Lightweight Intrusion Detection System for IoT Networks

Registration ID: IJNRD_306272

Published ID: IJNRD2505042

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Harshitha CS, Harish Kumar S, Harsha HR, Harshavardhan HV

Keywords: IoT, Intrusion Detection, Machine Learning, Deep Learning, CICIDS 2017. Support Vector Machine (SVM) and Convolutional Long Short-Term Memory (CLSTM)

The rapid expansion of the Internet of Things (IoT) has revolutionized industries and everyday life by connecting a wide range of devices. However, this exponential growth introduces significant security risks, as IoT devices are often vulnerable to various types of cyber-attacks. Effective intrusion detection systems (IDS) are crucial for safeguarding IoT networks against malicious activities, ensuring data privacy, and maintaining operational integrity. This project focuses on developing an advanced IDS leveraging both machine learning (ML) and deep learning (DL) techniques to detect and classify network traffic as benign or malicious.This project aims to develop an effective Intrusion Detection System (IDS) by combining machine learning and deep learning techniques. Using the CICIDS 2017 dataset, which contains both benign and malicious network traffic, the system will train models like Support Vector Machine (SVM) and Convolutional Long Short-Term Memory (CLSTM) to detect and classify intrusions. These models will analyze network traffic distinguishing between normal and suspicious activity. The project will evaluate model performance using metrics such as accuracy, precision, recall, and F1-score, and compare the effectiveness of SVM and CLSTM in reducing false positives. A user-friendly interface will display detection results. This solution aims to address the shortcomings of existing IDS, which struggle with high false-positive rates and adapting to the dynamic nature of IoT environments. The project will contribute to enhancing security by offering a more accurate, scalable, and efficient intrusion detection system for IoT networks.

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Harshitha CS, Harish Kumar S, Harsha HR, & Harshavardhan HV (May-2025). Lightweight Intrusion Detection System for IoT Networks. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 10(5), a499-a506. https://ijnrd.org/papers/IJNRD2505042.pdf

Volume 10 Issue 5, May-2025

Pages : a499-a506

Paper Reg. ID: IJNRD_306272

Published Paper Id: IJNRD2505042

Research Area: Science and Technology

Author Type: Indian Author

Country: Channarayapatna, Karnataka, India

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