Detection and mitigation of actuator attacks on small unmanned aircraft systems

Muniraj, Devaprakash; Farhood, Mazen

doi:10.1016/j.conengprac.2018.10.022

Cited by 27 publications

(14 citation statements)

References 34 publications

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“…As the system used only a few behavior indicators, the system is weak and fails to withstand cyberattacks on UAVs. In [39], Muniraj et al proposed a mitigation mechanism for UAVs against cyber attacks such as actuatorbased attacks. In this work, only actuator-based attacks are handled to protect the UAVs from hijacking.…”

Section: Literature Reviewmentioning

confidence: 99%

Detecting Signal Spoofing Attack in UAVs Using Machine Learning Models

2021

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Due to the tremendous advancement in interactive multimedia systems and technologies, security has become a major aspect. Advanced technology can be utilized for hacking autonomous systems like Unmanned Aerial vehicles (UAVs) in different ways such as spoofing and jamming. It can be spoofed by the injection of fake signals into the sensors. For the protection of the UAVs from the Global Positioning System (GPS) signal spoofing attack, we propose a new methodology by incorporating a machine learning (ML) algorithm such as Support Vector Machine (SVM). A detailed analysis of several learning algorithms is also carried out to choose the suitable learning algorithm for the proposed work. Once the suitable ML algorithm is selected, we perform K-fold analyses to develop other learning models by choosing different values of K-folds thus we called them K-learning models. The purpose of developing K-learning models is to apply voting techniques to the developed K-learning models. Moreover, the signal features used in the proposed work are jitter, jitter (absolute), jitter (local), jitter (RAP), jitter (ppq5), shimmer, shimmer (local), shimmer (dB), shimmer (apq3), shimmer (apq5) and frequency modulation. Based on these features of the signal, we train our proposed model for the detection of counterfeit GPS signals. To gauge the performance of the proposed model, we perform different experimentation analyses such as accuracy, precision, recall, and F1-score. The results and analysis show the effectiveness of the proposed work over existing techniques.

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Section: Literature Reviewmentioning

confidence: 99%

Detecting Signal Spoofing Attack in UAVs Using Machine Learning Models

2021

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“…Moreover, Blue et al detected (audible) command injections by identifying a frequency band which is produced by electronic speakers, but is absent in human speech [153]. In fact, detecting unique features of the sensed property is a common defense mechanism for general sensor manipulation attacks, such as those against Smart Grid power plants [156], [157], or unmanned aircraft systems [148]. However, such approaches require a theoretical system model, and assume an adversary who cannot inject data obeying this model.…”

Section: B Other Defensive Approachesmentioning

confidence: 99%

Taxonomy and Challenges of Out-of-Band Signal Injection Attacks and Defenses

Giechaskiel

Rasmussen

2020

IEEE Commun. Surv. Tutorials

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Recent research has shown that the integrity of sensor measurements can be violated through out-of-band signal injection attacks. These attacks target the conversion process from a physical quantity to an analog property-a process that fundamentally cannot be authenticated. Out-of-band signal injection attacks thus pose previously-unexplored security risks by exploiting hardware imperfections in the sensors themselves, or in their interfaces to microcontrollers. In response to the growingyet-disjointed literature in the subject, this article presents the first survey of out-of-band signal injection attacks. It focuses on unifying their terminology and identifying commonalities in their causes and effects through a chronological, evolutionary, and thematic taxonomy of attacks. By highlighting cross-influences between different types of out-of-band signal injections, this paper underscores the need for a common language irrespective of the attack method. By placing attack and defense mechanisms in the wider context of their dual counterparts of side-channel leakage and electromagnetic interference, this study identifies common threads and gaps that can help guide and inform future research. Overall, the ever-increasing reliance on sensors embedded in everyday commodity devices necessitates that a stronger focus be placed on improving the security of such systems against out-of-band signal injection attacks.

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“…Kune et al [78] adopt this principle and design a defense where some investigative actuation is cast on cardiac tissues, and cardiac signal readings are compared to their expected values. Similarly, Muniraj et al [191] propose an "active detection" method where judiciously designed excitation signals are superimposed on control commands, thus leading to a set of acceptable behavior for the system. Then the sensor will probe the environment, and the aforementioned effect is expected to be reflected in the sensor measurements if the system functions normally.…”

Section: A Physical-domain Defensementioning

confidence: 99%

Security and Privacy in the Emerging Cyber-Physical World: A Survey

Kaplan

Yan

et al. 2021

IEEE Commun. Surv. Tutorials

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With the emergence of low-cost smart and connected IoT devices, the area of cyber-physical security is becoming increasingly important. Past research has demonstrated new threat vectors targeting the transition process between the cyber and physical domains, where the attacker exploits the sensing system as an attack surface for signal injection or extraction of private information. Recently, there have been attempts to characterize an abstracted model for signal injection, but they primarily focus on the path of signal processing. This paper aims to systematize the existing research on security and privacy problems arising from the interaction of cyber world and physical world, with the context of broad CPS applications. The primary goals of the systematization are to (1) reveal the attack patterns and extract a general attack model of existing work, (2) understand possible new attacks, and (3) motivate development of defenses against the emerging cyber-physical threats.

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Detection and mitigation of actuator attacks on small unmanned aircraft systems

Cited by 27 publications

References 34 publications

Detecting Signal Spoofing Attack in UAVs Using Machine Learning Models

Detecting Signal Spoofing Attack in UAVs Using Machine Learning Models

Taxonomy and Challenges of Out-of-Band Signal Injection Attacks and Defenses

Security and Privacy in the Emerging Cyber-Physical World: A Survey

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