Security of Cyber-Physical Systems

Rubio‐Hernan, Jose; Rodolfo-Mejias, Juan; García-Alfaro, Joaquín

doi:10.1007/978-3-319-61437-3_1

Cited by 7 publications

(7 citation statements)

References 15 publications

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“…LegoSCADA [77], [78] is a cost-effective hybrid testbed developed at the Universite Paris-Saclay in France. The testbed's conceptual architecture is based on three block elements: the controller, the system, and the sensors.…”

Section: F Hybridmentioning

confidence: 99%

A Survey on Industrial Control System Testbeds and Datasets for Security Research

Conti,

Donadel,

Turrin

2021

Preprint

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The increasing digitization and interconnection of legacy Industrial Control Systems (ICSs) open new vulnerability surfaces, exposing such systems to malicious attackers. Furthermore, since ICSs are often employed in critical infrastructures (e.g., nuclear plants) and manufacturing companies (e.g., chemical industries), attacks can lead to devastating physical damages. In dealing with this security requirement, the research community focuses on developing new security mechanisms such as Intrusion Detection Systems (IDSs), facilitated by leveraging modern machine learning techniques. However, these algorithms require a testing platform and a considerable amount of data to be trained and tested accurately. To satisfy this prerequisite, Academia, Industry, and Government are increasingly proposing testbed (i.e., scaled-down versions of ICSs or simulations) to test the performances of the IDSs. Furthermore, to enable researchers to cross-validate security systems (e.g., security-bydesign concepts or anomaly detectors), several datasets have been collected from testbeds and shared with the community.In this paper, we provide a deep and comprehensive overview of ICSs, presenting the architecture design, the employed devices, and the security protocols implemented. We then collect, compare, and describe testbeds and datasets in the literature, highlighting key challenges and design guidelines to keep in mind in the design phases. Furthermore, we enrich our work by reporting the best performing IDS algorithms tested on every dataset to create a baseline in state of the art for this field. Finally, driven by knowledge accumulated during this survey's development, we report advice and good practices on the development, the choice, and the utilization of testbeds, datasets, and IDSs.

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Section: F Hybridmentioning

confidence: 99%

A Survey on Industrial Control System Testbeds and Datasets for Security Research

Conti,

Donadel,

Turrin

2021

Preprint

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“…It is possible to verify in the literature [7,8,12,13,15,16,21,22,26,[29][30][31][32][33][34][35][36] that in cyber-physical systems (which includes NCSs), a relevant portion of the attack surface often lies in the communication process between sensors/actuators and controllers. For this reason, in the ICS cybersecurity landscape, significant attention has been given to the study of cyber-attacks to sensor/actuator systems [7].…”

Section: Physicalmentioning

confidence: 99%

“…of an individual j of the BSA. In this case, the fitness f j of each individual j of the BSA is obtained comparingΥj(k) with Υ(k), according to (30):…”

Section: End If End For Endmentioning

confidence: 99%

“…begin if y (k) is in steady state regarding r(k) then Record w(k) and y (k) during T seconds; Computeȳ according to (5); Compute y 1 (k) according to (7); NII stage: Obtain a set of y j (k) signals from y 1 (k) and w(k) using Algorithm 2; Compute Υ(k) according to equation 22; Computeω j (0) according to equation (25); end Execute BSA, usingω j (0)δ(k) and Υ(k) to find M e (z) based on (10), (29) and (30). end if end…”

Section: Algorithm 3: Attack Identification With the Nii Techniquementioning

confidence: 99%

“…They are processed through the NII stage to result inω j (0)δ(k) and Υ(k). The signalsω j (0)δ(k) and Υ(k) used by the BSA in (29) and (30), respectively, are sized with N = 100 samples. This way, the signals processed by the BSA have the same size in both Algorithms 1 and 3 (i.e., N = N).…”

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confidence: 99%

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Identification of Data Injection Attacks in Networked Control Systems Using Noise Impulse Integration

Sá

Casimiro

Machado

et al. 2020

Sensors

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The benefits of using Networked Control Systems (NCS) in the growing Industry 4.0 arenumerous, including better management and operational capabilities, as well as costs reduction.However, despite these benefits, the use of NCSs can also expose physical plants to new threatsoriginated in the cyber domain—such as data injection attacks in NCS links through which sensorsand controllers transmit signals. In this sense, this work proposes a link monitoring strategy toidentify linear time-invariant (LTI) functions executed during controlled data injection attacksby a Man-in-the-Middle hosted in an NCS link. The countermeasure is based on a bioinspiredmetaheuristic, called Backtracking Search Optimization Algorithm (BSA), and uses white Gaussiannoise to excite the attack function. To increase the accuracy of this countermeasure, it is proposedthe Noise Impulse Integration (NII) technique, which is developed using the radar pulse integrationtechnique as inspiration. The results demonstrate that the proposed countermeasure is able toaccurately identify LTI attack functions, here executed to impair measurements transmitted bythe plant sensor, without interfering with the NCS behavior when the system is in its normaloperation. Moreover, the results indicate that the NII technique can increase the accuracy of the attackidentification.

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