Detection and Isolation of DoS and Integrity Cyber Attacks in Cyber-Physical Systems with a Neural Network-Based Architecture

Carlos M Paredes,Apolinar González-Potes,Vrani Ibarra-Junquera,Diego Martínez-Castro

doi:10.3390/electronics10182238

Carlos M Paredes, Apolinar González-Potes + Show 2 more

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https://doi.org/10.3390/electronics10182238

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New applications of industrial automation request great flexibility in the systems, supported by the increase in the interconnection between its components, allowing access to all the information of the system and its reconfiguration based on the changes that occur during its operations, with the purpose of reaching optimum points of operation. These aspects promote the Smart Factory paradigm, integrating physical and digital systems to create smarts products and processes capable of transforming conventional value chains, forming the Cyber-Physical Systems (CPSs). This flexibility opens a large gap that affects the security of control systems since the new communication links can be used by people to generate attacks that produce risk in these applications. This is a recent problem in the control systems, which originally were centralized and later were implemented as interconnected systems through isolated networks. To protect these systems, strategies that have presented acceptable results in other environments, such as office environments, have been chosen. However, the characteristics of these applications are not the same, and the results achieved are not as expected. This problem has motivated several efforts in order to contribute from different approaches to increase the security of control systems. Based on the above, this work proposes an architecture based on artificial neural networks for detection and isolation of cyber attacks Denial of Service (DoS) and integrity in CPS. Simulation results of two test benches, the Secure Water Treatment (SWaT) dataset, and a tanks system, show the effectiveness of the proposal. Regarding the SWaT dataset, the scores obtained from the recall and F1 score metrics was 0.95 and was higher than other reported works, while, in terms of precision and accuracy, it obtained a score of 0.95 which is close to other proposed methods. With respect to the interconnected tank system, scores of 0.96,0.83,0.81, and 0.83 were obtained for the accuracy, precision, F1 score, and recall metrics, respectively. The high true negatives rate in both cases is noteworthy. In general terms, the proposal has a high effectiveness in detecting and locating the proposed attacks.

Highlights

Cyber Physical Systems (CPSs) emerge from the attempts to unify the emerging applications of embedded computers and communication technologies used to monitor, control, as well as generate actions on physical elements to fulfill with a specific task [1], and they have an important impact on different sectors [2].The different parts of the system are usually interconnected using communication networks to share information and data that interact with each other and, sometimes, cloud computing services [3,4,5]
The results show that the algorithm has high accuracy and a low false positive and false negative rate (FPR and FNR)
We considered the type of attacks that were implemented, since our approach was to address different types of attacks, including those with the highest frequency and impact on the control systems found in the CPSs

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Introduction

Cyber Physical Systems (CPSs) emerge from the attempts to unify the emerging applications of embedded computers and communication technologies used to monitor, control, as well as generate actions on physical elements to fulfill with a specific task [1], and they have an important impact on different sectors [2].The different parts of the system are usually interconnected using communication networks to share information and data that interact with each other and, sometimes, cloud computing services [3,4,5]. Cyber Physical Systems (CPSs) emerge from the attempts to unify the emerging applications of embedded computers and communication technologies used to monitor, control, as well as generate actions on physical elements to fulfill with a specific task [1], and they have an important impact on different sectors [2]. The first is the physical layer, where the physical infrastructure of the system, sensors, and actuators are located, with the objective of monitoring and controlling physical processes. The second is the network layer, which implements the transmission data and allows the interaction between the physical layer and the cybernetic layer. A cybernetic layer allows the abstractions of the received data, as well as the interaction between networks, devices, and the physical infrastructure [6]

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