60,766 research outputs found
Catching Cheats: Detecting Strategic Manipulation in Distributed Optimisation of Electric Vehicle Aggregators
Given the rapid rise of electric vehicles (EVs) worldwide, and the ambitious
targets set for the near future, the management of large EV fleets must be seen
as a priority. Specifically, we study a scenario where EV charging is managed
through self-interested EV aggregators who compete in the day-ahead market in
order to purchase the electricity needed to meet their clients' requirements.
With the aim of reducing electricity costs and lowering the impact on
electricity markets, a centralised bidding coordination framework has been
proposed in the literature employing a coordinator. In order to improve privacy
and limit the need for the coordinator, we propose a reformulation of the
coordination framework as a decentralised algorithm, employing the Alternating
Direction Method of Multipliers (ADMM). However, given the self-interested
nature of the aggregators, they can deviate from the algorithm in order to
reduce their energy costs. Hence, we study the strategic manipulation of the
ADMM algorithm and, in doing so, describe and analyse different possible attack
vectors and propose a mathematical framework to quantify and detect
manipulation. Importantly, this detection framework is not limited the
considered EV scenario and can be applied to general ADMM algorithms. Finally,
we test the proposed decentralised coordination and manipulation detection
algorithms in realistic scenarios using real market and driver data from Spain.
Our empirical results show that the decentralised algorithm's convergence to
the optimal solution can be effectively disrupted by manipulative attacks
achieving convergence to a different non-optimal solution which benefits the
attacker. With respect to the detection algorithm, results indicate that it
achieves very high accuracies and significantly outperforms a naive benchmark
Byzantine Attack and Defense in Cognitive Radio Networks: A Survey
The Byzantine attack in cooperative spectrum sensing (CSS), also known as the
spectrum sensing data falsification (SSDF) attack in the literature, is one of
the key adversaries to the success of cognitive radio networks (CRNs). In the
past couple of years, the research on the Byzantine attack and defense
strategies has gained worldwide increasing attention. In this paper, we provide
a comprehensive survey and tutorial on the recent advances in the Byzantine
attack and defense for CSS in CRNs. Specifically, we first briefly present the
preliminaries of CSS for general readers, including signal detection
techniques, hypothesis testing, and data fusion. Second, we analyze the spear
and shield relation between Byzantine attack and defense from three aspects:
the vulnerability of CSS to attack, the obstacles in CSS to defense, and the
games between attack and defense. Then, we propose a taxonomy of the existing
Byzantine attack behaviors and elaborate on the corresponding attack
parameters, which determine where, who, how, and when to launch attacks. Next,
from the perspectives of homogeneous or heterogeneous scenarios, we classify
the existing defense algorithms, and provide an in-depth tutorial on the
state-of-the-art Byzantine defense schemes, commonly known as robust or secure
CSS in the literature. Furthermore, we highlight the unsolved research
challenges and depict the future research directions.Comment: Accepted by IEEE Communications Surveys and Tutoiral
ATTACK2VEC: Leveraging Temporal Word Embeddings to Understand the Evolution of Cyberattacks
Despite the fact that cyberattacks are constantly growing in complexity, the
research community still lacks effective tools to easily monitor and understand
them. In particular, there is a need for techniques that are able to not only
track how prominently certain malicious actions, such as the exploitation of
specific vulnerabilities, are exploited in the wild, but also (and more
importantly) how these malicious actions factor in as attack steps in more
complex cyberattacks. In this paper we present ATTACK2VEC, a system that uses
temporal word embeddings to model how attack steps are exploited in the wild,
and track how they evolve. We test ATTACK2VEC on a dataset of billions of
security events collected from the customers of a commercial Intrusion
Prevention System over a period of two years, and show that our approach is
effective in monitoring the emergence of new attack strategies in the wild and
in flagging which attack steps are often used together by attackers (e.g.,
vulnerabilities that are frequently exploited together). ATTACK2VEC provides a
useful tool for researchers and practitioners to better understand cyberattacks
and their evolution, and use this knowledge to improve situational awareness
and develop proactive defenses
Attack-Resilient Supervisory Control of Discrete-Event Systems
In this work, we study the problem of supervisory control of discrete-event
systems (DES) in the presence of attacks that tamper with inputs and outputs of
the plant. We consider a very general system setup as we focus on both
deterministic and nondeterministic plants that we model as finite state
transducers (FSTs); this also covers the conventional approach to modeling DES
as deterministic finite automata. Furthermore, we cover a wide class of attacks
that can nondeterministically add, remove, or rewrite a sensing and/or
actuation word to any word from predefined regular languages, and show how such
attacks can be modeled by nondeterministic FSTs; we also present how the use of
FSTs facilitates modeling realistic (and very complex) attacks, as well as
provides the foundation for design of attack-resilient supervisory controllers.
Specifically, we first consider the supervisory control problem for
deterministic plants with attacks (i) only on their sensors, (ii) only on their
actuators, and (iii) both on their sensors and actuators. For each case, we
develop new conditions for controllability in the presence of attacks, as well
as synthesizing algorithms to obtain FST-based description of such
attack-resilient supervisors. A derived resilient controller provides a set of
all safe control words that can keep the plant work desirably even in the
presence of corrupted observation and/or if the control words are subjected to
actuation attacks. Then, we extend the controllability theorems and the
supervisor synthesizing algorithms to nondeterministic plants that satisfy a
nonblocking condition. Finally, we illustrate applicability of our methodology
on several examples and numerical case-studies
A computer scientist looks at game theory
I consider issues in distributed computation that should be of relevance to
game theory. In particular, I focus on (a) representing knowledge and
uncertainty, (b) dealing with failures, and (c) specification of mechanisms.Comment: To appear, Games and Economic Behavior. JEL classification numbers:
D80, D8
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