Physical Attack Detection for Smart Objects

Güven, Ebu Yusuf; Camurcu, Ali Yilmaz

doi:10.1109/idap.2018.8620791

Cited by 3 publications

(6 citation statements)

References 12 publications

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“…However, the attacker needs to have complete information of the system in all of these cases. On the other hand, as a kind of adversarial disturbance, the physical attacks may cause the system components to operate incorrectly by maliciously modifying system inputs, and thus lead to system instability [7] and [9]. In [7], the machine learning method is utilizing to detect physical attacks on Internet of Things applications.…”

Section: Introductionmentioning

confidence: 99%

Event-Based Pinning Synchronization Control for Discrete-Time Delayed Complex Cyber-Physical Networks Under All-Around Attacks

Chaoqun

Jia

2023

ITC

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This paper is concerned with the problem of pinning synchronization control for a class of nonlinear discrete-time delayed complex cyber-physical networks under all-around attacks. To handle the all-around attacks, a constrained hybrid attacks model is established, which incorporates the pattern feature of false data injection attacks and physical attacks. By utilizing the Lyapunov stability theory and the linear matrix inequality technique, a novel dynamic event-triggering pinning synchronization control scheme is developed to cope with the synchronization control task. Subsequently, sufficient conditions are obtained to guarantee that the closed-loop error dynamics are ultimately exponentially bounded. Furthermore, the design procedure of the synchronization controller is presented for the considered complex cyber-physical networks subject to all-around attacks. Finally, an illustrative example is delivered to demonstrate the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Event-Based Pinning Synchronization Control for Discrete-Time Delayed Complex Cyber-Physical Networks Under All-Around Attacks

Chaoqun

Jia

2023

ITC

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“…Thus, violations of information security during the operation of these devices can have serious consequences for the life and health of people [ 5 ]. Moreover, the more cyberphysical devices are located in the area of a person’s presence, the greater the danger (in case of a successful attack) they will pose [ 6 ]. Preventing such consequences requires both counteraction to such attacks and their investigation, followed by preventive protection against them.…”

Section: Introductionmentioning

confidence: 99%

Analytical Modeling for Identification of the Machine Code Architecture of Cyberphysical Devices in Smart Homes

Kotenko

Izrailov

Buinevich

2022

Sensors

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Ensuring the security of modern cyberphysical devices is the most important task of the modern world. The reason for this is that such devices can cause not only informational, but also physical damage. One of the approaches to solving the problem is the static analysis of the machine code of the firmware of such devices. The situation becomes more complicated in the case of a Smart Home, since its devices can have different processor architectures (means instruction sets). In the case of cyberphysical devices of the Smart Home, the destruction of machine code due to physical influences is also possible. Therefore, the first step is to correctly identify the processor architecture. In the interests of this, a machine code model is proposed that has a formal notation and takes into account the possibility of code destruction. The article describes the full cycle of research (including experiment) in order to obtain this model. The model is based on byte-frequency machine code signatures. The experiment resulted in obtaining template signatures for the Top-16 processor architectures: Alpha, X32, Amd64, Arm64, Hppa64, I486, I686, Ia64, Mips, Mips64, Ppc, Ppc64, RiscV64, S390, S390x and Sparc64.

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“…Unfortunately, the above results only focus on cyber-attacks, and few works are involved with physical attacks. As a kind of adversarial disturbance, physical attacks may cause the system components to operate incorrectly by maliciously modifying system inputs and thus lead to system instability [31,32]. In [31], the machine learning method is utilized to detect physical attacks on Internet of Tings applications.…”

Section: Introductionmentioning

confidence: 99%

“…As a kind of adversarial disturbance, physical attacks may cause the system components to operate incorrectly by maliciously modifying system inputs and thus lead to system instability [31,32]. In [31], the machine learning method is utilized to detect physical attacks on Internet of Tings applications. In response to the problem of multiple stochastic physical attacks, the robust secure controller is developed to ensure the stability of cyber-physical systems [32].…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, complex cyberphysical networks may be afected by the combined efects of cyber-attacks, physical attacks, and time delay in a control practice. It is worth noting that both FDI attacks and physical attacks may bring signifcant risks to some practical applications (e.g., electric power grids, the Internet of Tings, and connected vehicles [31,39]) due to their concealed characteristics. Furthermore, the time delay is another major constraint for the application of complex cyber-physical networks, which will cause system performance degradation or even instability.…”

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confidence: 99%

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Intermittent Control for Synchronization of Discrete-Delayed Complex Cyber-Physical Networks under Mixed Attacks

Zhu

Jia

Zhang

2022

Journal of Control Science and Engineering

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This paper is concerned with the synchronization control problem for discrete-delayed complex cyber-physical networks under mixed attacks. To handle input delays and mixed attacks, the intermittent control mechanism is employed, which is distinctly different from the traditional control method. By utilizing the Lyapunov stability theorem, a novel synchronization control method is developed for the synchronization control of complex cyber-physical networks with mixed attacks. Then, sufficient conditions are derived to guarantee that the synchronization error dynamics are ultimately bounded. Moreover, the conditions for a special case where the absence of input delays. Subsequently, certain optimization problems are formulated with the aim to minimize the synchronization error. Finally, two numerical examples are given to verify the effectiveness and superiority of the proposed synchronization control strategy.

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Physical Attack Detection for Smart Objects

Cited by 3 publications

References 12 publications

Event-Based Pinning Synchronization Control for Discrete-Time Delayed Complex Cyber-Physical Networks Under All-Around Attacks

Event-Based Pinning Synchronization Control for Discrete-Time Delayed Complex Cyber-Physical Networks Under All-Around Attacks

Analytical Modeling for Identification of the Machine Code Architecture of Cyberphysical Devices in Smart Homes

Intermittent Control for Synchronization of Discrete-Delayed Complex Cyber-Physical Networks under Mixed Attacks

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