A cost effective machine learning based network intrusion detection system using Raspberry Pi for real time analysis.

ABSTRACT: In an increasingly interconnected world, the security of sensitive data and critical operations is paramount. This study presents the development of a Network Intrusion Detection System (NIDS) that analyzes both inbound and outbound network traffic to detect and classify various cyber attacks. The research begins with an extensive review of existing intrusion detection techniques, highlighting the limitations of traditional methods when addressing the unique security challenges posed by distributed networks. To overcome these limitations, advanced machine learning algorithms, including Random Forest, Long Short Term Memory (LSTM) networks, Artificial Neural Networks (ANN), XGBoost, and Naive Bayes, are employed to create a robust and adaptive intrusion detection system. The practical imple

SUBMITTER: Wijethilaka RWKS 

PROVIDER: S-EPMC12747338 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature