135 research outputs found
Transfer Learning for Device Fingerprinting with Application to Cognitive Radio Networks
Primary user emulation (PUE) attacks are an emerging threat to cognitive
radio (CR) networks in which malicious users imitate the primary users (PUs)
signals to limit the access of secondary users (SUs). Ascertaining the identity
of the devices is a key technical challenge that must be overcome to thwart the
threat of PUE attacks. Typically, detection of PUE attacks is done by
inspecting the signals coming from all the devices in the system, and then
using these signals to form unique fingerprints for each device. Current
detection and fingerprinting approaches require certain conditions to hold in
order to effectively detect attackers. Such conditions include the need for a
sufficient amount of fingerprint data for users or the existence of both the
attacker and the victim PU within the same time frame. These conditions are
necessary because current methods lack the ability to learn the behavior of
both SUs and PUs with time. In this paper, a novel transfer learning (TL)
approach is proposed, in which abstract knowledge about PUs and SUs is
transferred from past time frames to improve the detection process at future
time frames. The proposed approach extracts a high level representation for the
environment at every time frame. This high level information is accumulated to
form an abstract knowledge database. The CR system then utilizes this database
to accurately detect PUE attacks even if an insufficient amount of fingerprint
data is available at the current time frame. The dynamic structure of the
proposed approach uses the final detection decisions to update the abstract
knowledge database for future runs. Simulation results show that the proposed
method can improve the performance with an average of 3.5% for only 10%
relevant information between the past knowledge and the current environment
signals.Comment: 6 pages, 3 figures, in Proceedings of IEEE 26th International
Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Hong
Kong, P.R. China, Aug. 201
Primary User Emulation Attacks: A Detection Technique Based on Kalman Filter
Cognitive radio technology addresses the problem of spectrum scarcity by
allowing secondary users to use the vacant spectrum bands without causing
interference to the primary users. However, several attacks could disturb the
normal functioning of the cognitive radio network. Primary user emulation
attacks are one of the most severe attacks in which a malicious user emulates
the primary user signal characteristics to either prevent other legitimate
secondary users from accessing the idle channels or causing harmful
interference to the primary users. There are several proposed approaches to
detect the primary user emulation attackers. However, most of these techniques
assume that the primary user location is fixed, which does not make them valid
when the primary user is mobile. In this paper, we propose a new approach based
on the Kalman filter framework for detecting the primary user emulation attacks
with a non-stationary primary user. Several experiments have been conducted and
the advantages of the proposed approach are demonstrated through the simulation
results.Comment: 14 pages, 9 figure
Location Aided Cooperative Detection of Primary User Emulation Attacks in Cognitive Wireless Sensor Networks Using Nonparametric Techniques
Primary user emulation (PUE) attacks are a major security challenge to cognitive wireless
sensor networks (CWSNs). In this paper, we propose two variants of the PUE attack, namely,
the relay and replay attacks. Such threats are conducted by malicious nodes that replicate
the transmissions of a real primary user (PU), thus making them resilient to many defensive
procedures. However, we show that those PUE attacks can be effectively detected by a set of cooperating secondary users (SUs), using location information and received signal strength
(RSS) measurements. Two strategies for the detection of PUE relay and replay attacks are
presented in the paper: parametric and nonparametric. The parametric scheme is based on
the likelihood ratio test (LRT) and requires the existence of a precise path loss model for
the observed RSS values. On the contrary, the nonparametric procedure is not tied to any
particular propagation model; so, it does not require any calibration process and is robust
to changing environmental conditions. Simulations show that the nonparametric detection
approach is comparable in performance to the LRT under moderate shadowing conditions,
specially in case of replay attacks
Identification as a deterrent for security enhancement in cognitive radio networks
Cognitive Radio Networks (CRNs) are prone to emerging coexistence security threats such as Primary User Emulation Attack (PUEA). Specifically, a malicious CRN may mimic licensees’ (Primary Users (PUs)) signal characteristics to force another CRN to vacate its channels thinking that PUs have returned. While existing schemes are promising to some extent on detecting PUEAs, they are not able to prevent the attacks. In this article, we propose a PUEA Deterrent (PUED) algorithm that can provide PUEAs' commission details: offender CRNs and attacks’ time and bandwidth. There are many similarities between PUED and Closed-Circuit Television (CCTV) in terms of: deterrence strategy, reason for use, surveillance characteristics, surveillance outcome, and operation site. According to the criminology literature, robust CCTV systems have shown a significant reduction in visible offences (e.g. vehicle theft), reducing crime rates by 80%. Similarly, PUED will contribute the same effectiveness in deterring PUEAs. Furthermore, providing PUEAs’ details will prevent the network’s cognitive engine from considering the attacks as real PUs, consequently avoiding devising unreliable spectrum models for the attacked channels. Extensive simulations show the effectiveness of the PUED algorithm in terms of improving CRNs’ performance
Location Aided Cooperative Detection of Primary User Emulation Attacks in Cognitive Wireless Sensor Networks Using Nonparametric Techniques
Primary user emulation (PUE) attacks are a major security challenge to cognitive wireless sensor networks (CWSNs). In this paper, we propose two variants of the PUE attack, namely, the relay and replay attacks. Such threats are conducted by malicious nodes that replicate the transmissions of a real primary user (PU), thus making them resilient to many defensive procedures. However, we show that those PUE attacks can be effectively detected by a set of cooperating secondary users (SUs), using location information and received signal strength (RSS) measurements. Two strategies for the detection of PUE relay and replay attacks are presented in the paper: parametric and nonparametric. The parametric scheme is based on the likelihood ratio test (LRT) and requires the existence of a precise path loss model for the observed RSS values. On the contrary, the nonparametric procedure is not tied to any particular propagation model; so, it does not require any calibration process and is robust to changing environmental conditions. Simulations show that the nonparametric detection approach is comparable in performance to the LRT under moderate shadowing conditions, specially in case of replay attacks
Machine Learning based RF Transmitter Characterization in the Presence of Adversaries
The advances in wireless technologies have led to autonomous deployments of various wireless networks. As these networks must co-exist, it is important that all transmitters and receivers are aware of their radio frequency (RF) surroundings so that they can learn and adapt their transmission and reception parameters to best suit their needs. To this end, machine learning techniques have become popular as they can learn, analyze and even predict the RF signals and associated parameters that characterize the RF environment. In this dissertation, we address some of the fundamental challenges on how to effectively apply different learning techniques in the RF domain. In the presence of adversaries, malicious activities such as jamming, and spoofing are inevitable which render most machine learning techniques ineffective. To facilitate learning in such settings, we propose an adversarial learning-based approach to detect unauthorized exploitation of RF spectrum. First, we show the applicability of existing machine learning algorithms in the RF domain. We design and implement three recurrent neural networks using different types of cell models for fingerprinting RF transmitters. Next, we focus on securing transmissions on dynamic spectrum access network where primary user emulation (PUE) attacks can pose a significant threat. We present a generative adversarial net (GAN) based solution to counter such PUE attacks. Ultimately, we propose recurrent neural network models which are able to accurately predict the primary users\u27 activities in DSA networks so that the secondary users can opportunistically access the shared spectrum. We implement the proposed learning models on testbeds consisting of Universal Software Radio Peripherals (USRPs) working as Software Defined Radios (SDRs). Results reveal significant accuracy gains in accurately characterizing RF transmitters- thereby demonstrating the potential of our models for real world deployments
From Sensing to Predictions and Database Technique: A Review of TV White Space Information Acquisition in Cognitive Radio Networks
Strategies to acquire white space information is the single most significant
functionality in cognitive radio networks (CRNs) and as such, it has gone some evolution
to enhance information accuracy. The evolution trends are spectrum sensing, prediction
algorithm and recently, geo-location database technique. Previously, spectrum sensing was
the main technique for detecting the presence/absence of a primary user (PU) signal in a
given radio frequency (RF) spectrum. However, this expectation could not materialized as
a result of numerous technical challenges ranging from hardware imperfections to RF
signal impairments. To convey the evolutionary trends in the development of white space
information, we present a survey of the contemporary advancements in PU detection with
emphasis on the practical deployment of CRNs i.e. Television white space (TVWS) networks.
It is found that geo-location database is the most reliable technique to acquire
TVWS information although, it is financially driven. Finally, using financially driven
database model, this study compared the data-rate and spectral efficiency of FCC and
Ofcom TV channelization. It was discovered that Ofcom TV channelization outperforms
FCC TV channelization as a result of having higher spectrum bandwidth. We proposed the
adoption of an all-inclusive TVWS information acquisition model as the future research
direction for TVWS information acquisition techniques
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