2,573 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
Analyzing Attacks on Cooperative Adaptive Cruise Control (CACC)
Cooperative Adaptive Cruise Control (CACC) is one of the driving applications
of vehicular ad-hoc networks (VANETs) and promises to bring more efficient and
faster transportation through cooperative behavior between vehicles. In CACC,
vehicles exchange information, which is relied on to partially automate
driving; however, this reliance on cooperation requires resilience against
attacks and other forms of misbehavior. In this paper, we propose a rigorous
attacker model and an evaluation framework for this resilience by quantifying
the attack impact, providing the necessary tools to compare controller
resilience and attack effectiveness simultaneously. Although there are
significant differences between the resilience of the three analyzed
controllers, we show that each can be attacked effectively and easily through
either jamming or data injection. Our results suggest a combination of
misbehavior detection and resilient control algorithms with graceful
degradation are necessary ingredients for secure and safe platoons.Comment: 8 pages (author version), 5 Figures, Accepted at 2017 IEEE Vehicular
Networking Conference (VNC
Resilient Learning-Based Control for Synchronization of Passive Multi-Agent Systems under Attack
In this paper, we show synchronization for a group of output passive agents
that communicate with each other according to an underlying communication graph
to achieve a common goal. We propose a distributed event-triggered control
framework that will guarantee synchronization and considerably decrease the
required communication load on the band-limited network. We define a general
Byzantine attack on the event-triggered multi-agent network system and
characterize its negative effects on synchronization. The Byzantine agents are
capable of intelligently falsifying their data and manipulating the underlying
communication graph by altering their respective control feedback weights. We
introduce a decentralized detection framework and analyze its steady-state and
transient performances. We propose a way of identifying individual Byzantine
neighbors and a learning-based method of estimating the attack parameters.
Lastly, we propose learning-based control approaches to mitigate the negative
effects of the adversarial attack
Block Outlier Methods for Malicious User Detection in Cooperative Spectrum Sensing
Block outlier detection methods, based on Tietjen-Moore (TM) and Shapiro-Wilk
(SW) tests, are proposed to detect and suppress spectrum sensing data
falsification (SSDF) attacks by malicious users in cooperative spectrum
sensing. First, we consider basic and statistical SSDF attacks, where the
malicious users attack independently. Then we propose a new SSDF attack, which
involves cooperation among malicious users by masking. In practice, the number
of malicious users is unknown. Thus, it is necessary to estimate the number of
malicious users, which is found using clustering and largest gap method.
However, we show using Monte Carlo simulations that, these methods fail to
estimate the exact number of malicious users when they cooperate. To overcome
this, we propose a modified largest gap method.Comment: Accepted in Proceedings of 79th IEEE Vehicular Technology
Conference-Spring (VTC-Spring), May 2014, Seoul, South Kore
A Study on Techniques/Algorithms used for Detection and Prevention of Security Attacks in Cognitive Radio Networks
In this paper a detailed survey is carried out on the taxonomy of Security Issues, Advances on Security Threats and Countermeasures ,A Cross-Layer Attack, Security Status and Challenges for Cognitive Radio Networks, also a detailed survey on several Algorithms/Techniques used to detect and prevent SSDF(Spectrum Sensing Data Falsification) attack a type of DOS (Denial of Service) attack and several other  Network layer attacks in Cognitive Radio Network or Cognitive Radio Wireless Sensor Node Networks(WSNN’s) to analyze the advantages and disadvantages of those existing algorithms/techniques
An Empirical Estimation of CSS Cognitive Radio Network Performance under Spectrum Sensing Data Falsification Attack
Cooperative spectrum sensing (CSS) significantly improves the performance of spectrum sensing process in cognitive radio networks (CRNs). Individual spectrum sensing by a cognitive radio (CR) is often inaccurate as the channel often experiences fading and shadowing effects. CSS has been shown to have many advantages in terms of spectrum use and robustness. Despite these facts, a CSS scheme also vulnerable to many security attacks from Malicious users (MUs). In order to get unfair USAge of spectrum band, MUs can generate false spectrum sensing reports to disturb the good secondary users (SUs) decision about presence of primary user (PU). In this paper, we consider the spectrum sensing data falsification attack (SSDF) in CSS and propose the protocol to identify and eliminate the attacker or Malicious user (MU) to improve the network performance. In SSDF attack, MUs send the false spectrum sensing results to fusion center (FC) with the intension that it should make wrong decision about spectrum sensing. In this scenario, FC acts as a data collector to fuse the reports sent by SUs
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