2 research outputs found
SoK - Security and Privacy in the Age of Drones: Threats, Challenges, Solution Mechanisms, and Scientific Gaps
The evolution of drone technology in the past nine years since the first
commercial drone was introduced at CES 2010 has caused many individuals and
businesses to adopt drones for various purposes. We are currently living in an
era in which drones are being used for pizza delivery, the shipment of goods,
and filming, and they are likely to provide an alternative for transportation
in the near future. However, drones also pose a significant challenge in terms
of security and privacy within society (for both individuals and
organizations), and many drone related incidents are reported on a daily basis.
These incidents have called attention to the need to detect and disable drones
used for malicious purposes and opened up a new area of research and
development for academia and industry, with a market that is expected to reach
$1.85 billion by 2024. While some of the knowledge used to detect UAVs has been
adopted for drone detection, new methods have been suggested by industry and
academia alike to deal with the challenges associated with detecting the very
small and fast flying objects. In this paper, we describe new societal threats
to security and privacy created by drones, and present academic and industrial
methods used to detect and disable drones. We review methods targeted at areas
that restrict drone flights and analyze their effectiveness with regard to
various factors (e.g., weather, birds, ambient light, etc.). We present the
challenges arising in areas that allow drone flights, introduce the methods
that exist for dealing with these challenges, and discuss the scientific gaps
that exist in this area. Finally, we review methods used to disable drones,
analyze their effectiveness, and present their expected results. Finally, we
suggest future research directions
Learning-based Intelligent Attack against Mobile Robots with Obstacle-avoidance
The security issue of mobile robots have attracted considerable attention in
recent years. Most existing works focus on detection and countermeasures for
some classic attacks from cyberspace. Nevertheless, those work are generally
based on some prior assumptions for the attacker (e.g., the system dynamics is
known, or internal access is compromised). A few work are delicated to physical
attacks, however, there still lacks certain intelligence and advanced control
design. In this paper, we propose a physical-based and intelligent attack
framework against the obstacle-avoidance of mobile robots. The novelty of our
work lies in the following: i) Without any prior information of the system
dynamics, the attacker can learn the detection area and goal position of a
mobile robot by trial and observation, and the obstacle-avoidance mechanism is
learned by support vector regression (SVR) method; ii) Considering different
attack requirements, different attack strategies are proposed to implement the
attack efficiently; iii) The framework is suitable for holonomic and
non-holonomic mobile robots, and the algorithm performance analysis about time
complexity and optimality is provided. Furthermore, the condition is obtained
to guarantee the success of the attack. Simulations illustrate the
effectiveness of the proposed framework