4,190 research outputs found
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
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
Information-Theoretic Attacks in the Smart Grid
Gaussian random attacks that jointly minimize the amount of information
obtained by the operator from the grid and the probability of attack detection
are presented. The construction of the attack is posed as an optimization
problem with a utility function that captures two effects: firstly, minimizing
the mutual information between the measurements and the state variables;
secondly, minimizing the probability of attack detection via the
Kullback-Leibler divergence between the distribution of the measurements with
an attack and the distribution of the measurements without an attack.
Additionally, a lower bound on the utility function achieved by the attacks
constructed with imperfect knowledge of the second order statistics of the
state variables is obtained. The performance of the attack construction using
the sample covariance matrix of the state variables is numerically evaluated.
The above results are tested in the IEEE 30-Bus test system.Comment: 2017 IEEE International Conference on Smart Grid Communications
(SmartGridComm
False Data Injection Attacks on Phasor Measurements That Bypass Low-rank Decomposition
This paper studies the vulnerability of phasor measurement units (PMUs) to
false data injection (FDI) attacks. Prior work demonstrated that unobservable
FDI attacks that can bypass traditional bad data detectors based on measurement
residuals can be identified by detector based on low-rank decomposition (LD).
In this work, a class of more sophisticated FDI attacks that captures the
temporal correlation of PMU data is introduced. Such attacks are designed with
a convex optimization problem and can always bypass the LD detector. The
vulnerability of this attack model is illustrated on both the IEEE 24-bus RTS
and the IEEE 118-bus systems.Comment: 6 pages, 4 figures, submitted to 2017 IEEE International Conference
on Smart Grid Communications (SmartGridComm
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