Analysis on security-related concerns of unmanned aerial vehicle: attacks, limitations, and recommendations

Siddiqi, Murtaza Ahmed; Iwendi, Celestine; Jaroslava, Kniezova; Anumbe, Noble

doi:10.3934/mbe.2022121

Cited by 33 publications

(10 citation statements)

References 54 publications

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“…Concerns about security and privacy in relation to unmanned aerial vehicles were briefly analysed by Siddiqi M.A. et al [57]. As a means of addressing these issues, the report also provided solutions and recommendations.…”

Section: Related Workmentioning

confidence: 99%

Violence Detection Approach based on Cloud Data and Neutrosophic Cognitive Maps

et al. 2022

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Violence has remained a momentous problem since time immemorial. Various scientific studies are conducted in the recent past to identify the stimuli causing violent behavior among the masses and to achieve the target of cloud data protection. Given the inherent ambiguity or indeterminacy in human behaviour, this study in the area of violence detection appears to be effective, as it finds a variety of stimuli and character qualities that contribute to violent conduct among masses. This uncertainty of traits causing violence can easily be seen in surveillance data present over the cloud and also from the data collected using academic research. Therefore, for the purpose of identifying violent behavior we have considered the factors (data) from existing research and from data over clouds. The factors that lead to violent behavior and are identified by algorithms running over clouds are termed as determinate or certain factors. The factors that were not considered and least identified by the cloud algorithms and given less importance are termed indeterminate factors or uncertain factors. The indeterminate factors are also considered based on the expert’s opinion where the experts are not in a condition to provide a clear stance or when they are neutral in their opinion. Tests are performed using Neutrosophic Cognitive Maps (NCMs) to model the violent behavior taking into consideration both determinate and indeterminate factors. Earlier these tests were performed using Fuzzy Cognitive Maps (FCMs) where indeterminate or uncertain factors were not considered. Therefore, we provide a brief comparison between NCMs and FCMs and show how effective NCMs are when we need to consider the uncertainty of concepts while carrying out tests for identifying violent behavior. Later results are obtained by forming a Neutrosophic adjacency matrix which is evaluated using the concepts of linear algebra. The obtained results in the form of 1 ∗ n vector (1 I I I I 1 I 1 I I I I I I I I I I I ) clearly shows the presence of indeterminate factor ‘I’ in the vector which was absent in earlier models when designed using FCMs. This shows how these indeterminate or uncertain factors play a significant role in cultivating violent behavior which was not shown in the previous study. The study is significant since it takes into account factors from cloud data, experts’ opinions, and also from literature, and shows how these factors are taken into consideration at the data level itself so that they will not impact the modeling stage, and machine learning algorithms will perform well because uncertain and indeterminate information is taken care of at training phase itself. Hence uncertainty could be reduced in machine learning algorithms and in the overall recognition of violent behavior.

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Section: Related Workmentioning

confidence: 99%

Violence Detection Approach based on Cloud Data and Neutrosophic Cognitive Maps

et al. 2022

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“…In earlier sections, we provided a thorough review of a variety of threat modelling methods such as STRIDE, PASTA, and LINDDUN, which are used to model security and privacy in AS [95], [137], [138]. However, these techniques only emphasize some specified security but not data privacy threats.…”

Section: Limitation Of the Existing Data Privacy Threat Modelling Tec...mentioning

confidence: 99%

Data Privacy Threat Modelling for Autonomous Systems: A Survey From the GDPR's Perspective

Azam

Michala

Ansari

et al. 2023

IEEE Trans. Big Data

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Intelligence-based applications have been increasingly deployed in every field of life including smart homes, smart cities, healthcare services, and autonomous systems where personal data is collected across heterogeneous sources and processed using "black-box" algorithms in opaque centralised servers. As a consequence, preserving the data privacy and security of these applications is of utmost importance. In this respect, a modelling technique for identifying potential data privacy threats and specifying countermeasures to mitigate the related vulnerabilities in such AI-based systems plays a significant role in preserving and securing personal data. Various threat modelling techniques have been proposed such as STRIDE, LINDDUN, and PASTA but none of them is sufficient to model the data privacy threats in autonomous systems. Furthermore, they are not designed to model compliance with data protection legislation like the EU/UK General Data Protection Regulation (GDPR), which is fundamental to protecting data owners' privacy as well as to preventing personal data from potential privacy-related attacks. In this article, we survey the existing threat modelling techniques for data privacy threats in autonomous systems and then analyse such techniques from the viewpoint of GDPR compliance. Following the analysis, We employ STRIDE and LINDDUN in autonomous cars, a specific use-case of autonomous systems, to scrutinise the challenges and gaps of the existing techniques when modelling data privacy threats. Prospective research directions for refining data privacy threats & GDPR-compliance modelling techniques for autonomous systems are also presented.

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“…The defensive ECM helps to shield against offensive ECM. Some popular defensive ECM are as follows: The RF link between the UAV and the remote controller is vulnerable to jamming [ 36 ] and hacking [ 37 ]. The jamming can be avoided by spread-spectrum techniques and high frequency-hopping rates.…”

Section: Challenges and Threats From Malicious Uavsmentioning

confidence: 99%

“…The RF link between the UAV and the remote controller is vulnerable to jamming [ 36 ] and hacking [ 37 ]. The jamming can be avoided by spread-spectrum techniques and high frequency-hopping rates.…”

Section: Challenges and Threats From Malicious Uavsmentioning

confidence: 99%

Threats from and Countermeasures for Unmanned Aerial and Underwater Vehicles

Khawaja

Semkin

Ratyal

et al. 2022

Sensors

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The use of unmanned aerial vehicles (UAVs) for different applications has increased tremendously during the past decade. The small size, high maneuverability, ability to fly at predetermined coordinates, simple construction, and affordable price have made UAVs a popular choice for diverse aerial applications. However, the small size and the ability to fly close to the terrain make the detection and tracking of UAVs challenging. Similarly, unmanned underwater vehicles (UUVs) have revolutionized underwater operations. UUVs can accomplish numerous tasks that were not possible with manned underwater vehicles. In this survey paper, we provide features and capabilities expected from current and future UAVs and UUVs, and review potential challenges and threats due to use of such UAVs/UUVs. We also overview the countermeasures against such threats, including approaches for the detection, tracking, and classification of UAVs and UUVs.

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Analysis on security-related concerns of unmanned aerial vehicle: attacks, limitations, and recommendations

Cited by 33 publications

References 54 publications

Violence Detection Approach based on Cloud Data and Neutrosophic Cognitive Maps

Violence Detection Approach based on Cloud Data and Neutrosophic Cognitive Maps

Data Privacy Threat Modelling for Autonomous Systems: A Survey From the GDPR's Perspective

Threats from and Countermeasures for Unmanned Aerial and Underwater Vehicles

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