NIDS – Network Intrusion Detection System

Abstract

The rapid expansion of Internet of Things (IoT) devices has transformed modern industries and everyday life. However, it has also introduced significant security challenges due to limited computational resources and weak built-in security mechanisms. To address these issues, this paper presents the design and implementation of a lightweight Network Intrusion Detection System (NIDS) specifically tailored for IoT environments. The proposed system continuously monitors network traffic and device behavior to detect unauthorized access, anomalies, and malicious activities with high accuracy while maintaining low computational overhead. The system utilizes machine learning-based techniques for efficient intrusion detection and ensures minimal resource consumption, making it suitable for resource-constrained IoT devices. Experimental results demonstrate the effectiveness of the proposed model in identifying various types of network attacks, thereby enhancing the overall security of IoT networks.