Vulnerabilities of Control Systems in Internet of Things Applications

Radisavljević-Gajić, Verica; Park, Seri; Chasaki, Danai

doi:10.1109/jiot.2017.2787962

Cited by 13 publications

(2 citation statements)

References 60 publications

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“…where x k ∈ R n collects the variables (state) of the system, A k ∈ R n×n is the state matrix, and white noise w k ∼ N (0, W k ) captures model uncertainty. Such class of models is widely used in control applications, by virtue of their simplicity but also powerful expressiveness [28], [66]- [69]. For example, a standard approach in control of systems modeled through nonlinear differential equations is to approximate the original model as a parameter-or time-varying linear system, for which efficient control techniques are known [28], [70], [71].…”

Section: A System Modelmentioning

confidence: 99%

To Compute or Not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computing

Ballotta,

Peserico,

Zanini

et al. 2024

IEEE Trans. Netw. Sci. Eng.

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We consider a network of smart sensors for an edge computing application that sample a time-varying signal and send updates to a base station for remote global monitoring. Sensors are equipped with sensing and compute, and can either send raw data or process them on-board before transmission. Limited hardware resources at the edge generate a fundamental latency-accuracy trade-off : raw measurements are inaccurate but timely, whereas accurate processed updates are available after processing delay. Hence, one needs to decide when sensors should transmit raw measurements or rely on local processing to maximize network monitoring performance. To tackle this sensing design problem, we model an estimation-theoretic optimization framework that embeds both computation and communication latency, and propose a Reinforcement Learning-based approach that dynamically allocates computational resources at each sensor. Effectiveness of our proposed approach is validated through numerical experiments motivated by smart sensing for the Internet of Drones and self-driving vehicles. In particular, we show that, under constrained computation at the base station, monitoring performance can be further improved by an online sensor selection.

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Section: A System Modelmentioning

confidence: 99%

To Compute or Not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computing

Ballotta,

Peserico,

Zanini

et al. 2024

IEEE Trans. Netw. Sci. Eng.

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“…Here, the FDI attack on wireless communication links between the MGCC and DGs is the concern as we consider the existing centralized secondary frequency control in microgrids. From the system theoretic point of view, the vulnerability of the communication links between the MGCC and DGs to FDI attack could be assessed by using the controllability Gramian [19][20][21][22], while FDI attack is modelled as the extra control input. Risk analysis of microgrid control systems with cyber attacks is investigated by using the Markov state transition model [8].…”

mentioning

confidence: 99%

Game theoretic vulnerability management for secondary frequency control of islanded microgrids against false data injection attacks

Liu

Chen

2021

IET Cyber-Phy Sys Theory & Ap

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While most existing works ignore securing the communication of control signals in microgrids' centralized secondary frequency control, here, a stochastic game between the microgrid control centre (MGCC) and the attacker for enhancing the vulnerability of the MGCC to false data injection (FDI) attack (wireless spoof attack) is proposed. The vulnerability to wireless spoof attack is assessed based on the controllability Gramian when the FDI attack is modelled as the malicious control input that aims to drive the microgrid state to undesired values. In the formulated zero-sum two-player Markov game, the state is uniquely associated with the vulnerability index defined by the trace of the controllability Gramian with respect to the attack input. Moreover, the utility function of the stochastic game includes not only the costs related to conducting spoof attack and encryption actions but also the possible remedy costs associated with the resulted vulnerability levels. In turn, the potential impacts of the cyber-layer action choices on the performance of the physical power system are considered when designing the optimal attack and defence strategies. A distribution feeder system with four distributed generators (DGs) is used for simulation studies. The vulnerability assessment results show that the vulnerability level increases when the attacker compromises more on the communication links between the MGCC and DGs. In the simulated game, mixed stationary attack and defence strategies are predominate when considering the uncertainty of the other player.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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A Power Efficient Authentication Model for E-Healthcare System Security Concerns in Context to Internet of Things (IoT)

Parthiban¹

2019

Advances in Intelligent Systems and Computing

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Vulnerabilities of Control Systems in Internet of Things Applications

Cited by 13 publications

References 60 publications

To Compute or Not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computing

To Compute or Not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computing

Game theoretic vulnerability management for secondary frequency control of islanded microgrids against false data injection attacks

A Power Efficient Authentication Model for E-Healthcare System Security Concerns in Context to Internet of Things (IoT)

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