Implementation and Assessment of Jamming Effectiveness Against an FMCW Tracking Radar Based on a Novel Criterion

Zakerhaghighi, Mohammad Reza; Mivehchy, Mohsen; Sabahi, Mohamad Farzan

doi:10.1109/taes.2020.3000001

Cited by 8 publications

(4 citation statements)

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“…This limitation may be caused by energy or hardware constraints, or attack strategy adopted. For instance, an attacker may use a laser for compromising all the vision sensors at different time, but at each time, only one single vision sensor may be affected due to a small beam length of the laser [6]; a radio generator capable of sending radio signals with different frequencies can be used to compromise multiple radars at different time, but only one radar can be affected at each time [32].…”

Section: Assumptionmentioning

confidence: 99%

“…However, note that it is still possible that η(t) = 0 for some t ≥ 0 but inequality (30) still holds, which results in a failure of detection. To increase the detection rate of our algorithm, we perform attack detection over every time window of length T ∈ N. That is, for each t ∈ [iT, (i + 1)T ), i ∈ N, we check if (32) is satisfied for all t in the time window. If there exists t 1 ∈ [iT, (i + 1)T ), i ∈ N such that (32) holds, we say that sensors are under attack in the i-th time window.…”

Section: A Detection Strategymentioning

confidence: 99%

“…We assume that the set of attacked sensors are not a priori known to us and can be time-varying. For instance, the time-variance of the set of attacked sensors may be caused by the attack strategy adopted by the attacker with energy-saving considerations [32]. For every subset of sensors, we compute the largest difference between the average value of all the sensor measurements and the measurement of every single sensor in the subset.…”

Section: Introductionmentioning

confidence: 99%

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A Secure Sensor Fusion Framework for Connected and Automated Vehicles under Sensor Attacks

Yang

2021

Preprint

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By using various sensors to measure the surroundings and sharing local sensor information with the surrounding vehicles through wireless networks, connected and automated vehicles (CAVs) are expected to increase safety, efficiency, and capacity of our transportation systems. However, the increasing usage of sensors has also increased the vulnerability of CAVs to sensor faults and adversarial attacks. Anomalous sensor values resulting from malicious cyberattacks or faulty sensors may cause severe consequences or even fatalities. In this paper, we increase the resilience of CAVs to faults and attacks by using multiple sensors for measuring the same physical variable to create redundancy. We exploit this redundancy and propose a sensor fusion algorithm for providing a robust estimate of the correct sensor information with bounded errors independent of the attack signals, and for attack detection and isolation. The proposed sensor fusion framework is applicable to a large class of security-critical Cyber-Physical Systems (CPSs). To minimize the performance degradation resulting from the usage of estimation for control, we provide an H∞ controller for CACCequipped CAVs capable of stabilizing the closed-loop dynamics of each vehicle in the platoon while reducing the joint effect of estimation errors and communication channel noise on the tracking performance and string behavior of the vehicle platoon. Numerical examples are presented to illustrate the effectiveness of our methods.

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Section: Assumptionmentioning

confidence: 99%

Section: A Detection Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Secure Sensor Fusion Framework for Connected and Automated Vehicles under Sensor Attacks

Yang

2021

Preprint

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“…Although the de‐spreading process, performed in both acquisition and tracking stages, improves the SNR, the GNSS receivers are still susceptible to jamming attacks. Jamming and spoofing attacks are used as a modern threat and novel battle domains [3, 4]. For example, they are used to cause some problems in the positioning of the platforms or to forced‐landing deception of aerial platforms.…”

Section: Introductionmentioning

confidence: 99%