Primary User Emulation Attack (PUEA) is one of the major threats to the spectrum sensing in cognitive
radio networks. This paper studies the PUEA using energy detection that is based on the energy of the
received signal. It discusses the impact of increasing the number of attackers on the performance of
secondary user. Moreover, studying how the malicious user can emulate the Primary User (PU) signal is
made. This is the first analytical method to study PUEA under a different number of attackers. The
detection of the PUEA increases with increasing the number of attackers and decreases when changing the
channel from lognormal to Rayleigh fading.
INVESTIGATION OF PUEA IN COGNITIVE RADIO NETWORKS USING ENERGY DETECTION IN D...csijjournal
ABSTRACT
Primary User Emulation Attack (PUEA) is one of the major threats to the spectrum sensing in cognitive radio networks. This paper studies the PUEA using energy detection that is based on the energy of the received signal. It discusses the impact of increasing the number of attackers on the performance of secondary user. Moreover, studying how the malicious user can emulate the Primary User (PU) signal is made. This is the first analytical method to study PUEA under a different number of attackers. The detection of the PUEA increases with increasing the number of attackers and decreases when changing the channel from lognormal to Rayleigh fading.
KEYWORDS:
Cognitive Radio, Primary User Emulation Attacks (PUEA), energy detector.
INVESTIGATION OF PUEA IN COGNITIVE RADIO NETWORKS USING ENERGY DETECTION IN D...csijjournal
Primary User Emulation Attack (PUEA) is one of the major threats to the spectrum sensing in cognitive radio networks. This paper studies the PUEA using energy detection that is based on the energy of the received signal. It discusses the impact of increasing the number of attackers on the performance of secondary user. Moreover, studying how the malicious user can emulate the Primary User (PU) signal is made. This is the first analytical method to study PUEA under a different number of attackers. The detection of the PUEA increases with increasing the number of attackers and decreases when changing the channel from lognormal to Rayleigh fading.
Investigation of Puea in Cognitive Radio Networks Using Energy Detection in D...Abigailabril2
Primary User Emulation Attack (PUEA) is one of the major threats to the spectrum sensing in cognitive radio networks. This paper studies the PUEA using energy detection that is based on the energy of the
received signal. It discusses the impact of increasing the number of attackers on the performance of secondary user. Moreover, studying how the malicious user can emulate the Primary User (PU) signal is made. This is the first analytical method to study PUEA under a different number of attackers. The detection of the PUEA increases with increasing the number of attackers and decreases when changing the channel from lognormal to Rayleigh fading
Error rate detection due to primary user emulation attack in cognitive radio ...IJECEIAES
Security threat is a crucial issue in cognitive radio network (CRN). These threats come from physical layer, data link layer, network layer, transport layer, and application layer. Hence, security system to all layers in CRN has a responsibility to protect the communication between among Secondary User (SU) or to maintain valid detection to the presence of Primary User (PU) signals. Primary User Emulation Attack (PUEA) is a threat on physical layer where malicious user emulates PU signal. This paper studies the effect of exclusive region of PUEA in CRN. We take two setting of exclusive distances, 30m and 50m, where this radius of area is free of malicious users. Probability of false alarm (Pf) and miss detection (Pm) are used to evaluate the performances. The result shows that increasing distance of exclusive region may decrease Pf and Pm.
Signal detection in cognitive radio network (CRN) is influenced by several factors. One of them is
malicious user that emulate primary user (PU) signal. Emulation of PU signal causes detection error. This
paper investigates the impact of malicious user attack to PU signal detection. A number of malicious users
are randomly deployed around secondary user (SU) at a certain distance. They attempt to attack primary
signal detection that is transmitted from 100 km to SU receiver. Then, the received signal power at
secondary receiver and the performance of probability of false alarm and probability of miss detection
under two hypothesis of Neyman Pearson criterion are studied. The derived results show that a number of
malicious users has a significant impact to the performance of received power at SU and detection error
rate.
Performance analysis of cooperative spectrum sensing using double dynamic thr...IAESIJAI
Increased use of wireless technologies and in turn more utilization of available spectrum is subsequently leading to the increasing demand for wireless spectrum. This research work incorporates spectrum sensing detection consisting of a double dynamic threshold followed by cooperative type spectrum sensing. The performance has been analyzed using two modulation schemes, quadrature-amplitude-modulation (QAM) and binary-phase-shift-keying (BPSK). Improved probability of detection has been witnessed using the double dynamic threshold where a comparison of average values of local decision (LD) and the observed value of energy (EO) has been considered instead of using direct values of local decisions and
energy. Further, the probability-of-detection (𝑃𝑑) is found to be better with QAM as compared to the BPSK. From the results, it has been observed that the detection of primary users is also affected by the number of samples. The simulation environment considered for this work is MATLAB and the performance of cooperative spectrum sensing for 500 and 1000 samples with -9db and -12 SNR by considering different false alarm values i. e 0.1, 0.3 and 0.5 has been analyzed. The further scope shall be to enhance the primary user detection by considering different QAM schemes and different signal to noise ratio (SNRs).
A novel scheme to improve the spectrum sensing performanceIJCNCJournal
Due to limited availability of spectrum for license
d users only, the need for secondary access by unli
censed
users is increasing. Cognitive radio turns out to b
e helping this situation because all that is needed
is a
technique that could efficiently detect the empty s
paces and provide them to the secondary devices wit
hout
causing any interference to the primary (licensed)
users. Spectrum sensing is the foremost function of
the
cognitive radio which senses the environment for wh
ite spaces. Energy detection is one of the various
spectrum sensing techniques that are under research
. Earlier it was shown that energy detection works
better under AWGN channel as compared to Rayleigh c
hannel, however the conventional spectrum sensing
techniques have a high probability of false alarm a
nd also show a better probability of detection for
higher
values of SNR. There is a need for a new technique
that shows a reduced probability of false alarm as
well
as an increase in the probability of detection for
lower values of SNR. In the present work the conven
tional
energy detection technique has been enhanced to get
better results.
INVESTIGATION OF PUEA IN COGNITIVE RADIO NETWORKS USING ENERGY DETECTION IN D...csijjournal
ABSTRACT
Primary User Emulation Attack (PUEA) is one of the major threats to the spectrum sensing in cognitive radio networks. This paper studies the PUEA using energy detection that is based on the energy of the received signal. It discusses the impact of increasing the number of attackers on the performance of secondary user. Moreover, studying how the malicious user can emulate the Primary User (PU) signal is made. This is the first analytical method to study PUEA under a different number of attackers. The detection of the PUEA increases with increasing the number of attackers and decreases when changing the channel from lognormal to Rayleigh fading.
KEYWORDS:
Cognitive Radio, Primary User Emulation Attacks (PUEA), energy detector.
INVESTIGATION OF PUEA IN COGNITIVE RADIO NETWORKS USING ENERGY DETECTION IN D...csijjournal
Primary User Emulation Attack (PUEA) is one of the major threats to the spectrum sensing in cognitive radio networks. This paper studies the PUEA using energy detection that is based on the energy of the received signal. It discusses the impact of increasing the number of attackers on the performance of secondary user. Moreover, studying how the malicious user can emulate the Primary User (PU) signal is made. This is the first analytical method to study PUEA under a different number of attackers. The detection of the PUEA increases with increasing the number of attackers and decreases when changing the channel from lognormal to Rayleigh fading.
Investigation of Puea in Cognitive Radio Networks Using Energy Detection in D...Abigailabril2
Primary User Emulation Attack (PUEA) is one of the major threats to the spectrum sensing in cognitive radio networks. This paper studies the PUEA using energy detection that is based on the energy of the
received signal. It discusses the impact of increasing the number of attackers on the performance of secondary user. Moreover, studying how the malicious user can emulate the Primary User (PU) signal is made. This is the first analytical method to study PUEA under a different number of attackers. The detection of the PUEA increases with increasing the number of attackers and decreases when changing the channel from lognormal to Rayleigh fading
Error rate detection due to primary user emulation attack in cognitive radio ...IJECEIAES
Security threat is a crucial issue in cognitive radio network (CRN). These threats come from physical layer, data link layer, network layer, transport layer, and application layer. Hence, security system to all layers in CRN has a responsibility to protect the communication between among Secondary User (SU) or to maintain valid detection to the presence of Primary User (PU) signals. Primary User Emulation Attack (PUEA) is a threat on physical layer where malicious user emulates PU signal. This paper studies the effect of exclusive region of PUEA in CRN. We take two setting of exclusive distances, 30m and 50m, where this radius of area is free of malicious users. Probability of false alarm (Pf) and miss detection (Pm) are used to evaluate the performances. The result shows that increasing distance of exclusive region may decrease Pf and Pm.
Signal detection in cognitive radio network (CRN) is influenced by several factors. One of them is
malicious user that emulate primary user (PU) signal. Emulation of PU signal causes detection error. This
paper investigates the impact of malicious user attack to PU signal detection. A number of malicious users
are randomly deployed around secondary user (SU) at a certain distance. They attempt to attack primary
signal detection that is transmitted from 100 km to SU receiver. Then, the received signal power at
secondary receiver and the performance of probability of false alarm and probability of miss detection
under two hypothesis of Neyman Pearson criterion are studied. The derived results show that a number of
malicious users has a significant impact to the performance of received power at SU and detection error
rate.
Performance analysis of cooperative spectrum sensing using double dynamic thr...IAESIJAI
Increased use of wireless technologies and in turn more utilization of available spectrum is subsequently leading to the increasing demand for wireless spectrum. This research work incorporates spectrum sensing detection consisting of a double dynamic threshold followed by cooperative type spectrum sensing. The performance has been analyzed using two modulation schemes, quadrature-amplitude-modulation (QAM) and binary-phase-shift-keying (BPSK). Improved probability of detection has been witnessed using the double dynamic threshold where a comparison of average values of local decision (LD) and the observed value of energy (EO) has been considered instead of using direct values of local decisions and
energy. Further, the probability-of-detection (𝑃𝑑) is found to be better with QAM as compared to the BPSK. From the results, it has been observed that the detection of primary users is also affected by the number of samples. The simulation environment considered for this work is MATLAB and the performance of cooperative spectrum sensing for 500 and 1000 samples with -9db and -12 SNR by considering different false alarm values i. e 0.1, 0.3 and 0.5 has been analyzed. The further scope shall be to enhance the primary user detection by considering different QAM schemes and different signal to noise ratio (SNRs).
A novel scheme to improve the spectrum sensing performanceIJCNCJournal
Due to limited availability of spectrum for license
d users only, the need for secondary access by unli
censed
users is increasing. Cognitive radio turns out to b
e helping this situation because all that is needed
is a
technique that could efficiently detect the empty s
paces and provide them to the secondary devices wit
hout
causing any interference to the primary (licensed)
users. Spectrum sensing is the foremost function of
the
cognitive radio which senses the environment for wh
ite spaces. Energy detection is one of the various
spectrum sensing techniques that are under research
. Earlier it was shown that energy detection works
better under AWGN channel as compared to Rayleigh c
hannel, however the conventional spectrum sensing
techniques have a high probability of false alarm a
nd also show a better probability of detection for
higher
values of SNR. There is a need for a new technique
that shows a reduced probability of false alarm as
well
as an increase in the probability of detection for
lower values of SNR. In the present work the conven
tional
energy detection technique has been enhanced to get
better results.
Performance Analysis of Group-Blind Multiuser Detectors for Synchronous CDMAidescitation
Blind multiuser detectors are attractive for the
suppression of interference in a CDMA environment. This
paper deals with the performance of group blind multiuser
detector for synchronous CDMA is analyzed. The blind multi
user detectors are Direct Matrix Inversion(DMI),Subspace
and group blind multiuser detector. The performance
analysis is performed by means of the Signal to Interference
Noise Ratio(SINR) and Bit Error Rate(BER). The numerical
results are plotted as variation of SINR Vs SNR, K and M,
SINR with respect to correlation coefficient( ) and BER
Vs Number of samples(M) for three detectors using
MATLAB software. The gain rises in group blind multiuser
detector over the DMI and subspace detectors. The
comparison is carried out for equicorrelated signals for
mathematical simplicity.
An Optimized Energy Detection Scheme For Spectrum Sensing In Cognitive RadioIJERD Editor
With rapid growth of wireless devices, the Scarcity of Spectrum resources arises ,due to the improper and inefficient usage of available spectrum band. This problem can be alleviated by Cognitive radio . The major function of the cognitive radio rely on efficient sensing of available spectrum and Spectrum sensing techniques have been used to enhance the detection performance. Among these techniques, Energy detection is considered to be the implemented in practice because of less complexity. In this paper we propose an Adaptive threshold scheme which improves the detection performance under low SNR region. In this paper, noise uncertainty factor is considered wherein the Probability of error is minimized in various SNR regions.
INTRUSION DETECTION SYSTEM USING DISCRETE FOURIER TRANSFORM WITH WINDOW FUNCTIONIJNSA Journal
An Intrusion Detection System (IDS) is countermeasureagainst network attack. There are mainly two typesof detections; signature-based and anomaly-based. And thereare two kinds of error; false negative and false positive. Indevelopment of IDS, establishment of a method to reduce suchfalse is a major issue. In this paper, we propose a new anomaly-baseddetection method using Discrete Fourier Transform (DFT)with window function. In our method, we assume fluctuation ofpayload in ordinary sessions as random. On the other hand, we cansee fluctuation in attack sessions have bias. From the viewpointof spectrum analysis based on such assumption, we can find outdifferent characteristic in spectrum of attack sessions. Using thecharacteristic, we can detect attack sessions. Example detectionagainst Kyoto2006+ dataset shows 12.0% of false positive at most,and 0.0% of false negative.
INTRUSION DETECTION SYSTEM USING DISCRETE FOURIER TRANSFORM WITH WINDOW FUNCTIONIJNSA Journal
An Intrusion Detection System (IDS) is countermeasureagainst network attack. There are mainly two
typesof detections; signature-based and anomaly-based. And thereare two kinds of error; false negative
and false positive. Indevelopment of IDS, establishment of a method to reduce suchfalse is a major issue.
In this paper, we propose a new anomaly-baseddetection method using Discrete Fourier Transform
(DFT)with window function. In our method, we assume fluctuation ofpayload in ordinary sessions as
random. On the other hand, we cansee fluctuation in attack sessions have bias. From the viewpointof
spectrum analysis based on such assumption, we can find outdifferent characteristic in spectrum of attack
sessions. Using thecharacteristic, we can detect attack sessions. Example detectionagainst Kyoto2006+
dataset shows 12.0% of false positive at most,and 0.0% of false negative.
Intrusion detection system using discrete fourier transform with window functionIJNSA Journal
An Intrusion Detection System (IDS) is counter measure against network attack. There are mainly two types of detections; signature-based and anomaly-based. And there are two kinds of error; false negative and false positive. In development of IDS, establishment of a method to reduce such false is a major issue. In this paper, we propose a new anomaly-based detection method using Discrete Fourier Transform (DFT)with window function. In our method, we assume fluctuation of payload in ordinary sessions as random. On the other hand, we can see fluctuation in attack sessions have bias. From the view point of spectrum analysis based on such assumption, we can find out different characteristic in spectrum of attack sessions. Using thecharacteristic, we can detect attack sessions. Example detection against Kyoto2006+
dataset shows 12.0% of false positive at most,and 0.0% of false negative.
Derivative threshold actuation for single phase wormhole detection with reduc...ijdpsjournal
Communication in mobile Ad hoc networks is completed via multi
-
hop ways. Owing to the distributed
specification and restricted resource of nodes, MANET is a lot prone
to wormhole attacks i.e. wormhole
attacks place severe threats to each Ad hoc routing protocol and a few security enhancements. Thus,
so as
to discover wormholes, totally different techniques are in use. In all those techniques fixation of
threshold
is mer
ely by trial & error methodology or by random manner. Conjointly wormhole detection is in twin
part by putting the nodes that is higher than the edge in a suspicious set, however predicting the n
ode as a
wormhole by using some other algorithms. Our aim in
this paper is to deduce the traffic threshold level by
derivational approach for identifying wormholes in a very single phase in relay network having dissi
milar
characteristics.
Performance Analysis and Comparative Study of Cognitive Radio Spectrum Sensin...IOSR Journals
Abstract : In cognitive radio, spectrum sensing is an emergent technology to find available and unused spectrum for increasing spectrum utilization and to overcome spectrum scarcity problem without harmful interference to licensed users. Cooperative spectrum sensing is used to give reliable performance in terms of detection probability and false alarm probability as well as in order to reduce fading, noise and shadowing effects on cognitive radio users. In this paper according to detection performance and complexity various cooperative spectrum sensing schemes have been discussed. We have analyzed spectrum sensing with different fusion rules and their comparative behavior has also been studied. Furthermore, we introduced AND-OR fusion rules in 2-bit and 3-bit hard combination schemes. Keywords - Cognitive radio, cooperative spectrum sensing, energy detector, spectrum sensing, hard combination
Performance Analysis and Comparative Study of Cognitive Radio Spectrum Sensin...IOSR Journals
In cognitive radio, spectrum sensing is an emergent technology to find available and unused
spectrum for increasing spectrum utilization and to overcome spectrum scarcity problem without harmful
interference to licensed users. Cooperative spectrum sensing is used to give reliable performance in terms of
detection probability and false alarm probability as well as in order to reduce fading, noise and shadowing
effects on cognitive radio users. In this paper according to detection performance and complexity various
cooperative spectrum sensing schemes have been discussed. We have analyzed spectrum sensing with different fusion rules and their comparative behavior has also been studied. Furthermore, we introduced AND-OR fusion rules in 2-bit and 3-bit hard combination schemes
Comparative Study of Different Non-Cooperative Techniques in Cognitive RadioRSIS International
Wireless technology is expanding its domain and with it
is growing the need for more frequencies for communication.
Cognitive radio offers a solution to this problem by using the
concept of Dynamic spectrum access instead of fixed spectrum
allocation. Such radios are capable of sensing the RF spectrum
for identifying idle frequency bands. It then transmits
opportunistically so as to avoid interference with primary user
over same band. In cognitive radio, intelligent spectrum sensing
forms the major and most important part. Out of the various
sensing techniques, we will give an overview of some of the
prominent non-cooperative techniques. The paper deals with
comparative study of these methods.
Detection of PUE Attack by SPARS Model using WSPRTEditor IJCATR
Cognitive radio is a system which improves the utilization of the spectrum by sensing the white spaces in its vicinity. This
sensed information will be utilized by the Secondary User (SU) to transmit the data. But some of the malicious users attacks th
system by generating the signal same as that of the primary transmitter. The attack caused by generating the signal same as t
primary transmitter is called as Primary User Emulation Attack (PUEA). In this paper the Signal Activity Pattern Acquisition
Reconstruction System (SPARS) is used to detect the attack. But this system suffers from low True Positive Rate. To incr
positive rate or sensitivity a new technique was proposed called as Weighted Sequential Probability Ratio Test (WSPRT). By
improving the true positive rate or sensitivity, the detection capability of the system will be improved.
Detection of PUE Attack by SPARS Model using WSPRTEditor IJCATR
Cognitive radio is a system which improves the utilization of the spectrum by sensing the white spaces in its vicinity. This
sensed information will be utilized by the Secondary User (SU) to transmit the data. But some of the malicious users attacks th
system by generating the signal same as that of the primary transmitter. The attack caused by generating the signal same as t
primary transmitter is called as Primary User Emulation Attack (PUEA). In this paper the Signal Activity Pattern Acquisition
Reconstruction System (SPARS) is used to detect the attack. But this system suffers from low True Positive Rate. To incr
positive rate or sensitivity a new technique was proposed called as Weighted Sequential Probability Ratio Test (WSPRT). By
improving the true positive rate or sensitivity, the detection capability of the system will be improved.
Performance Evaluation of Computationally Efficient Energy Detection Based Sp...IJRST Journal
The rapid growth of bandwidth demanding wireless technologies has led to the problem of spectrum scarcity. However, studies show that licensed spectrum is underutilized. Cognitive radio technology promises a solution to the problem by allowing unlicensed users, access to the licensed bands opportunistically. A prime component of the cognitive radio technology is spectrum sensing. Many spectrum sensing techniques have been developed to sense the presence or not of a licensed user. This paper evaluates the performance of the energy detection based spectrum sensing technique in noisy, fading, jamming, interference environments. Both single user detection and cooperative detection situations were investigated. Closed form solutions for the probabilities of detection and false alarm were derived. The analytical results were varied by numerical computations using Monte Carlo method with MATLAB. The performance of the computationally efficient energy detection (CE-ED) techniques were evaluated by use of Receiver Operating Characteristics (ROC) curves over additive white Gaussian noise (AWGN) and fading (Rayleigh & Nakagami-m) channels. Results show that for single user detection, the energy detection technique performs better in AWGN channel than in the fading channel models. The performance of cooperative detection is better than single user detection in fading environments.
Design of matched filter for radar applicationselelijjournal
The aim of this paper is to present the details of signal processing techniques in Military RADARS . These
techniques are strongly based on mathematics and specially on stochastic processes. Detecting a target in
a noisy environment is a many folds sequential process. The signal processing chain only provides to the
overall system boolean indicators stating the presence (or not) of targets inside the coverage area. It is
part of the strategical operation of the radar. This paper mainly focuses on Design of Matched filter and
generation of chirp Signal.
A HYBRID APPROACH COMBINING RULE-BASED AND ANOMALY-BASED DETECTION AGAINST DD...IJNSA Journal
We have designed a hybrid approach combining rule-based and anomaly-based detection against DDoS attacks. In the approach, the rule-based detection has established a set of rules and the anomaly-based detection use one-way ANOVA test to detect possible attacks. We adopt TFN2K (Tribe Flood, the Net 2K) as an attack traffic generator and monitor the system resource of the victim like throughput, memory utilization, CPU utilization consumed by attack traffic. Target users of the proposed scheme are data center administrators. The types of attack traffic have been analysed and by that we develop a defense scheme. The experiment has demonstrated that the proposed scheme can effectively detect the attack traffic.
A HYBRID APPROACH COMBINING RULE-BASED AND ANOMALY-BASED DETECTION AGAINST DD...IJNSA Journal
We have designed a hybrid approach combining rule-based and anomaly-based detection against DDoS attacks. In the approach, the rule-based detection has established a set of rules and the anomaly-based detection use one-way ANOVA test to detect possible attacks. We adopt TFN2K (Tribe Flood, the Net 2K) as an attack traffic generator and monitor the system resource of the victim like throughput, memory
utilization, CPU utilization consumed by attack traffic. Target users of the proposed scheme are data center administrators. The types of attack traffic have been analysed and by that we develop a defense scheme. The experiment has demonstrated that the proposed scheme can effectively detect the attack traffic.
A HYBRID APPROACH COMBINING RULE-BASED AND ANOMALY-BASED DETECTION AGAINST DD...IJNSA Journal
We have designed a hybrid approach combining rule-based and anomaly-based detection against DDoS
attacks. In the approach, the rule-based detection has established a set of rules and the anomaly-based
detection use one-way ANOVA test to detect possible attacks. We adopt TFN2K (Tribe Flood, the Net 2K)
as an attack traffic generator and monitor the system resource of the victim like throughput, memory
utilization, CPU utilization consumed by attack traffic. Target users of the proposed scheme are data
center administrators. The types of attack traffic have been analysed and by that we develop a defense
scheme. The experiment has demonstrated that the proposed scheme can effectively detect the attack traffic.
Performance Analysis of Group-Blind Multiuser Detectors for Synchronous CDMAidescitation
Blind multiuser detectors are attractive for the
suppression of interference in a CDMA environment. This
paper deals with the performance of group blind multiuser
detector for synchronous CDMA is analyzed. The blind multi
user detectors are Direct Matrix Inversion(DMI),Subspace
and group blind multiuser detector. The performance
analysis is performed by means of the Signal to Interference
Noise Ratio(SINR) and Bit Error Rate(BER). The numerical
results are plotted as variation of SINR Vs SNR, K and M,
SINR with respect to correlation coefficient( ) and BER
Vs Number of samples(M) for three detectors using
MATLAB software. The gain rises in group blind multiuser
detector over the DMI and subspace detectors. The
comparison is carried out for equicorrelated signals for
mathematical simplicity.
An Optimized Energy Detection Scheme For Spectrum Sensing In Cognitive RadioIJERD Editor
With rapid growth of wireless devices, the Scarcity of Spectrum resources arises ,due to the improper and inefficient usage of available spectrum band. This problem can be alleviated by Cognitive radio . The major function of the cognitive radio rely on efficient sensing of available spectrum and Spectrum sensing techniques have been used to enhance the detection performance. Among these techniques, Energy detection is considered to be the implemented in practice because of less complexity. In this paper we propose an Adaptive threshold scheme which improves the detection performance under low SNR region. In this paper, noise uncertainty factor is considered wherein the Probability of error is minimized in various SNR regions.
INTRUSION DETECTION SYSTEM USING DISCRETE FOURIER TRANSFORM WITH WINDOW FUNCTIONIJNSA Journal
An Intrusion Detection System (IDS) is countermeasureagainst network attack. There are mainly two typesof detections; signature-based and anomaly-based. And thereare two kinds of error; false negative and false positive. Indevelopment of IDS, establishment of a method to reduce suchfalse is a major issue. In this paper, we propose a new anomaly-baseddetection method using Discrete Fourier Transform (DFT)with window function. In our method, we assume fluctuation ofpayload in ordinary sessions as random. On the other hand, we cansee fluctuation in attack sessions have bias. From the viewpointof spectrum analysis based on such assumption, we can find outdifferent characteristic in spectrum of attack sessions. Using thecharacteristic, we can detect attack sessions. Example detectionagainst Kyoto2006+ dataset shows 12.0% of false positive at most,and 0.0% of false negative.
INTRUSION DETECTION SYSTEM USING DISCRETE FOURIER TRANSFORM WITH WINDOW FUNCTIONIJNSA Journal
An Intrusion Detection System (IDS) is countermeasureagainst network attack. There are mainly two
typesof detections; signature-based and anomaly-based. And thereare two kinds of error; false negative
and false positive. Indevelopment of IDS, establishment of a method to reduce suchfalse is a major issue.
In this paper, we propose a new anomaly-baseddetection method using Discrete Fourier Transform
(DFT)with window function. In our method, we assume fluctuation ofpayload in ordinary sessions as
random. On the other hand, we cansee fluctuation in attack sessions have bias. From the viewpointof
spectrum analysis based on such assumption, we can find outdifferent characteristic in spectrum of attack
sessions. Using thecharacteristic, we can detect attack sessions. Example detectionagainst Kyoto2006+
dataset shows 12.0% of false positive at most,and 0.0% of false negative.
Intrusion detection system using discrete fourier transform with window functionIJNSA Journal
An Intrusion Detection System (IDS) is counter measure against network attack. There are mainly two types of detections; signature-based and anomaly-based. And there are two kinds of error; false negative and false positive. In development of IDS, establishment of a method to reduce such false is a major issue. In this paper, we propose a new anomaly-based detection method using Discrete Fourier Transform (DFT)with window function. In our method, we assume fluctuation of payload in ordinary sessions as random. On the other hand, we can see fluctuation in attack sessions have bias. From the view point of spectrum analysis based on such assumption, we can find out different characteristic in spectrum of attack sessions. Using thecharacteristic, we can detect attack sessions. Example detection against Kyoto2006+
dataset shows 12.0% of false positive at most,and 0.0% of false negative.
Derivative threshold actuation for single phase wormhole detection with reduc...ijdpsjournal
Communication in mobile Ad hoc networks is completed via multi
-
hop ways. Owing to the distributed
specification and restricted resource of nodes, MANET is a lot prone
to wormhole attacks i.e. wormhole
attacks place severe threats to each Ad hoc routing protocol and a few security enhancements. Thus,
so as
to discover wormholes, totally different techniques are in use. In all those techniques fixation of
threshold
is mer
ely by trial & error methodology or by random manner. Conjointly wormhole detection is in twin
part by putting the nodes that is higher than the edge in a suspicious set, however predicting the n
ode as a
wormhole by using some other algorithms. Our aim in
this paper is to deduce the traffic threshold level by
derivational approach for identifying wormholes in a very single phase in relay network having dissi
milar
characteristics.
Performance Analysis and Comparative Study of Cognitive Radio Spectrum Sensin...IOSR Journals
Abstract : In cognitive radio, spectrum sensing is an emergent technology to find available and unused spectrum for increasing spectrum utilization and to overcome spectrum scarcity problem without harmful interference to licensed users. Cooperative spectrum sensing is used to give reliable performance in terms of detection probability and false alarm probability as well as in order to reduce fading, noise and shadowing effects on cognitive radio users. In this paper according to detection performance and complexity various cooperative spectrum sensing schemes have been discussed. We have analyzed spectrum sensing with different fusion rules and their comparative behavior has also been studied. Furthermore, we introduced AND-OR fusion rules in 2-bit and 3-bit hard combination schemes. Keywords - Cognitive radio, cooperative spectrum sensing, energy detector, spectrum sensing, hard combination
Performance Analysis and Comparative Study of Cognitive Radio Spectrum Sensin...IOSR Journals
In cognitive radio, spectrum sensing is an emergent technology to find available and unused
spectrum for increasing spectrum utilization and to overcome spectrum scarcity problem without harmful
interference to licensed users. Cooperative spectrum sensing is used to give reliable performance in terms of
detection probability and false alarm probability as well as in order to reduce fading, noise and shadowing
effects on cognitive radio users. In this paper according to detection performance and complexity various
cooperative spectrum sensing schemes have been discussed. We have analyzed spectrum sensing with different fusion rules and their comparative behavior has also been studied. Furthermore, we introduced AND-OR fusion rules in 2-bit and 3-bit hard combination schemes
Comparative Study of Different Non-Cooperative Techniques in Cognitive RadioRSIS International
Wireless technology is expanding its domain and with it
is growing the need for more frequencies for communication.
Cognitive radio offers a solution to this problem by using the
concept of Dynamic spectrum access instead of fixed spectrum
allocation. Such radios are capable of sensing the RF spectrum
for identifying idle frequency bands. It then transmits
opportunistically so as to avoid interference with primary user
over same band. In cognitive radio, intelligent spectrum sensing
forms the major and most important part. Out of the various
sensing techniques, we will give an overview of some of the
prominent non-cooperative techniques. The paper deals with
comparative study of these methods.
Detection of PUE Attack by SPARS Model using WSPRTEditor IJCATR
Cognitive radio is a system which improves the utilization of the spectrum by sensing the white spaces in its vicinity. This
sensed information will be utilized by the Secondary User (SU) to transmit the data. But some of the malicious users attacks th
system by generating the signal same as that of the primary transmitter. The attack caused by generating the signal same as t
primary transmitter is called as Primary User Emulation Attack (PUEA). In this paper the Signal Activity Pattern Acquisition
Reconstruction System (SPARS) is used to detect the attack. But this system suffers from low True Positive Rate. To incr
positive rate or sensitivity a new technique was proposed called as Weighted Sequential Probability Ratio Test (WSPRT). By
improving the true positive rate or sensitivity, the detection capability of the system will be improved.
Detection of PUE Attack by SPARS Model using WSPRTEditor IJCATR
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The rapid growth of bandwidth demanding wireless technologies has led to the problem of spectrum scarcity. However, studies show that licensed spectrum is underutilized. Cognitive radio technology promises a solution to the problem by allowing unlicensed users, access to the licensed bands opportunistically. A prime component of the cognitive radio technology is spectrum sensing. Many spectrum sensing techniques have been developed to sense the presence or not of a licensed user. This paper evaluates the performance of the energy detection based spectrum sensing technique in noisy, fading, jamming, interference environments. Both single user detection and cooperative detection situations were investigated. Closed form solutions for the probabilities of detection and false alarm were derived. The analytical results were varied by numerical computations using Monte Carlo method with MATLAB. The performance of the computationally efficient energy detection (CE-ED) techniques were evaluated by use of Receiver Operating Characteristics (ROC) curves over additive white Gaussian noise (AWGN) and fading (Rayleigh & Nakagami-m) channels. Results show that for single user detection, the energy detection technique performs better in AWGN channel than in the fading channel models. The performance of cooperative detection is better than single user detection in fading environments.
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INVESTIGATION OF PUEA IN COGNITIVE RADIO NETWORKS USING ENERGY DETECTION IN DIFFERENT CHANNEL MODEL
1. Circuits and Systems: An International Journal (CSIJ), Vol.2, No.2/3/4, October 2015
DOI: 10.5121/csij.2015.2401 1
INVESTIGATION OF PUEA IN COGNITIVE
RADIO NETWORKS USING ENERGY
DETECTION IN DIFFERENT CHANNEL
MODEL
1
Walid. R. Ghanem, 2
Mona Shokair and 3
MI Dessouky
1, 2, 3
Department of electronic and electrical communication, Faculty of electronic
engineering, Menouf, Egypt
ABSTRACT
Primary User Emulation Attack (PUEA) is one of the major threats to the spectrum sensing in cognitive
radio networks. This paper studies the PUEA using energy detection that is based on the energy of the
received signal. It discusses the impact of increasing the number of attackers on the performance of
secondary user. Moreover, studying how the malicious user can emulate the Primary User (PU) signal is
made. This is the first analytical method to study PUEA under a different number of attackers. The
detection of the PUEA increases with increasing the number of attackers and decreases when changing the
channel from lognormal to Rayleigh fading.
KEYWORDS:
Cognitive Radio, Primary User Emulation Attacks (PUEA), energy detector.
1. INTRODUCTION
Cognitive Radio Networks (CRNs) are a new technology that uses the unused spectrum to enable
much higher spectrum efficiency. The concept of cognitive radio is introduced in [1-6], where
secondary (unlicensed) users utilize the licensed frequencies while the primary user (licensed)
user is absent. To make this utilization, sensing process is needed to know the situation of the
primary user. If the secondary user senses that primary user do not transmit, they can use the
channel to transmit, otherwise, secondary user detect the presence of the primary user it stops
transmitting. Some problems will be found during this process. One of these problems is made
due to many attacks such as a denial of service attack, false sensing data report attack, and
primary user emulation attack. The last one of attack is a serious problem, which is presented by
R. Chen in [7]. When the primary user does not use the spectrum, a malicious user or attacker
sends a signal whose characteristics emulates that of the primary user therefore the secondary
user may think that this signal is from the primary user and thus prevented from accessing the
CRNs. Recently, Primary user emulation attack (PUEA) has been studied in many researches. R.
Chen proposed to use the location of the primary user to identify the primary user emulation
attack [7]. S. Annand made an analytical model based on Fenton's approximation and Markov
inequality [8]. Z. Jin et al. Presented a Neyman–Pearson composite hypothesis test [9] and a
Wald's sequential probability ratio test [10] to detect PUEA. Z. Chen showed how the attacker
emulated the primary user signal to confuse the secondary user and use an advanced strategy
called variance detection to mitigate the effect of an attacker using the difference between the
communication channel of PUEA and primary user [11]. C. Chen et al made a joint position
verification method to enhance the positioning accuracy [12]. Moreover, C. Chen et al discussed
2. Circuits and Systems: An International Journal (CSIJ), Vol.2, No.2/3/4, October 2015
2
the cooperative spectrum sensing model in the presence of PUEA and established a scheme to
maximize the detection probability of PU [13]. Feijng Bao et al studied the PUEA with the
motion secondary users in cognitive radio network and using a hybrid method based on Energy
Detection (ED) and Variance Detection [14]. ED is one technique from some techniques
depending on the sensing which is the basis of cognitive radio network.
Another technique such as matched filters (MF), cyclostationary detection, covariance detection,
Eigen value based detection, wavelet edge detection. All existing PUEA detection used ED due to
its simplicity and have no prior information about the detecting signal.
In this paper, analytical method is used to study PUEA under a different number of attackers
when the attack strategy is used by each attacker, which hasn't done before. In this system, no
cooperation is considered between the attackers. Therefore, each attacker wants to fool the SU
with transmitting a signal whose characteristics mimics the primary user signal. The victim user
(SU) receives signals from PU and the attacker and makes its decision.
The remainder of this paper is structured as follows. In Section 2, the problem formulation is
introduced. In Section 3, an analytical model for energy detection strategy against primary user
emulation attacks for many numbers of attackers under the strategy used by each attacker. The
numerical and simulation results will be made in Section 4. Finally, conclusions will be done in
Section 5.
2. PROBLEM FORMULATION
2.1. SYTEM MODEL
The network model shown in Fig 1. where the attackers and secondary user (victim) are located
in circular grid. The primary user is a TV tower located at a distance of dp from the CRN and all
users position is fixed in the network. Each attacker wants to fool the victim by transmitting a
signal whose characteristic emulates that of the primary user. The victim listens to the channel to
distinguish between the signal coming from the primary user or the attacker.
Fig. 1 System model of the CRN [8]
The following assumptions are considered for proposed model specification:
1. dJ is the distance between the Jth
attacker and the victim, the target region in which each
Attacker wants to fool the victim is a loop of radius Ro and R1.
2. The primary transmitter located at a distance of dP from the Cognitive Radio network.
3. Circuits and Systems: An International Journal (CSIJ), Vol.2, No.2/3/4, October 2015
3
3. The primary user transmits a power of Pt and each attacker transmit an adaptive power of
Ps.
4. The signal from primary and the attacker undergoes path loss and, lognormal, or fading.
5. At the victim the free space propagation model is considered for the signal from primary
and two ray ground model for the signal from the attacker, respectively. The received signal at
victim from the primary is proportional to
-2
P
d , and from the attacker is proportional to 4
J
d
[9].
6. The shadowing random variable for the primary transmitter is[15]
(1)
where
ln10
10
a and
2
(0, )
P
N
p
follows a normal distribution with zero mean and variance
equal to 2
P
.
7. The shadowing random variable for the attacker is [15]
(2)
Where
ln10
10
a , 2
(0, )
s
N
s
follows normal distribution, with zero mean and variance equal
to 2
s
.
8. NO cooperation is consumed between the attackers.
2.2. Performance Metrics Parameters
This section discusses the metric parameter for the modal performance measurement. Most
existing work on cognitive sensing focuses on performing a hypothesis testing to decide the
presence of the primary user [11]. In this paper, the interaction between the attacker and the
victim are discussed. As a result, in our work a victim (or a defender) performs a hypothesis
testing to decide whether a signal is from the primary user or from the attacker,
As shown in the following two hypotheses [11].
Ho: the signal is from the primary user
H1: the signal is from the attacker
In the hypothesis testing, two matrices are used to demonstrate the performance of strategies
taken by the attacker and the victim [11].
Probability of false positives (PFP) or (probability of false alarm (PFA)):
When the signal is from the primary user, the probability that the victim falsely identifies as the
signal from the attacker is expressed as [14],
1 0
Pr( )
FP
P H
(3)
If this case happens, the victim will attempt to access the network and cause interference to the
primary user. Then the victim may be punished as an attacker user. Hence the victim may use a
strategy to make PFP (PFA) as small as possible while the attacker want to make PFP (PFA) as
large as possible [11].
10
10
p
P
a P
e
G
10
10
s
s
a s
e
G
4. Circuits and Systems: An International Journal (CSIJ), Vol.2, No.2/3/4, October 2015
4
Probability of false negatives (PFN) or the probability of misdetection (PMD):
When the signal is for the attacker, the probability that the victim falsely classifies it as from the
primary user is defined as [11],
0 1
Pr( )
FN
P H
(4)
If this case happens, the victim will vacate the spectrum unnecessarily or give up accessing the
network, although the spectrum band is vacant, and the attacker launches a successful PUEA and
take the spectrum resource.
Another widely matrices is the probability of detection (PD) [14].
1 1
1- Pr( )
D FN
P H
P
(5)
The victim should take a strategy to make FN
P ( MD
P ) as small as possible where the attacker
aims to make FN
P or probability of miss detection ( MD
P ) as large as possible.
3. ANAYLTICAL MODEL
3.1. The attack strategy
In this part we describe by the equation the attack strategy used by the attacker. Each attacker
wants to fool the victim (SU) by transmitting a signal whose characteristics emulates that of the
primary user to make PFN and PFP as large as possible, in [11] a mean-field approach is used to
derive a solution of Ps and this method focus on the average of the received power Ignoring
fluctuations this approach describe in Fig.2. Where an attacker receive a power from the primary
user and transmit the emulating power to the secondary user.
Fig.2 The attacker strategy
The received power at the victim from primary user, ( )
v
r
P , is denoted as follows[9]:
( ) -2 -2
v a p
r t p p t p
P P d G P d e
(6)
Where p
G is the shadowing random variable from the primary user to the victim
The received power at victim from each of the jth
attacker is given as [9],
( ) -4 -4 s
j j
j j
a
sj
r s s s
P Ps d G Ps d e
(7)
The attacker mimics the PU
signal and retransmit it to the SU
To take the free band for himself
PU
SU
Victim
Attacker
5. Circuits and Systems: An International Journal (CSIJ), Vol.2, No.2/3/4, October 2015
5
Where s
G is the shadowing random variable from the primary user to the jth
attackers?
The Moment generating function ( )
t
of a random variable β is expressed as [15],
1 2
2
2
( ) ( )
t
t
t E e e
(8)
The mean of ( )
P v
r that given by (3) and ( )
P s
r that given by (4)
1 2 2
2
( ) -2 -2 -2
( ) ( ) ( )
a p
v a
r t p p t p p
p
t
E P P d E G P d E e e
P d
(9)
1 2 2
2
( ) -4 -4 -4
( ) ( ) )
(
j a s
s a
sj
r sj s sj sj sj s
s
j
E P P d E G P d E e e
P d
(10)
The attacker emulates the primary user signal under the condition of ( ) ( )
E(P ) E(P )
sj v
r r
[11]
Thus the power of each attacker is expressed as,
1 2 2 2
( - )
2
sj
sj t
p
a P s
d
e
P P
d
(11)
3.2. The Energy Detection Defense Model
In this part the mathematical analysis of probability of false Positive (PFP) that is given by (1) and
probability of false negative that given by (2) will be made.
The energy detection is used to defense the PUEA as follows [11]:
2
IF r r r
p u k
: The signal from the primary user (H0) (12)
2
IF r r r
p u k
: The signal from the malicious user (H1) (13)
Where Pr is the received power at the victim , and k (k > 0) is a constant that controls the
threshold of determination and is called the threshold factor.
The mean of the received signal is given by
1 2 2
2
( 2
)
( )
a P
t
r r p
u E P P d e
(14)
The variance of the received power from primary user is given by:
2 2
2 2
2 ( 4
) 2
d ( 1)
( ) -
a
a P
P
t p
r r P e e
Var P
(15)
Therefore the root mean square is given by
2 2
r r r
1
=u u c
a p
e
(16)
Where
2 2
1
a p
c e
6. Circuits and Systems: An International Journal (CSIJ), Vol.2, No.2/3/4, October 2015
6
When the received signal is from the primary user is given by (3),
( ) -2 -2
v a p
r t p p t p
P P d G P d e
(17)
From the determination criteria from (12) and (13), the probability of false positives can be
calculated as [11]:
( )
( )
FP
r r
r
p pr p u k
(18)
( ) ( )
( (1 ) ) ( (1 ) )
FP
r r
r r
p pr p kc u pr p kc u
(19)
When 1
kc
FP
P
p
p
1 1
p =pr( ln(1+kc))
2 a
P
a
P
p
p
1 1
+pr( ln(1-kc))
2 a
P
a
(20)
FP
1 1
p
2 a p
p =1+Q( ln(1+kc))
a
1 1
p
2 a p
-Q( ln(1-kc))
a
(21)
If 1
kc ,
FP
P
p
p
1 1
p =pr( ln(1+kc))
2 a
P
a
= p
p
1 1
Q( ln(1+kc))
2 a
a
(22)
Where
2
2
2
1
(
x
dx
Q e
Note that PFP only depends on P
and k and independent on Pt, dp and .
On the other hand, when the signal from the attacker,
The Probability of false negative is defined as,
( )
Pr( )
s
FN r
r
P p ur k
(23)
FN
( )
P =Pr(1 kc 1 kc)
s
P
r
ur
(24)
Where
( ) 4
1
M
s
sj
r j
j
P P d G
(25)
Where sj
P the power from the j attacker and dj is is the distance between the j attacker and the
victim, G is the shadowing between each attacker and victim. Since there is no cooperation
between the attackers therefore each attacker wants to conflict the SU with transmitting power
with mean equal to the mean of the primary signal.
Thus
1 2 2 2
( )
2
a p s
sj
sj t
p
P
d
e
d
P
(26)
7. Circuits and Systems: An International Journal (CSIJ), Vol.2, No.2/3/4, October 2015
7
2
2
2 2 2
1 2 2
2
4 1 ( ) 4
2
( )
1
1
( ) j
j
p
p
p s
a s
M a a
t
s M
a
j
r
t
sj
j
j
r
s
d
d
P e e
d
p
e e
u P d
(27)
1 2 2
2
1
(1 1 )
a s j
FN
M
a
j
p pr kc e e kc
(28)
By taking Ln for both sides.
2 2 2 2
1
1 1
( ln(1 ) ln(1 ) )
2 2
FN
M
j
s s
j
p pr a kc a kc a
(29)
2 2 2 2
1
1 1
( ln(1 ) ln(1 ) )
2 2
FN
M
j
s s
j
p pr a kc a kc a
(30)
Where 2
(0, )
j s
N
, the sum of normal variable is also normal with mean zero and
variance equal to j
1
M
T
j
where T
is the normal value with mean equal zero and variance
= 2
s
.
The probability of false negative can be expressed as
FN
1 1
2 2 2 2
2 2
p =pr( a ln(1 kc) ln(1 kc) a )
T
s s
a
(31)
by dividing by . s
M
FN
2 2
.
a a
ln(1 kc) ln(1 kc)
p =pr( )
2 2
.
T
s s
s s
s
M M M M
a a
M
(32)
The probability of false negative as a function of false negative
FN
2 2
. .
a a
ln(1 kc) ln(1 kc)
p =Q( ) Q( )
2 2
s s
s s
M M M M
a a
(33)
When kc≥1
The probability can be given by,
FN
2
.
a
ln(1 kc)
p =pr( )
2
.
T s
s
s M M
a
M
(34)
The final expression of the probability of false negative is given by
FN
2
.
a
ln(1 kc)
p =1 Q( )
2
s
s M M
a
(35)
Note that PFN is depending on ,
p s
and k and M the number of malicious users. It is
independent on Pt, dp, dj and α.
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3.3. Energy Detection Scheme
Figure 3 shows the ED defense scheme, first the system will be initialize by defining the primary
user, secondary user, the attacker and also the channel models that used. These channels are
AWGN and Rayleigh fading. Then the SU performs spectrum sensing to distinguish that the signal
from the primary user or attacker and that based on some threshold that define above in (12), (13).
Fig.3 Flow chart of ED method defense strategies
4. SIMULATION RESULTS
The values of the system simulation parameters are listed in Table 1 [14].
Table 1 SIMULATIONS PARAMETER.
Parameter Value Parameter Value
dp
Pt
2
p
10Km
100KW
8,4
Ro
R1
2
s
30m
500
4,8,12
In this section, The simulations for the proposed models shown if Fig.1 will be validated and the
simulation parameter given in table 1. First, consider a system with fixed primary user at a
distance of dp from the CRN, and transmit power Pt The shadowing random variable from the
primary transmitter is given by equation (1)
The target region of the victim is loop with inner radius Ro and outer radius R1=, the attackers
located at any distance 1
o
R R R
and each attacker transmit with adaptive power Ps that give
by (8) ,the shadowing random variable from the attacker is given by (2) , the victim is using
energy detection method. The Rayleigh fading channel from the primary user to the CRN is
considered to be a two paths channel. The PFA with PD is plotting with the variability of the
threshold value k, we use monte carol with 100000 run times for every value of the threshold
value
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Figure 4 gives the probability density functions (PDFs) of signal power received by the victim
when the primary user and the attacker transmitting under the lognormal channel model. The
attacker applies the advanced strategy that is explained above from this figure we conclude that,
when the number of attackers increase, the PDF of the received signal will differ from that of the
primary user signal and the mean of the received signal is differ, thus the performance detection
increases and the secondary user easy identifies the signal that from PU or from the attacker.
Fig. 4 The PDF's of the received power at the victim from the primary user and the attackers.
Fig.5 gives the relation between the threshold factor (k) and the probability of False alarm (PFA)
that given by (19) when the PUEA method is depend on the energy detection method under
lognormal channel. By varying the value of threshold k value from 0 to 10 the PFA decrease from
1to 0, the threshold factor normally chosen to be around 1.
Fig.5 The impact of k on the probability of false alarm (PFA), when 2 8
p
, 2 4
p
under lognormal
channel
Fig.6 gives the relation between the threshold factor k and the probability of detection (PD) that
give by equation (5), when the PUEA method is depend on the energy detection method under
lognormal at different number of attackers and different variance of the channel. By varying the
threshold factor value k from 0 to 10 the probability of detection decrease from 1 to 0. The
simulation results shows that increase the number of attackers increase the probability of
detection(PD) , also change the variance from 2 4
s
to 2 8
s
decrease the detection
probability.
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Fig.6 The impact of K on the probability of detection (PD) when 2 8
p
.
Fig.7 shows the relation between the threshold factor k and the probability of detection (PD)
when the PUEA method depends on the energy detection method under lognormal and Rayleigh
Fading channel at different number of attackers. The simulation shows that the probability of
detection is increased when the channel is changed from Lognormal to Rayleigh under the same
number of attackers.
Fig.7 The impact of K on the probability of Detection (PD) when 2 8
p
and 2 4
s
under
lognormal and Rayleigh Fading channel.
Fig.8 shows the Receiver operating characteristics (ROC) of the energy detection of one
malicious user at different channel parameters. The analytical results in (16) and (32) match with
the simulation of the proposed model. When 2 2
p s
the performance reduce, otherwise the
performance increase.
Fig.8 Receiver operating characteristics (ROCs) of Energy detection
With
2
8
p
and different
2
s
, M=1.
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Fig.9 shows the ROCs at a different number of attackers with different channel parameters from
the victim to the attacker under the lognormal channel for both the primary user of victim channel
and from the attacker to victim channel. The performance detection increases with increasing the
number of attackers. The performance detection when 2 2
8
p s
has a bad detection at the
same number of attackers. At PFA=0.5 the PD=0.7 and 1 for the number of attacker M=2 and 3, the
probability of detection is increase by 30% when the number of attacker change from 2 to 3.
Fig.9 Receiver operating characteristics (ROCs) of energy detection with different channel parameter under
a different number of attackers
Fig.10 shows the ROCs of energy detection under lognormal and Rayleigh fading from the
primary user to the victim, and lognormal from the attacker to the victim, the performance
detection of the victim gives small PD for the same value of the PFA for the same number of
malicious users therefore the detection is worse for the Rayleigh fading channel. At PFA =0.5 the
probability of detection equal to 0.65 and 0.8 for Rayleigh and Lognormal at M=2 so the
performance decrease by 15% when the channel change from lognormal to Rayleigh fading.
Fig.10 ROCs of energy detection under different number of attackers under lognormal and
Rayleigh fading channel 2
8
p
,
2
4
s
.
5. CONCLUSION
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The PUEA is one of the major threats to the spectrum sensing in CRN, and it was degrading the
performance of the CRN. In this paper, a CRN Network model consists of a primary user,
secondary user and many numbers of attackers. Channel models such as lognormal shadowing
and Rayleigh fading are used. In this model each attacker applies an attack strategy to fool the
victim with emulating the primary user signal. Then the energy detection method is applied to
mitigate the effect of the attackers. The analysis and simulation of the system shows that the
performance of the detection gives worse when changing the channel from lognormal to
Rayleigh fading and get better with the increase number of attackers.
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AUTHORS
Walid Ghanem received the B.Sc. degree in communication engineering from Faculty of
Electronic Engineering, Menoufia University, Egypt, in May 2011, and he is currently
working toward the MSc degree in Electrical communication engineering. His current
research interests are cognitive radio networks, Localization, wireless security, encryption
and optimization algorithms.
Mona Shokair received the B.Sc., and M.Sc. degrees in electronics engineering from
Menoufia University, Menoufia, Egypt, in 1993, and 1997, respectively. She received the
Ph.D. degree from Kyushu University, Japan, in 2005. She received VTS chapter IEEE
award from Japan, in 2003. She published about 70 papers until 2014. She received the
Associated Professor degree in 2011. Presently, she is an Associated Professor at
Menoufia University. Her research interests include adaptive array antennas, CDMA
system, WIMAX system, OFDM system, game theory , next generation networks and
optimization algorithms.
Moawad I. Dessouky received the B.Sc. (Honors) and M.Sc. degrees from the Faculty of
Electronic Engineering, Menoufia University, Menouf, Egypt, in 1976 and 1981,
respectively, and the Ph.D. from McMaster University, Canada, in 1986. He joined the
teaching staff of the Department of Electronics and Electrical Communications, Faculty of
Electronic Engineering, Menoufia University, Menouf, Egypt, in 1986. He has published
more than 200 scientific papers in national and International conference proceedings and
journals. He has received the most cited paper award from Digital Signal Processing
journal for 2008. His current research areas of interest include spectral estimation
techniques, image enhancement, image restoration, super resolution reconstruction of
images, satellite communications, and spread spectrum techniques.