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@article{199299,
        author = {Apurva Iraskar and Dr.Sudhir Mohod},
        title = {Design and Implementation of an Intelligent IoT Security Framework for Real-Time Cyberattack Detection Using ML and Web Technologies},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {13921-13935},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199299},
        abstract = {Industry4.0isfundamentallybased on networked systems. Real-time communication between machines, sensors, devices, and people makes it easier to transmit the data needed to make decisions. Informed decision-making is empowered by the comprehensive insights and analytics made possible by this connectedness in conjunction with information transparency.  Healthcare IoT, a fast-growing field, could revolutionize patient monitoring and intervention. This interconnection raises new security concerns, requiring real-time anomaly detection to protect patient data and device integrity. This study presents a novel security framework that uses combination of Hidden Markov Models (HMM) and Support Vector Machine (SVM) to detect anomalies in real-time healthcare IoT environments with high accuracy. The framework prioritizes real-time strength and efficiency. Sensor data from wearables, medical devices, and other IoT devices is carefully segmented into time intervals. Features are carefully derived from each segment, including statistical summaries, patterns, and frequency domain characteristics. The system integrates Random Forest, XGBoost, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) models within an optimized ensemble architecture to improve detection accuracy and robustness. A structured preprocessing pipeline extracts key features from network traffic, enabling real-time classification of attacks such as DDoS, botnets, malware, intrusion attempts, and port scanning. An intelligent risk assessment mechanism categorizes detected threats into CRITICAL, HIGH, MEDIUM, and LOW levels based on confidence scores and severity, supporting effective response prioritization. Unlike traditional threshold-based approaches, the proposed architecture is capable of autonomously distinguishing between benign operational anomalies and adversarial cyber-attacks, thereby reducing false alarms and improving detection accuracy. The framework adopts a multi-layer design that combines data acquisition, feature learning, anomaly detection, and response.Furthermore, a scalable web-based dashboard provides real-time monitoring, attack visualization, and system performance insights through interactive data streaming. The proposed framework delivers accurate, scalable, and efficient IoT cybersecurity protection suitable for dynamic network environments.},
        keywords = {IoT Cybersecurity, Machine Learning, Real-Time Cyberattack Detection, Intrusion Detection System, Anomaly Detection, Network Traffic Analysis, Web-Based Application, Deep Learning, DDoS Detection, Ensemble Learning.},
        month = {April},
        }