43 research outputs found
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
Fully Distributed Cooperative Spectrum Sensing for Cognitive Radio Networks
Cognitive radio networks (CRN) sense spectrum occupancy and manage themselves to operate in unused bands without disturbing licensed users. The detection capability of a radio system can be enhanced if the sensing process is performed jointly by a group of nodes so that the effects of wireless fading and shadowing can be minimized. However, taking a collaborative approach poses new security threats to the system as nodes can report false sensing data to force a wrong decision. Providing security to the sensing process is also complex, as it usually involves introducing limitations to the CRN applications. The most common limitation is the need for a static trusted node that is able to authenticate and merge the reports of all CRN nodes. This paper overcomes this limitation by presenting a protocol that is suitable for fully distributed scenarios, where there is no static trusted node
Cognitive Radio Networks: Realistic or Not?
A large volume of research has been conducted in the cognitive radio (CR)
area the last decade. However, the deployment of a commercial CR network is yet
to emerge. A large portion of the existing literature does not build on real
world scenarios, hence, neglecting various important interactions of the
research with commercial telecommunication networks. For instance, a lot of
attention has been paid to spectrum sensing as the front line functionality
that needs to be completed in an efficient and accurate manner to enable an
opportunistic CR network architecture. This is necessary to detect the
existence of spectrum holes without which no other procedure can be fulfilled.
However, simply sensing (cooperatively or not) the energy received from a
primary transmitter cannot enable correct dynamic spectrum access. For example,
the low strength of a primary transmitter's signal does not assure that there
will be no interference to a nearby primary receiver. In addition, the presence
of a primary transmitter's signal does not mean that CR network users cannot
access the spectrum since there might not be any primary receiver in the
vicinity. Despite the existing elegant and clever solutions to the DSA problem
no robust, implementable scheme has emerged. In this paper, we challenge the
basic premises of the proposed schemes. We further argue that addressing the
technical challenges we face in deploying robust CR networks can only be
achieved if we radically change the way we design their basic functionalities.
In support of our argument, we present a set of real-world scenarios, inspired
by realistic settings in commercial telecommunications networks, focusing on
spectrum sensing as a basic and critical functionality in the deployment of
CRs. We use these scenarios to show why existing DSA paradigms are not amenable
to realistic deployment in complex wireless environments.Comment: Work in progres
Reinforcement learning-based trust and reputation model for spectrum leasing in cognitive radio networks
Cognitive Radio (CR), which is the next generation
wireless communication system, enables unlicensed users or
Secondary Users (SUs) to exploit underutilized spectrum (called white spaces) owned by the licensed users or Primary Users(PUs) so that bandwidth availability improves at the SUs, which helps to improve the overall spectrum utilization. Collaboration, which has been adopted in various schemes such distributed channel sensing and channel access, is an intrinsic characteristic of CR to improve network performance. However, the requirement to collaborate has inevitably open doors to various forms of attacks by malicious SUs, and this can be addressed
using Trust and Reputation Management (TRM). Generally
speaking, TRM detects malicious SUs including honest SUs that turn malicious. To achieve a more efficient detection, we advocate the use of Reinforcement Learning (RL), which is
known to be flexible and adaptable to the changes in operating environment in order to achieve optimal network performance. Its ability to learn and re-learn throughout the duration of its existence provides intelligence to the proposed TRM model, and so the focus on RL-based TRM model in this paper. Our preliminary results show that the detection performance of RLbased TRM model has an improvement of 15% over the traditional TRM in a centralized cognitive radio network. The investigation in the paper serves as an important foundation for future work in this research field
A Game-Theoretic Framework for Optimum Decision Fusion in the Presence of Byzantines
Optimum decision fusion in the presence of malicious nodes - often referred
to as Byzantines - is hindered by the necessity of exactly knowing the
statistical behavior of Byzantines. By focusing on a simple, yet widely
studied, set-up in which a Fusion Center (FC) is asked to make a binary
decision about a sequence of system states by relying on the possibly corrupted
decisions provided by local nodes, we propose a game-theoretic framework which
permits to exploit the superior performance provided by optimum decision
fusion, while limiting the amount of a-priori knowledge required. We first
derive the optimum decision strategy by assuming that the statistical behavior
of the Byzantines is known. Then we relax such an assumption by casting the
problem into a game-theoretic framework in which the FC tries to guess the
behavior of the Byzantines, which, in turn, must fix their corruption strategy
without knowing the guess made by the FC. We use numerical simulations to
derive the equilibrium of the game, thus identifying the optimum behavior for
both the FC and the Byzantines, and to evaluate the achievable performance at
the equilibrium. We analyze several different setups, showing that in all cases
the proposed solution permits to improve the accuracy of data fusion. We also
show that, in some instances, it is preferable for the Byzantines to minimize
the mutual information between the status of the observed system and the
reports submitted to the FC, rather than always flipping the decision made by
the local nodes as it is customarily assumed in previous works