Detecting amalous behaviour in smart devices using machine learning

Project Code :TCMAPY1996

Objective

This project leverages machine learning algorithms to monitor and analyze data generated by smart devices, identifying irregular or malicious activities that deviate from normal patterns. The system processes input features like network traffic, usage frequency, and access patterns to detect anomalies effectively. Techniques such as Random Forest, Isolation Forest, and Neural Networks are explored to ensure high precision and recall. The project enhances IoT security by providing early detection, alert mechanisms, and adaptive learning to protect connected systems from unauthorized access, malware, and potential cyber threats in real-time operations.

Abstract

The rapid expansion of the Internet of Things (IoT) and the increasing dependence on smart devices have introduced significant challenges in maintaining data security, network stability, and system reliability. With millions of interconnected devices exchanging sensitive information, the detection of anomalous behaviour has become crucial in preventing cyber intrusions, unauthorized access, and data breaches. This project, titled “Detecting Anomalous Behaviour in Smart Devices using Machine Learning,” proposes an intelligent, data-driven framework designed to identify irregular patterns in network communication and classify potential threats with high precision.The proposed model leverages advanced machine learning algorithms, including Random Forest, XGBoost, and Support Vector Machine (SVM), to analyze network traffic data and categorize device behaviour as either normal or anomalous. Among these algorithms, the Random Forest Classifier demonstrated the most robust and consistent performance, achieving an exceptional accuracy of 99.35%. Its ensemble-based architecture enhances predictive stability, minimizes overfitting, and efficiently handles complex multidimensional data patterns common in IoT environments. The system is deployed through a Flask-based web application, offering users an interactive interface to upload datasets, select algorithms, and perform real-time anomaly detection. In addition, the application provides interpretable output and intelligent insights, aiding administrators in proactive threat management. Overall, this project presents a scalable, accurate, and user-friendly detection framework that strengthens IoT network security. The results reaffirm that Random Forest is a highly effective and reliable model for identifying anomalous behaviour in smart devices, promoting safer, more resilient, and adaptive IoT ecosystems.

Keywords: Internet of Things (IoT), Smart Devices , Data Security , Network Stability, System Reliability, Anomalous Behaviour, Machine Learning, Cyber Intrusions, Random Forest, XGBoost, Support Vector Machine (SVM), Network Traffic , Anomaly Detection,· Flask Web Application , Threat Management, IoT Security

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.