A Novel Approach for Detection of Cyber Attacks in MQTT-Based IIoT Systems Using Machine Learning Techniques DOI Creative Commons
Serkan Gönen

Journal of Advanced Research in Natural and Applied Sciences, Год журнала: 2024, Номер 10(4), С. 899 - 912

Опубликована: Дек. 31, 2024

The Internet of Things (IoT) and the Industrial (IIoT) have grown significantly in last decade, underlining increasing need for effective, secure, reliable data communication protocols. widely accepted Message Queuing Telemetry Transport (MQTT) protocol, with its structure that meets needs welding-oriented devices IoT IIoT applications, is a prime example. However, user-friendly simplicity also makes it susceptible to threats such as Dispersed Services Rejection (DDOS), Brete-Force, incorrectly shaped package attacks. This article introduces robust framework preventing defending against attacks MQTT-based systems based on theory merging expert system incorporates Adaboost model can detect anomalies by processing network traffic closed setting identifying impending threats. With design, was subjected various attack scenarios during testing, consistently detected interventions an average accuracy 92.7%, demonstrating potential use intervention detection systems. research findings not only contribute theoretical practical concerns about effective protection but offer hope future cybersecurity these

Язык: Английский

Cybersecurity Solutions for Industrial Internet of Things–Edge Computing Integration: Challenges, Threats, and Future Directions DOI Creative Commons
Tamara Zhukabayeva, Lazzat Zholshiyeva, Nurdaulet Karabayev

и другие.

Sensors, Год журнала: 2025, Номер 25(1), С. 213 - 213

Опубликована: Янв. 2, 2025

This paper provides the complete details of current challenges and solutions in cybersecurity cyber-physical systems (CPS) within context IIoT its integration with edge computing (IIoT–edge computing). We systematically collected analyzed relevant literature from past five years, applying a rigorous methodology to identify key sources. Our study highlights prevalent layer attacks, common intrusion methods, critical threats facing IIoT–edge environments. Additionally, we examine various types cyberattacks targeting CPS, outlining their significant impact on industrial operations. A detailed taxonomy primary security mechanisms for CPS is developed, followed by comparative analysis our approach against existing research. The findings underscore widespread vulnerabilities across architecture, particularly relation DoS, ransomware, malware, MITM attacks. review emphasizes advanced technologies, including machine learning (ML), federated (FL), blockchain, blockchain–ML, deep (DL), encryption, cryptography, IT/OT convergence, digital twins, as essential enhancing real-time data protection computing. Finally, outlines potential future research directions aimed at advancing this rapidly evolving domain.

Язык: Английский

Процитировано

1

A Novel Approach for Detection of Cyber Attacks in MQTT-Based IIoT Systems Using Machine Learning Techniques DOI Creative Commons
Serkan Gönen

Journal of Advanced Research in Natural and Applied Sciences, Год журнала: 2024, Номер 10(4), С. 899 - 912

Опубликована: Дек. 31, 2024

The Internet of Things (IoT) and the Industrial (IIoT) have grown significantly in last decade, underlining increasing need for effective, secure, reliable data communication protocols. widely accepted Message Queuing Telemetry Transport (MQTT) protocol, with its structure that meets needs welding-oriented devices IoT IIoT applications, is a prime example. However, user-friendly simplicity also makes it susceptible to threats such as Dispersed Services Rejection (DDOS), Brete-Force, incorrectly shaped package attacks. This article introduces robust framework preventing defending against attacks MQTT-based systems based on theory merging expert system incorporates Adaboost model can detect anomalies by processing network traffic closed setting identifying impending threats. With design, was subjected various attack scenarios during testing, consistently detected interventions an average accuracy 92.7%, demonstrating potential use intervention detection systems. research findings not only contribute theoretical practical concerns about effective protection but offer hope future cybersecurity these

Язык: Английский

Процитировано

0