Powered by OpenAIRE graph
Found an issue? Give us feedback
addClaim

This Research product is the result of merged Research products in OpenAIRE.

You have already added 0 works in your ORCID record related to the merged Research product.

The vulnerability of UAVs: an adversarial machine learning perspective

Authors: Michael Doyle; Joshua D. Harguess; Keith Manville; Mikel Rodriguez;

The vulnerability of UAVs: an adversarial machine learning perspective

Abstract

The study of the vulnerabilities of unmanned aerial vehicles (UAVs) to a wide range of counter-UAV (C-UAV) attacks is well-known and established. While most C-UAV lies in the cyber, sensing, and kinetic domains, there is an emerging threat to these platforms from the perspective of adversarial machine learning (ML). Modern ML approaches are vulnerable to attacks that are largely imperceptible to humans and can be extremely successful in causing undesired false positives and false negatives in real-world scenarios. With the proliferation of ML algorithms throughout the software stack of modern UAVs, these new attacks could have real implications in the security of UAVs. A successful attack on a UAV has real-world consequences such as a collision or takeover of the platform itself. We describe a methodology for understanding the vulnerability of UAVs to these attacks by threat modeling each potential state and mode of the UAV, from powering-on, to various mission modes. In this threat modeling, we consider well-known attacks on deep learning approaches, such as state-of-the-art object detection, but also explore the possibility of novel attacks on traditional computer vision approaches, such as stereo algorithms. We examine one potential threat vector and evaluate the likelihood of success of such an attack given the current progress of adversarial ML.

Related Organizations
Subjects by Vocabulary

Microsoft Academic Graph classification: Computer science business.industry Deep learning False positives and false negatives Vulnerability Adversarial machine learning Computer security computer.software_genre Drone Object detection Adversarial system Threat model Artificial intelligence business computer

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    3
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    3
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
    Powered byBIP!BIP!
Powered by OpenAIRE graph
Found an issue? Give us feedback
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Top 10%
Average
Average