Online-Semisupervised Neural Anomaly Detector to Identify MQTT-Based Attacks in Real Time

Industry 4.0 focuses on continuous interconnection services, allowing for the continuous and uninterrupted exchange of signals or information between related parties. The application of messaging protocols for transferring data to remote locations must meet specific specifications such as asynchronous communication, compact messaging, operating in conditions of unstable connection of the transmission line of data, limited network bandwidth operation, support multilevel Quality of Service (QoS), and easy integration of new devices. The Message Queue Telemetry Transport (MQTT) protocol is used in software applications that require asynchronous communication. It is a light and simplified protocol based on publish-subscribe messaging and is placed functionally over the TCP/IP protocol. It is designed to minimize the required communication bandwidth and system requirements increasing reliability and probability of successful message transmission, making it ideal for use in Machine-to-Machine (M2M) communication or networks where bandwidth is limited, delays are long, coverage is not reliable, and energy consumption should be as low as possible. Despite the fact that the advantage that MQTT offers its way of operating does not provide a serious level of security in how to achieve its interconnection, as it does not require protocol dependence on one intermediate third entity, the interface is dependent on each application. This paper presents an innovative real-time anomaly detection system to detect MQTT-based attacks in cyber-physical systems. This is an online-semisupervised learning neural system based on a small number of sampled patterns that identify crowd anomalies in the MQTT protocol related to specialized attacks to undermine cyber-physical systems.

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Real-Time Health-related Data, Wearable Medical Sensor Devices, and Smart Cyber-Physical Systems

American Journal of Medical Research ◽

10.22381/ajmr6220193 ◽

2019 ◽

Vol 6 (2) ◽

pp. 25

Keyword(s):

Real Time ◽

Cyber Physical Systems ◽

Physical Systems ◽

Related Data ◽

Sensor Devices ◽

Health Related ◽

Wearable Medical

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Assurance monitoring of learning-enabled cyber-physical systems using inductive conformal prediction based on distance learning

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s089006042100010x ◽

2021 ◽

Vol 35 (2) ◽

pp. 251-264

Author(s):

Dimitrios Boursinos ◽

Xenofon Koutsoukos

Keyword(s):

Neural Networks ◽

Distance Learning ◽

Real Time ◽

Speaker Recognition ◽

Deep Neural Networks ◽

Cyber Physical Systems ◽

Error Rates ◽

Traffic Sign ◽

Conformal Prediction ◽

Physical Systems

AbstractMachine learning components such as deep neural networks are used extensively in cyber-physical systems (CPS). However, such components may introduce new types of hazards that can have disastrous consequences and need to be addressed for engineering trustworthy systems. Although deep neural networks offer advanced capabilities, they must be complemented by engineering methods and practices that allow effective integration in CPS. In this paper, we proposed an approach for assurance monitoring of learning-enabled CPS based on the conformal prediction framework. In order to allow real-time assurance monitoring, the approach employs distance learning to transform high-dimensional inputs into lower size embedding representations. By leveraging conformal prediction, the approach provides well-calibrated confidence and ensures a bounded small error rate while limiting the number of inputs for which an accurate prediction cannot be made. We demonstrate the approach using three datasets of mobile robot following a wall, speaker recognition, and traffic sign recognition. The experimental results demonstrate that the error rates are well-calibrated while the number of alarms is very small. Furthermore, the method is computationally efficient and allows real-time assurance monitoring of CPS.

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Real-Time Attack-Recovery for Cyber-Physical Systems Using Linear Approximations

2020 IEEE Real-Time Systems Symposium (RTSS) ◽

10.1109/rtss49844.2020.00028 ◽

2020 ◽

Author(s):

Lin Zhang ◽

Xin Chen ◽

Fanxin Kong ◽

Alvaro A. Cardenas

Keyword(s):

Real Time ◽

Cyber Physical Systems ◽

Physical Systems ◽

Linear Approximations

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The Importance of Implementing Cyber Physical Systems to Acquire Real-Time Data and Indicators

J ◽

10.3390/j4020012 ◽

2021 ◽

Vol 4 (2) ◽

pp. 147-153

Author(s):

Paula Morella ◽

María Pilar Lambán ◽

Jesús Antonio Royo ◽

Juan Carlos Sánchez

Keyword(s):

Decision Making ◽

Data Acquisition ◽

Real Time ◽

Failure Detection ◽

Cyber Physical Systems ◽

Future Research ◽

Time Data ◽

Physical Systems ◽

The Real ◽

Real Time Data

Among the new trends in technology that have emerged through the Industry 4.0, Cyber Physical Systems (CPS) and Internet of Things (IoT) are crucial for the real-time data acquisition. This data acquisition, together with its transformation in valuable information, are indispensable for the development of real-time indicators. Moreover, real-time indicators provide companies with a competitive advantage over the competition since they enhance the calculus and speed up the decision-making and failure detection. Our research highlights the advantages of real-time data acquisition for supply chains, developing indicators that would be impossible to achieve with traditional systems, improving the accuracy of the existing ones and enhancing the real-time decision-making. Moreover, it brings out the importance of integrating technologies 4.0 in industry, in this case, CPS and IoT, and establishes the main points for a future research agenda of this topic.

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