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The different realistic propagation channels are faced frequently the multipath fading environments. The main goal of this system design (cognitive radio network) is to improve the efficiency of spectrum access on a non-interfering basis. This system achieves high utilization for the limited spectrum in order to fulfill needs for all users’ demands which are considered as a problem in wireless communications due to rapidly increasing in wireless applications and service. This system is exposed to attack due to the vulnerabilities existence in this system. So, the main outcome of this paper is to investigate the performance of the cooperative sensing in cognitive radio networks under malicious attacks over different channel impairments, and to illustrate the most suitable individual probability of detection in real faded channel by using Nakagami model. This paper illustrates the effectiveness of the attacks and fading on the performance of spectrum sensing process.

Cognitive Radio Networks (CRNs) are considered as promising technologies that utilize the unused spectra to enable much higher spectrum efficiency. An example of CRN is the usage of free spectrum (white spaces) in the television band where the television transmitter is considered as a primary transmitter, the television receivers are considered as primary receivers and the secondary transmitters receivers are considered as the users who are not television subscribers but need to use the free spectrum in the television band for their own traffic. Where the secondary (unlicensed) users are utilize the licensed frequencies while the primary user (licensed) is absent. An introduction for this system is illustrated in [_{f}) in order to reduce attacking risks and illustrate that for a ﬁxed percentage of malicious users, the detection accuracy increases almost exponentially as the network size increases. The system of energy detection of unknown signal over different faded channels which the energy detection has been widely applied, then a closed form expression for the probability of detection (P_{d}) is presented in [

The rest of the paper is organized as follows. Section 2 illustrates the detection and false alarm probabilities of non-fading additive white Gaussian noise (AWGN). Section 3 discusses the average detection probability over faded channel. The cooperative spectrum sensing is presented in Section 4. The cooperative spectrum sensing with malicious attack is discussed in Section 5. The results and analysis are presented in Section 6. Finally, the paper is concluded in Section 7.

Closed form expressions for both the individual probability of detection P_{d} and the individual probability of false alarm P_{f} over AWGN channels were presented in [

where

Y = received signal strength.

H_{1} = hypothesis that the primary signal successfully received.

H_{0} = hypothesis that the primary signal not successfully received.

P_{d} and P_{f} may be evaluated as follow in conjunction with [

where u = TW: time bandwidth product, _{u} is the generalized Marcum’s Q_{u} function, Γ (,) is the incomplete gamma function [

The average detection probability is derived over Nakagami faded channels. The presented expressions are in a closed form by averaging the P_{d} in the AWGN over the signal to noise ratio (SNR) fading distribution following the criteria that was presented in [_{f} will not be affected heavily according to the different conditions. So, P_{f} will be approximately considered as the case of AWGN and this is in consistence with the presented work in [

If the amplitude of the received signal (after the channel impairments effect and noise existence) follow a Nakagami distribution, then the PDF of γ follows a gamma PDF which was mentioned [

where m: is the Nakagami fading parameter and _{d} in the case of Nakagami channels

where _{1}F_{1} (; ;) is the conﬂuent hypergeometric function and where

Hence,

where L_{v} () is the Laguerre polynomial of degree v [

In order to improve the performance of spectrum sensing, different secondary users cooperate by sharing their sensing information (local observation) as shown in _{0} or H_{1}) to the central base station. If a free spectrum was detected, it reports “0” (primary user not present) otherwise “1”. The base station collects all the reports and makes the final decision statistics by using different techniques as was mentioned in [

For simplicity, assume that all users have the same independent and identically distribution (i.i.d) fading condition with the same average signal to noise ratio (_{f} and an individual probability of detection P_{d}.

In the previous section, it was assumed that all users in the system are benign. There are number of malicious users sending false reports (sensing information) to the base station. Assume that there are k malicious users in the system and the base station was used the q-out-of-n rule for make a decision. In the worst case, all malicious users report “1” when the spectrum is actually unoccupied.

In the generalized sensing strategy, assume that the network consists of n active users including k malicious users. First, assume that malicious users can detect the primary signal with no errors and always report false information. Each node in the network performs spectrum sensing and reports its one-bit hard decision result to a base station (fusion center) through a control channel. The control channel is assumed to be error free (P_{e} = 0). The sensing result is either “1” which means that the primary user is present, or “0” which means that the band is not used by the primary. The fusion center is then responsible for making the ﬁnal decision based on the received sensing reports from all users. In the q-out-of-m_{s} scheme, the fusion center randomly polls m_{s} out of n users and relies on q-out-of-m_{s} rule for ﬁnal decision making (the fusion center decides that a primary is present if q or more out of the m_{s} polled users report “1”), then at least there should be q users reporting the presence of the primary signal in order to be able to detect it. The number of malicious users d = max (0, m_{s} + k − n) indicates that when the number of users being polled, m_{s}, is greater than that of the benign users, then there are at least m_{s} − (n − k) copies of malicious reports received by the fusion center. The main objective is to minimize the overall false alarm rate (Q_{f}) while keeping the overall miss-detection (Q_{m}) below a certain predeﬁned value quality of service (β). as was mentioned in [_{f} and the probability of detection Q_{d} under two cases: 1) the sensing information that was sent correctly to the base station during a channel free of error, and 2) the sensing information was sent with error due to the channel impairments.

The malicious users always report false information. So, the attacking scenarios may be described as follows.

The first scenarios, part of these malicious users (d users) are considered as member of decision making group (q users). In this part the miss-detection probability (Q_{m} = 1 − Q_{d}) while depend only upon the malicious effect on the descion making group (q), by assuming that the channel is configure to be perfect one. So, any miss-de- tection or miss-orientation in decision making is resulting from the malicious sub-group (d) which deviate the decision making group (q). Then, the overall probability of detection Q_{d} under the generalized sensing strategy was mentioned in [

where

_{s} − d out of n ? k benign users and d out of k malicious users.

For more reality, the channel is not perfect all over the time due to the unstable channel characteristics of its behavior as the wireless media. In order to represent this phenomenon, the channel will be modeled as Nakagamifunction which is presented in Equation (9). Then, the miss-detection probability in this case will be due to the conjunction effect between the channel imperfection as well as the malicious attackers. The presented work in this paper is focusing on this manner. So, the miss-detection probability may be represented by the collaboration or coexistence of the miss-detection due to malicious attack and the miss-detection due to the channel impairment this is represented by Equation (9), which in consistence with previously mentioned work in [

A validation of the Nakagami model had been mentioned in section 3 as illustrated in

In this part the first objective is to define the effective region of the channel impairments on the overall detection probability. So,

_{d} < 60%) the channel impairments are the dominant part. On the other hand, (60% < P_{d} < 100%) the malicious attacks are the dominant. So, the current work may be used to help the other researchers to use the operational parameters that P_{d} more than 60% and then can predict the probability of detection in spite of the channel conditions.

For further investigation, the current work is aiming to determine the most effective number of the decision making group (q_{o}), which is the semi optimized number of the decision making users that will bear on.

As was illustrated in paper [_{opt}, m_{opt}) = (14, 29), at which (Q_{f},_{ }Q_{m}) = (0.00028, 0.0088). And this was proved in [_{s} is equal to or very close to the network size n

which almost independent on the percentage of malicious users, and the optimum q verses n follows an approximate linear function of n with different slopes depending on the percentage of malicious users. So, for network size n = 30 and the percentage of malicious users is 13%. Then the optimal q (i.e. q_{o}) is equal to 15, and at percentage of malicious users 20% the optimal q (i.e. q_{o}) is equal to 14, and at percentage of malicious users 27% the optimal q (i.e. q_{o}) is equal to 12. This illustrated that to improve the performance of system is not by increasing randomly q but there is a relation to be investigated in order to achieve the most probable optimal value of q. The main objective in cognitive radio is to minimize the overall false alarm rate (Q_{f}) while keeping the overall miss-detection (Q_{m}) below certain predeﬁned value β (QoS).

This paper performs a reduction for operation parameter to get the suitable probability of detection (P_{d} = 0.775) against malicious attack in real faded channel using Nakagami model. The main contribution of this paper is to illustrate the suitable P_{d} that may be taken for malicious attack model and that give two benefits: the first benefits is making verification for proposed value of P_{d} mentioned in [

So, as illustrated in Section 6, this paper proposed that P_{d} must be taken as (P_{d} = 0.775) and this in consistent with the previous published work in [_{d} more than 60% the system will behave most likely as AWGN (case of high P_{d}) and more immunity for malicious attacks.

Further investigations are required to investigate the traffic characteristics with the effect of the malicious attacks on the obtained performance.

Hagar O. Shazly,Asmaa Saafan,Hesham El Badawy,Hadia M. El Hennawy, (2016) Performance of Analysis Cognitive Radio with Cooperative Sensing under Malicious Attacks over Nakagami Faded Channels. Wireless Engineering and Technology,07,67-74. doi: 10.4236/wet.2016.72007