553 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
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The THREAT-ARREST Cyber-Security Training Platform
Cyber security is always a main concern for critical infrastructures and nation-wide safety and sustainability. Thus, advanced cyber ranges and security training is becoming imperative for the involved organizations. This paper presets a cyber security training platform, called THREAT-ARREST. The various platform modules can analyze an organization’s system, identify the most critical threats, and tailor a training program to its personnel needs. Then, different training programmes are created based on the trainee types (i.e. administrator, simple operator, etc.), providing several teaching procedures and accomplishing diverse learning goals. One of the main novelties of THREAT-ARREST is the modelling of these programmes along with the runtime monitoring, management, and evaluation operations. The platform is generic. Nevertheless, its applicability in a smart energy case study is detailed
Synoptic analysis techniques for intrusion detection in wireless networks
Current system administrators are missing intrusion alerts hidden by large numbers of false positives. Rather than accumulation more data to identify true alerts, we propose an intrusion detection tool that e?ectively uses select data to provide a picture of ?network health?. Our hypothesis is that by utilizing the data available at both the node and cooperative network levels we can create a synoptic picture of the network providing indications of many intrusions or other network issues. Our major contribution is to provide a revolutionary way to analyze node and network data for patterns, dependence, and e?ects that indicate network issues. We collect node and network data, combine and manipulate it, and tease out information about the state of the network. We present a method based on utilizing the number of packets sent, number of packets received, node reliability, route reliability, and entropy to develop a synoptic picture of the network health in the presence of a sinkhole and a HELLO Flood attacker. This method conserves network throughput and node energy by requiring no additional control messages to be sent between the nodes unless an attacker is suspected. We intend to show that, although the concept of an intrusion detection system is not revolutionary, the method in which we analyze the data for clues about network intrusion and performance is highly innovative
A minimal statistical-mechanical model for multihyperuniform patterns in avian retina
Birds are known for their extremely acute sense of vision. The very peculiar
structural distribution of five different types of cones in the retina
underlies this exquisite ability to sample light. It was recently found that
each cone population as well as their total population display a disordered
pattern in which long wave-length density fluctuations vanish. This property,
known as hyperuniformity is also present in perfect crystals. In situations
like the avian retina in which both the global structure and that of each
component display hyperuniformity, the system is said to be multi-hyperuniform.
In this work, we aim at devising a minimal statistical-mechanical model that
can reproduce the main features of the spatial distribution of photoreceptors
in avian retina, namely the presence of disorder, multi-hyperuniformity and
local hetero-coordination. This last feature is key to avoid local clustering
of the same type of photoreceptors, an undesirable feature for the efficient
sampling of light. For this purpose we formulate a simple model that
definitively exhibits the required structural properties, namely an equimolar
three-component mixture (one component to sample each primary color, red,
green, and blue) of non-additive hard disks to which a long-range logarithmic
repulsion is added between like particles. A Voronoi analysis of our idealized
system of photoreceptors shows that the space-filling Voronoi polygons
interestingly display a rather uniform area distribution, symmetrically
centered around that of a regular lattice, a structural property also found in
human retina. Disordered multi-hyperuniformity offers an alternative to
generate photoreceptor patterns with minimal long-range concentration and
density fluctuations. This is the key to overcome the difficulties in devising
an efficient visual system in which crystal-like order is absent
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