261 research outputs found
Explainable and Safe Reinforcement Learning for Autonomous Air Mobility
Increasing traffic demands, higher levels of automation, and communication
enhancements provide novel design opportunities for future air traffic
controllers (ATCs). This article presents a novel deep reinforcement learning
(DRL) controller to aid conflict resolution for autonomous free flight.
Although DRL has achieved important advancements in this field, the existing
works pay little attention to the explainability and safety issues related to
DRL controllers, particularly the safety under adversarial attacks. To address
those two issues, we design a fully explainable DRL framework wherein we: 1)
decompose the coupled Q value learning model into a safety-awareness and
efficiency (reach the target) one; and 2) use information from surrounding
intruders as inputs, eliminating the needs of central controllers. In our
simulated experiments, we show that by decoupling the safety-awareness and
efficiency, we can exceed performance on free flight control tasks while
dramatically improving explainability on practical. In addition, the safety Q
learning module provides rich information about the safety situation of
environments. To study the safety under adversarial attacks, we additionally
propose an adversarial attack strategy that can impose both safety-oriented and
efficiency-oriented attacks. The adversarial aims to minimize safety/efficiency
by only attacking the agent at a few time steps. In the experiments, our attack
strategy increases as many collisions as the uniform attack (i.e., attacking at
every time step) by only attacking the agent four times less often, which
provide insights into the capabilities and restrictions of the DRL in future
ATC designs. The source code is publicly available at
https://github.com/WLeiiiii/Gym-ATC-Attack-Project
Adversarial jamming attacks and defense strategies via adaptive deep reinforcement learning
As the applications of deep reinforcement learning (DRL) in wireless
communications grow, sensitivity of DRL based wireless communication strategies
against adversarial attacks has started to draw increasing attention. In order
to address such sensitivity and alleviate the resulting security concerns, we
in this paper consider a victim user that performs DRL-based dynamic channel
access, and an attacker that executes DRLbased jamming attacks to disrupt the
victim. Hence, both the victim and attacker are DRL agents and can interact
with each other, retrain their models, and adapt to opponents' policies. In
this setting, we initially develop an adversarial jamming attack policy that
aims at minimizing the accuracy of victim's decision making on dynamic channel
access. Subsequently, we devise defense strategies against such an attacker,
and propose three defense strategies, namely diversified defense with
proportional-integral-derivative (PID) control, diversified defense with an
imitation attacker, and defense via orthogonal policies. We design these
strategies to maximize the attacked victim's accuracy and evaluate their
performances.Comment: 13 pages, 24 figure
A Survey on Reinforcement Learning Security with Application to Autonomous Driving
Reinforcement learning allows machines to learn from their own experience.
Nowadays, it is used in safety-critical applications, such as autonomous
driving, despite being vulnerable to attacks carefully crafted to either
prevent that the reinforcement learning algorithm learns an effective and
reliable policy, or to induce the trained agent to make a wrong decision. The
literature about the security of reinforcement learning is rapidly growing, and
some surveys have been proposed to shed light on this field. However, their
categorizations are insufficient for choosing an appropriate defense given the
kind of system at hand. In our survey, we do not only overcome this limitation
by considering a different perspective, but we also discuss the applicability
of state-of-the-art attacks and defenses when reinforcement learning algorithms
are used in the context of autonomous driving
Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities
Robotics and Artificial Intelligence (AI) have been inextricably intertwined
since their inception. Today, AI-Robotics systems have become an integral part
of our daily lives, from robotic vacuum cleaners to semi-autonomous cars. These
systems are built upon three fundamental architectural elements: perception,
navigation and planning, and control. However, while the integration of
AI-Robotics systems has enhanced the quality our lives, it has also presented a
serious problem - these systems are vulnerable to security attacks. The
physical components, algorithms, and data that make up AI-Robotics systems can
be exploited by malicious actors, potentially leading to dire consequences.
Motivated by the need to address the security concerns in AI-Robotics systems,
this paper presents a comprehensive survey and taxonomy across three
dimensions: attack surfaces, ethical and legal concerns, and Human-Robot
Interaction (HRI) security. Our goal is to provide users, developers and other
stakeholders with a holistic understanding of these areas to enhance the
overall AI-Robotics system security. We begin by surveying potential attack
surfaces and provide mitigating defensive strategies. We then delve into
ethical issues, such as dependency and psychological impact, as well as the
legal concerns regarding accountability for these systems. Besides, emerging
trends such as HRI are discussed, considering privacy, integrity, safety,
trustworthiness, and explainability concerns. Finally, we present our vision
for future research directions in this dynamic and promising field
Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses
The ongoing deployment of the fifth generation (5G) wireless networks
constantly reveals limitations concerning its original concept as a key driver
of Internet of Everything (IoE) applications. These 5G challenges are behind
worldwide efforts to enable future networks, such as sixth generation (6G)
networks, to efficiently support sophisticated applications ranging from
autonomous driving capabilities to the Metaverse. Edge learning is a new and
powerful approach to training models across distributed clients while
protecting the privacy of their data. This approach is expected to be embedded
within future network infrastructures, including 6G, to solve challenging
problems such as resource management and behavior prediction. This survey
article provides a holistic review of the most recent research focused on edge
learning vulnerabilities and defenses for 6G-enabled IoT. We summarize the
existing surveys on machine learning for 6G IoT security and machine
learning-associated threats in three different learning modes: centralized,
federated, and distributed. Then, we provide an overview of enabling emerging
technologies for 6G IoT intelligence. Moreover, we provide a holistic survey of
existing research on attacks against machine learning and classify threat
models into eight categories, including backdoor attacks, adversarial examples,
combined attacks, poisoning attacks, Sybil attacks, byzantine attacks,
inference attacks, and dropping attacks. In addition, we provide a
comprehensive and detailed taxonomy and a side-by-side comparison of the
state-of-the-art defense methods against edge learning vulnerabilities.
Finally, as new attacks and defense technologies are realized, new research and
future overall prospects for 6G-enabled IoT are discussed
- …