Real-Time Safety Assessment of Unmanned Aircraft Systems Against Stealthy Cyber Attacks

Kwon, Cheolhyeon; Yantek, Scott; Hwang, Inseok

doi:10.2514/1.i010388

Cited by 31 publications

(18 citation statements)

References 15 publications

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“…Here, T is the length of the detector's time window, and c i for i = 1, ..., T are predefined non-negative coefficients, with c T being strictly positive. The above formulation captures both fixed window size detectors, where T is a constant, as well as detectors where the time window size T satisfies T = k. Also, the definition of the detection function g k covers a wide variety of commonly used intrusion detectors, such as χ 2 and sequential probability ratio test (SPRT) detectors previously considered in these scenarios [20], [18], [23], [19], [11], [12]. The alarm is triggered when the value of the detection function g k satisfies that g k > threshold, (7) and the probability of the alarm at time k can be captured as…”

Section: A System Model Without Attacksmentioning

confidence: 99%

“…One line of work is based on the use of unknown input observers (e.g., [34], [27]) and non-convex optimization for resilient estimation (e.g., [4], [25]), while another focuses on attackdetection and estimation guarantees in systems with standard Kalman filter-based state estimators (e.g., [22], [21], [11], [12], [24], [23], [10]). In the later works, estimation residuebased failure detectors, such as χ 2 [22], [23] and sequential probability ratio test (SPRT) detectors [12], are employed for intrusion detection. Still, irrelevant of the utilized attack detection mechanism, after compromising a suitable subset of sensors, an intelligent attacker can significantly degrade control performance while remaining undetected (i.e., stealthy).…”

Section: Introductionmentioning

confidence: 99%

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Relaxing Integrity Requirements for Attack-Resilient Cyber-Physical Systems

Jovanov

Pajić

2019

IEEE Trans. Automat. Contr.

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The increase in network connectivity has also resulted in several high-profile attacks on cyber-physical systems. An attacker that manages to access a local network could remotely affect control performance by tampering with sensor measurements delivered to the controller. Recent results have shown that with network-based attacks, such as Man-in-the-Middle attacks, the attacker can introduce an unbounded state estimation error if measurements from a suitable subset of sensors contain false data when delivered to the controller. While these attacks can be addressed with the standard cryptographic tools that ensure data integrity, their continuous use would introduce significant communication and computation overhead. Consequently, we study effects of intermittent data integrity guarantees on system performance under stealthy attacks. We consider linear estimators equipped with a general type of residual-based intrusion detectors (including χ 2 and SPRT detectors), and show that even when integrity of sensor measurements is enforced only intermittently, the attack impact is significantly limited; specifically, the state estimation error is bounded or the attacker cannot remain stealthy. Furthermore, we present methods to:(1) evaluate the effects of any given integrity enforcement policy in terms of reachable state-estimation errors for any type of stealthy attacks, and (2) design an enforcement policy that provides the desired estimation error guarantees under attack. Finally, on three automotive case studies we show that even with less than 10% of authenticated messages we can ensure satisfiable control performance in the presence of attacks.

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Section: A System Model Without Attacksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Relaxing Integrity Requirements for Attack-Resilient Cyber-Physical Systems

Jovanov

Pajić

2019

IEEE Trans. Automat. Contr.

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“…In feedback-control based CPS, the residue ( 6) is widely used for anomaly detection -χ 2 detector in (Y. Mo and B. Sinopoli, 2010;Mo & Sinopoli, 2015), cumulative sum in (Tunga et al, 2018), sequential probability ratio test (SPRT) detector in (Kwon et al, 2016), and a general window-type detector in (Jovanov & Pajic, 2019). For instance, with χ 2 -based detectors the detection function g t is a weighted norm of z t (with the χ 2 distribution) -i.e.,…”

Section: System Modelmentioning

confidence: 99%

Learning-Based Vulnerability Analysis of Cyber-Physical Systems

Khazraei¹,

Hallyburton²,

Gao³

et al. 2021

Preprint

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This work focuses on the use of deep learning for vulnerability analysis of cyber-physical systems (CPS). Specifically, we consider a control architecture widely used in CPS (e.g., robotics), where the low-level control is based on e.g., the extended Kalman filter (EKF) and an anomaly detector. To facilitate analyzing the impact potential sensing attacks could have, our objective is to develop learning-enabled attack generators capable of designing stealthy attacks that maximally degrade system operation. We show how such problem can be cast within a learning-based grey-box framework where parts of the runtime information are known to the attacker, and introduce two models based on feed-forward neural networks (FNN); both models are trained offline, using a cost function that combines the attack effects on the estimation error and the residual signal used for anomaly detection, so that the trained models are capable of recursively generating such effective sensor attacks in real-time. The effectiveness of the proposed methods is illustrated on several case studies.

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“…Several procedures and techniques that analyze the residual for attack detection exist, one of which is the Sequential Probability Ratio Testing (SPRT) [8] that tests the sequence of incoming residuals one at a time by taking the loglikelihood function (LLF). The Cumulative Sum (CUSUM) procedure proposed in [9] and [10] leverages the known characteristics of the residual covariance and sequentially sums the residual error to find changes in mean of the distribution.…”

Section: A Related Workmentioning

confidence: 99%

Model-based Randomness Monitor for Stealthy Sensor Attacks

Bonczek¹,

Gao²,

Bezzo³

2020

Preprint

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Malicious attacks on modern autonomous cyberphysical systems (CPSs) can leverage information about the system dynamics and noise characteristics to hide while hijacking the system toward undesired states. Given attacks attempting to hide within the system noise profile to remain undetected, an attacker with the intent to hijack a system will alter sensor measurements, contradicting with what is expected by the system's model. To deal with this problem, in this paper we present a framework to detect non-randomness in sensor measurements on CPSs under the effect of sensor attacks. Specifically, we propose a run-time monitor that leverages two statistical tests, the Wilcoxon Signed-Rank test and Serial Independence Runs test to detect inconsistent patterns in the measurement data. For the proposed statistical tests we provide formal guarantees and bounds for attack detection. We validate our approach through simulations and experiments on an unmanned ground vehicle (UGV) under stealthy attacks and compare our framework with other anomaly detectors.

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Real-Time Safety Assessment of Unmanned Aircraft Systems Against Stealthy Cyber Attacks

Cited by 31 publications

References 15 publications

Relaxing Integrity Requirements for Attack-Resilient Cyber-Physical Systems

Relaxing Integrity Requirements for Attack-Resilient Cyber-Physical Systems

Learning-Based Vulnerability Analysis of Cyber-Physical Systems

Model-based Randomness Monitor for Stealthy Sensor Attacks

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