A Novel Approach for Detection of Cyber Attacks in MQTT-Based IIoT Systems Using Machine Learning Techniques
The Internet of Things (IoT) and the Industrial Internet of Things (IIoT) have grown significantly in the last decade, underlining the increasing need for effective, secure, and reliable data communication protocols. The widely accepted Message Queuing Telemetry Transport (MQTT) protocol, with its s...
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| Format: | Article |
| Language: | English |
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Çanakkale Onsekiz Mart University
2024-12-01
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| Series: | Journal of Advanced Research in Natural and Applied Sciences |
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| Online Access: | https://dergipark.org.tr/en/download/article-file/4256401 |
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| author | Serkan Gönen |
| author_facet | Serkan Gönen |
| author_sort | Serkan Gönen |
| collection | DOAJ |
| description | The Internet of Things (IoT) and the Industrial Internet of Things (IIoT) have grown significantly in the last decade, underlining the increasing need for effective, secure, and reliable data communication protocols. The widely accepted Message Queuing Telemetry Transport (MQTT) protocol, with its structure that meets the needs of welding-oriented devices in IoT and IIoT applications, is a prime example. However, its user-friendly simplicity also makes it susceptible to threats such as Dispersed Services Rejection (DDOS), Brete-Force, and incorrectly shaped package attacks. This article introduces a robust and reliable framework for preventing and defending against such attacks in MQTT-based IIoT systems based on the theory of merging attacks. The expert system incorporates the Adaboost model and can detect anomalies by processing network traffic in a closed setting and identifying impending threats. With its robust design, the system was subjected to various attack scenarios during testing, and it consistently detected interventions with an average accuracy of 92.7%, demonstrating its potential for use in intervention detection systems. The research findings not only contribute to the theoretical and practical concerns about the effective protection of IIoT systems but also offer hope for the future of cybersecurity in these systems. |
| format | Article |
| id | doaj-art-324f36b7c0a64c79bbed63c4548477bd |
| institution | Kabale University |
| issn | 2757-5195 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Çanakkale Onsekiz Mart University |
| record_format | Article |
| series | Journal of Advanced Research in Natural and Applied Sciences |
| spelling | doaj-art-324f36b7c0a64c79bbed63c4548477bd2025-02-05T18:13:03ZengÇanakkale Onsekiz Mart UniversityJournal of Advanced Research in Natural and Applied Sciences2757-51952024-12-0110489991210.28979/jarnas.1559652453A Novel Approach for Detection of Cyber Attacks in MQTT-Based IIoT Systems Using Machine Learning TechniquesSerkan Gönen0https://orcid.org/0000-0002-1417-4461İSTANBUL GELİŞİM ÜNİVERSİTESİThe Internet of Things (IoT) and the Industrial Internet of Things (IIoT) have grown significantly in the last decade, underlining the increasing need for effective, secure, and reliable data communication protocols. The widely accepted Message Queuing Telemetry Transport (MQTT) protocol, with its structure that meets the needs of welding-oriented devices in IoT and IIoT applications, is a prime example. However, its user-friendly simplicity also makes it susceptible to threats such as Dispersed Services Rejection (DDOS), Brete-Force, and incorrectly shaped package attacks. This article introduces a robust and reliable framework for preventing and defending against such attacks in MQTT-based IIoT systems based on the theory of merging attacks. The expert system incorporates the Adaboost model and can detect anomalies by processing network traffic in a closed setting and identifying impending threats. With its robust design, the system was subjected to various attack scenarios during testing, and it consistently detected interventions with an average accuracy of 92.7%, demonstrating its potential for use in intervention detection systems. The research findings not only contribute to the theoretical and practical concerns about the effective protection of IIoT systems but also offer hope for the future of cybersecurity in these systems.https://dergipark.org.tr/en/download/article-file/4256401iotiiotmqttcyber securitymachine learning |
| spellingShingle | Serkan Gönen A Novel Approach for Detection of Cyber Attacks in MQTT-Based IIoT Systems Using Machine Learning Techniques Journal of Advanced Research in Natural and Applied Sciences iot iiot mqtt cyber security machine learning |
| title | A Novel Approach for Detection of Cyber Attacks in MQTT-Based IIoT Systems Using Machine Learning Techniques |
| title_full | A Novel Approach for Detection of Cyber Attacks in MQTT-Based IIoT Systems Using Machine Learning Techniques |
| title_fullStr | A Novel Approach for Detection of Cyber Attacks in MQTT-Based IIoT Systems Using Machine Learning Techniques |
| title_full_unstemmed | A Novel Approach for Detection of Cyber Attacks in MQTT-Based IIoT Systems Using Machine Learning Techniques |
| title_short | A Novel Approach for Detection of Cyber Attacks in MQTT-Based IIoT Systems Using Machine Learning Techniques |
| title_sort | novel approach for detection of cyber attacks in mqtt based iiot systems using machine learning techniques |
| topic | iot iiot mqtt cyber security machine learning |
| url | https://dergipark.org.tr/en/download/article-file/4256401 |
| work_keys_str_mv | AT serkangonen anovelapproachfordetectionofcyberattacksinmqttbasediiotsystemsusingmachinelearningtechniques AT serkangonen novelapproachfordetectionofcyberattacksinmqttbasediiotsystemsusingmachinelearningtechniques |