architecture is modular and includes layers for user interaction, data processing, machine learning detection, monitoring and visualization, and storage. Data flows through preprocessing, feature extraction, classification, anomaly detection, and reporting modules.

The implementation uses Python (Flask), Scikit-learn, and web technologies like HTML, CSS, and JavaScript, with visualization tools such as Chart.js. Communication between modules is handled via REST APIs.

Conclusion

This paper presented an IOT Based Smart Threat Detection and Monitoring System designed to provide real-time anomaly detection and continuous surveillance of IOT-enabled environments. Through its workflow of data collection analysis-alert generation response, the system ensures improved visibility into network activities and devices behaviour. Expanded evaluations demonstrated the system’s ability to detect suspicious activities efficiently, reduces response time and provide response time, and provide detailed reporting when compared with traditional manual monitoring approaches. With its modular architecture, real-time monitoring capabilities, automated alert system, and scalable design represents a significant step toward next generations smart security frameworks. Future enhancements will focus on AI-driven detection, automated remediation, cloud-native deployment, and secure large-scale IOT integration.

References

[1] S. Rekha, L. Thirupathi, S. Renikunta, and R. Gangula, “Study of Security Issues and Solutions in Internet of Things (IoT),” Materials Today: Proceedings, vol.80, pp. 3559,2023. [2] S.A. Althar, M. Usama, F. Ali, and S. Iqbal, “Machine Learning Techniques for Detecting Cyberattacks in IoT Systems,” International Journal of Advanced Computer Science and Applications, vol.13, no.4, pp.45-52,2022. [3] A.A. Laghari, H. Li, A.A. Khan, Y. Shoulin, S. Karim, and M.A.K. Khani, “Internet of Things (IoT) Applications: Security Trends and Challenges,” Discover Internet of Things, vol.4, pp.1-20,2024. [4] L. Tageldin, Internet of Things Security: Threats, Recent Trends, and Mitigation Approaches,” Advanced in Internet of Things, vol.15, no.1, pp.1- 15-2025 [5] L. Sommerville, Software Engineering ,9th ed. Pearson Education,2011, ISBN: 978-0-13-703515- 9. [6] R. Mall, Software Project Management, 3rd ed. Pearson Education, 2014.

Copyright

Copyright © 2026 K Mehak Anjum, Basveraj Malipatil, Indu Prakash K , Atharva , Mr. Reddy Santosh Kumar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.