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Design and Analysis for Fall Detection System Simplification

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

R W K S Wijethilaka¹, Kanishka Yapa¹, Deemantha Siriwardena¹

¹Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka.

|December 29, 2025

Summary

This summary is machine-generated.

This study developed a Network Intrusion Detection System (NIDS) using machine learning to detect cyber attacks. The NIDS achieves high accuracy and fast response times, enhancing network security.

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Area of Science:

Computer Science
Cybersecurity
Network Security

Background:

Increasingly interconnected world necessitates robust data security.
Traditional intrusion detection systems struggle with distributed network challenges.
Need for advanced methods to detect and classify cyber attacks in real time.

Purpose of the Study:

Develop an adaptive Network Intrusion Detection System (NIDS).
Analyze inbound and outbound network traffic for threat detection.
Improve accuracy and efficiency over traditional Intrusion Detection Systems (IDS).

Main Methods:

Utilized machine learning algorithms: Random Forest, LSTM, ANN, XGBoost, Naive Bayes.
Implemented NIDS on a Raspberry Pi for real-time traffic analysis.
Developed a comprehensive alert system with email notifications and physical indicators.

Main Results:

Achieved 96.5% detection accuracy on the NF-UQ-NIDS dataset.
Significantly reduced false positive rates using SMOTE.
Real-time traffic processing with an average response time of 50 milliseconds.

Conclusions:

The developed NIDS is an effective tool for safeguarding networks against cyber threats.
The system demonstrates superior accuracy and efficiency compared to traditional IDS.
Real-time analysis and adaptive machine learning enhance network security posture.