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

Title:
A cost effective machine learning based network intrusion detection system using Raspberry Pi for real time analysis.
Authors:
Wijethilaka RWKS; Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka., Yapa K; Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka., Siriwardena D; Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka.
Source:
PloS one [PLoS One] 2025 Dec 29; Vol. 20 (12), pp. e0331123. Date of Electronic Publication: 2025 Dec 29 (Print Publication: 2025).
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
Imprint Name(s):
Original Publication: San Francisco, CA : Public Library of Science
MeSH Terms:
Machine Learning* , Computer Security* , Computer Communication Networks*/economics, Neural Networks, Computer ; Algorithms ; Bayes Theorem ; Cost-Benefit Analysis ; Humans
Entry Date(s):
Date Created: 20251229 Date Completed: 20251229 Latest Revision: 20251231
Update Code:
20251231
PubMed Central ID:
PMC12747338
DOI:
10.1371/journal.pone.0331123
PMID:
41460890
Database:
MEDLINE

Weitere Informationen

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 implementation utilizes a Raspberry Pi as the central processing unit for real time traffic analysis, supported by hardware components such as Ethernet cables, LEDs, and buzzers for continuous monitoring and immediate threat response. A comprehensive alert system is developed, sending email notifications to administrators and activating physical indicators to signify detected threats. Our proposed NIDS achieves 96.5 detection accuracy on the NF-UQ-NIDS dataset, with a significantly reduced false positive rate after applying SMOTE. The system processes real time network traffic with an average response time of 50 milliseconds, outperforming traditional IDS solutions in accuracy and efficiency. Evaluation using the NF-UQ-NIDS dataset demonstrates a significant improvement in detection accuracy and response time, establishing the system as an effective tool for safeguarding networks against emerging cyber threats.
(Copyright: © 2025 Wijethilaka et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The authors have declared that no competing interests exist.