1,637 research outputs found
Attack Resilience and Recovery using Physical Challenge Response Authentication for Active Sensors Under Integrity Attacks
Embedded sensing systems are pervasively used in life- and security-critical
systems such as those found in airplanes, automobiles, and healthcare.
Traditional security mechanisms for these sensors focus on data encryption and
other post-processing techniques, but the sensors themselves often remain
vulnerable to attacks in the physical/analog domain. If an adversary
manipulates a physical/analog signal prior to digitization, no amount of
digital security mechanisms after the fact can help. Fortunately, nature
imposes fundamental constraints on how these analog signals can behave. This
work presents PyCRA, a physical challenge-response authentication scheme
designed to protect active sensing systems against physical attacks occurring
in the analog domain. PyCRA provides security for active sensors by continually
challenging the surrounding environment via random but deliberate physical
probes. By analyzing the responses to these probes, and by using the fact that
the adversary cannot change the underlying laws of physics, we provide an
authentication mechanism that not only detects malicious attacks but provides
resilience against them. We demonstrate the effectiveness of PyCRA through
several case studies using two sensing systems: (1) magnetic sensors like those
found wheel speed sensors in robotics and automotive, and (2) commercial RFID
tags used in many security-critical applications. Finally, we outline methods
and theoretical proofs for further enhancing the resilience of PyCRA to active
attacks by means of a confusion phase---a period of low signal to noise ratio
that makes it more difficult for an attacker to correctly identify and respond
to PyCRA's physical challenges. In doing so, we evaluate both the robustness
and the limitations of PyCRA, concluding by outlining practical considerations
as well as further applications for the proposed authentication mechanism.Comment: Shorter version appeared in ACM ACM Conference on Computer and
Communications (CCS) 201
Generation of Time-Varying Impedance Attacks Against Haptic Shared Control Steering Systems
The safety-critical nature of vehicle steering is one of the main motivations
for exploring the space of possible cyber-physical attacks against the steering
systems of modern vehicles. This paper investigates the adversarial
capabilities for destabilizing the interaction dynamics between human drivers
and vehicle haptic shared control (HSC) steering systems. In contrast to the
conventional robotics literature, where the main objective is to render the
human-automation interaction dynamics stable by ensuring passivity, this paper
takes the exact opposite route. In particular, to investigate the damaging
capabilities of a successful cyber-physical attack, this paper demonstrates
that an attacker who targets the HSC steering system can destabilize the
interaction dynamics between the human driver and the vehicle HSC steering
system through synthesis of time-varying impedance profiles. Specifically, it
is shown that the adversary can utilize a properly designed non-passive and
time-varying adversarial impedance target dynamics, which are fed with a linear
combination of the human driver and the steering column torques. Using these
target dynamics, it is possible for the adversary to generate in real-time a
reference angular command for the driver input device and the directional
control steering assembly of the vehicle. Furthermore, it is shown that the
adversary can make the steering wheel and the vehicle steering column angular
positions to follow the reference command generated by the time-varying
impedance target dynamics using proper adaptive control strategies. Numerical
simulations demonstrate the effectiveness of such time-varying impedance
attacks, which result in a non-passive and inherently unstable interaction
between the driver and the HSC steering system.Comment: 8 pages, 13 figures, accepted in The 2023 IEEE/RSJ International
Conference on Intelligent Robots and Systems (IROS 2023), Detroit, MI, Oct.
202
Performance analysis with network-enhanced complexities: On fading measurements, event-triggered mechanisms, and cyber attacks
Copyright © 2014 Derui Ding et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Nowadays, the real-world systems are usually subject to various complexities such as parameter uncertainties, time-delays, and nonlinear disturbances. For networked systems, especially large-scale systems such as multiagent systems and systems over sensor networks, the complexities are inevitably enhanced in terms of their degrees or intensities because of the usage of the communication networks. Therefore, it would be interesting to (1) examine how this kind of network-enhanced complexities affects the control or filtering performance; and (2) develop some suitable approaches for controller/filter design problems. In this paper, we aim to survey some recent advances on the performance analysis and synthesis with three sorts of fashionable network-enhanced complexities, namely, fading measurements, event-triggered mechanisms, and attack behaviors of adversaries. First, these three kinds of complexities are introduced in detail according to their engineering backgrounds, dynamical characteristic, and modelling techniques. Then, the developments of the performance analysis and synthesis issues for various networked systems are systematically reviewed. Furthermore, some challenges are illustrated by using a thorough literature review and some possible future research directions are highlighted.This work was supported in part by the National Natural Science Foundation of China under Grants 61134009, 61329301, 61203139, 61374127, and 61374010, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
Resilient Consensus Control Design for DC Microgrids against False Data Injection Attacks Using a Distributed Bank of Sliding Mode Observers
This paper investigates the problem of false data injection attack (FDIA) detection in microgrids. The grid under study is a DC microgrid with distributed boost converters, where the false data are injected into the voltage data so as to investigate the effect of attacks. The proposed algorithm uses a bank of sliding mode observers that estimates the states of the neighbor agents. Each agent estimates the neighboring states and, according to the estimation and communication data, the detection mechanism reveals the presence of FDIA. The proposed control scheme provides resiliency to the system by replacing the conventional consensus rule with attack-resilient ones. In order to evaluate the efficiency of the proposed method, a real-time simulation with eight agents has been performed. Moreover, a verification experimental test with three boost converters has been utilized to confirm the simulation results. It is shown that the proposed algorithm is able to detect FDI attacks and it protects the consensus deviation against FDI attacks
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