Security in Cognitive Radio Network: Defense against Primary User Emulation attacks using Genetic Artificial Bee Colony (GABC) algorithm

Accepted Manuscript Security in cognitive radio network: Defense against primary user emulation attacks using Genetic Artificial Bee Colony (GABC) algorithm Sally M. Elghamrawy

PII: DOI: Reference:

S0167-739X(17)32124-6 https://doi.org/10.1016/j.future.2018.08.022 FUTURE 4406

To appear in:

Future Generation Computer Systems

Received date : 19 September 2017 Revised date : 13 July 2018 Accepted date : 12 August 2018 Please cite this article as: S.M. Elghamrawy, Security in cognitive radio network: Defense against primary user emulation attacks using Genetic Artificial Bee Colony (GABC) algorithm, Future Generation Computer Systems (2018), https://doi.org/10.1016/j.future.2018.08.022 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Security in Cognitive Radio Network: Defense Against Primary User Emulation Attacks Using Genetic Artificial Bee Colony (GABC) Algorithm Sally M. Elghamrawy MISR Higher Institute for Engineering and Technology, Egypt IEEE Member, Scientific Research Group in Egypt (SRGE) [email protected], [email protected]

Abstract Due to the exponential increase in the number of wireless devices and in data rates demands, the shortage of the free frequency spectrum resources becomes a critical problem. Cognitive Radio Network (CRN) is considered as an emerging technology that eases off the problem of spectrum shortage by optimizing the usage of the available spectrum through giving the unlicensed Secondary Users (SU) the opportunity to exploit the spectrum without causing any interference with the Primary Users’ (PU) usage. Securing the CRN is a crucial problem that must be addressed. Primary User Emulation (PUE) attacks are one of the main threats in deploying CRN, where the Malicious Users mimic the PU signal to confuse the other SUs and corrupt the spectrum sensing process. Many efforts had been made for securing the spectrum sensing process. In this paper, a hybrid Genetic Artificial Bee Colony (GABC) algorithm is proposed to optimize the spectrum utilization by detecting the PUE attacks and enhancing the probability of detection. GABC integrates the Genetic operators with ABC algorithm to reach the balance between exploitation and exploration to find the optimal solution. The simulations results show promising performance of GABC in optimizing spectrum sensing, when compared with recent detection algorithms.

Keywords: Securing Spectrum Sensing, Primary User Emulation PUE, Cognitive radio network (CRN), Genetic Algorithm, ABC algorithm.

1. Introduction Recently, Cognitive Radio Network (CRN) is considered as an effective solution to the spectral shortage problem. Its defined by Federal Communications Commission (FCC) [1] as: “Cognitive radio is a radio or system that senses its operational electromagnetic environment and can dynamically and autonomously adjust its radio operating parameters to modify system operation, such as maximize throughput, mitigate interference, facilitate interoperability, access secondary markets.” The main function of CRN is to exploit the unused spectrum by giving SU the ability to sense the spectrum used by the PU, through the use of spectrum holes, and access the unoccupied channels. This process is termed as the spectrum sensing [2] method. Among all the challenges tackled by the spectrum sensing [3] for cognitive radio network, security is the most crucial challenges that must be addressed. CRN can’t be useful when it is not safe and exhibited to harmful attacks. These attacks can be in form of a Denial of Service (DoS), Objective Function, Spectrum Sensing Data Falsification, misbehavior users’

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attacks [4]. This paper concerned with the Primary User Emulation (PUE) attacks, first introduced in [5], where the Malicious Users (MU) mimic the PU signal to confuse the other SUs and mislead the spectrum sensing process. A. Related Work

Many researches have focused on the detection of PUE attacks based on the location detection or energy detection methods. The location detection method depends only on the location of the PU. In [6], the authors proposed a double-sided neighbor distance (DSND) algorithm for the detection of misbehaving users relying on the distances between the reports of users using Genetic Algorithm GA. A detection technique for primary user emulation attack (PUEA) in the VANET is proposed in [7], based on energy-efficient resource allocation. In addition, the authors in [8] proposed a new location-scheme which detect Received Signal Strength (RSS). The main limitation of location detection methods occurs in the case of the mobile attackers that change their location continuously, this will lead to difficulty in tracing and detection of PUE. On the other hand, some researchers used energy detection methods. Zheng et al [9] proposed an improved energy detection method, with the "hard" fusion OR/AND decision rules to resist the PUE Attacker. In [10], the authors combined energy detection and location verification in order to secure CRN against PUE attacks. In [11], the authors used the received power statistics of the secondary users, as a decision metric to detect the PUEA. Also in [12], a scheme that combines energy detection and localization for PUE attack detection is proposed by detecting the received energy with multiple thresholds. However, the main drawback of energy detection methods is the low efficiency produced in the low Signal to Noise Ratio (SNR) because these methods can’t easily recognize the noise signals. Recently, Ning, et al [13] proposed a sensing method using the power of fingerprints in a multipath Rayleigh fading channel for PUE detection. The SWARM optimization algorithms had been used in many researches for the PUE detection. In [14], the authors proposed a localization method for measuring the arrival time of signals to detect the attacks, using the firefly algorithm. Artificial Bee Colony algorithm is modified, in [15], and proposed an Efficient Adaptive (EA-ABC) algorithm to enhance the spectrum sensing and reduce the probability of false alarms. B. Paper contribution

In this paper, a hybrid Genetic Artificial Bee Colony (GABC) algorithm is proposed to detect PUE attacks and give the licensed SU the ability of accurately detecting the available PU signals. In addition, GABC is used to reduce the impact of false spectrum sensing alarms caused by the Malicious Users MU. The original ABC algorithm suffers from poor exploitation of solutions, while GA uses the crossover and mutation operations to solve this shortage. On the other hand, GA reaches the local optima too quickly due to its lack in exploring the optimized search space. In this context, GABC modified the original ABC algorithm by integrating the GA, crossover and mutation operations, in the onlooker and scout bee phases of the ABC algorithm, in order to promote the search process for the optimal solution. In other words, GABC combines the advantages of the Genetic algorithm GA along with the ABC algorithm to select the more effective genes in order to optimize the spectrum sensing without reaching the local optima caused by applying GA. The contribution of this paper is as follows:  The PUE attacks threat is defined as a model that can be formulated as an optimization problem, its main goal is to find the optimal convenient solution. The PUE problem formulation is discussed in section 2, and the proposed system model is shown section 3.  In the proposed GABC, four cognitive radio parameters are considered for the solution representation: The Signal-to-Noise-Rate, the received signal strength, the Distance Ratio Test, and the signal sensing overhead, that will be described in section 3. 2

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

 

GABC uses two main predefined thresholds which are compared with the received signal energy of SUs, to indicate the presence of PU and PUE, in contrast with conventional spectrum sensing models, that uses only one predefined threshold which can’t detect the PUE attack signal. Section 4 addresses how the proposed GABC algorithm is used to overcome the effect of the PUE attacks to increase the utilization of the free spectrum. The simulation results, in section 5, proves the promising performance of GABC in detecting PUE attacks when compared to recent detection algorithms. GABC achieves high probability of detection and low probability of false alarm ( ). Finally, Section 6 concludes the paper.

2. Problem formulation of the PUE attack in CRN The considered cognitive radio network model is shown in figure 1, suppose that the CRN is composed of a primary base station, a primary user (PU), a number of secondary users (SU), a few of malicious user(MU), and one fusion center (FC). Primary Base station PU

f5

Unoccupied frequency bands Primary Signal PU

CRN

Primary user

…..

f7

SU-1

Report signal

f5

f1 f9

SU-4

Secondary users

FC Fusion center Reports repository

f5

Emulating Signal Malicious users

MU -1 PUE Attackers

MU -3

Figure 1. PUE attacks in Cognitive Radio Network

A scenario of a PUE attack is shown in figure 1. Suppose that a base station is transmitting channels (frequencies) from f.1 to f.9. The PU uses the frequencies f.2, f.3, f.4, f.6 and f.8. Then, the SUs sense the unoccupied frequency bands (f.1, f.5, f.7, f.9) and send signal reports to the FC, that takes these reports and give some decisions about spectrum assignments. However, some of the MU also detect the unoccupied frequency bands and emulate the idle primary signals, this will mislead the SU from detecting the spectrum holes. For example, MU-3 mimic the frequency f-5 to mislead SU-4 from discovering the signal and this will force SU-4 to leave f-5 and announce it as false alarm detection, and look for another free channel. Consequently, MU-3 will obtain the full band of this spectrum which will lead to waste the spectrum opportunity of SU-4, leading to spectrum bandwidth waste. Therefore, the main goal is to reduce the effect of the PUE attacks by: 3

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 Improving the channel sensing of SU to be able to distinguish idle primary signals from the emulating signals.  Improving the decision made by FC of identification of malicious users based on the reports collected from SU. this will lead to increase the utilization of free spectrum and give low probability of false alarm and high probability of detection.

3. The Proposed System Model The network system model, shown in figure 2, composed of K SU, indexed by i ∈ {1, 2, .., K}. Each SU can perform local spectrum sensing independently at time instant to decide the signal condition if it is idle or occupied by a licensed PU not a MU. Then, the K SU send their reports to a FC to give the final decision. 𝑛1

𝑥2 (𝑡)

Local sensing 𝑠2 (𝑡) ℎ𝑃𝑈𝐸2 𝑠𝑘 (𝑡) ℎ𝑃𝑈𝑘

PUEA 𝑠𝑘 (𝑡) ℎ𝑃𝑈𝐸𝑘

𝑛𝑘

SU k Local sensing

𝑌𝑆𝑈1 𝑆𝐸2 𝑛2

𝒁

| • |2

𝑌𝑆𝑈2

𝒕=𝟏

.. . 𝑥𝑘 (𝑡)

𝑛1

| • |2 𝒕=𝟏

𝑛2

SU 2

𝑆𝐸1

𝒁

𝒁

| • |2

𝑆𝐸𝑘

𝑤𝑖

𝑛𝑘

Fusion Center

𝑠1 (𝑡) ℎ𝑃𝑈1

PU

𝑥1 (𝑡)

Local sensing

Collector

SU 1

𝑌𝐹𝐶

Decision

𝑌𝑆𝑈𝑘

𝒕=𝟏

Figure 2. The Schematic representation of CRN with the PUE attack

Assume that each SU in the CRN is equipped with an energy detector to be able to perform local spectrum sensing independently, based on the signals received. Each SU gives a local decision about the condition of the channel. There are four possibilities of the channel condition, depending on the presence or absence of the PU and PUE, as shown: ( )

{

}

(1)

where, ( ) is the signal received by the SU at the time instant. As shown in (1) there are four kinds of the signals received: represents the absence of the PU signal. represents the presence of the PU signal. represents the absence of PU signal with the presence of PUE attack only. Finally, represents the presence of both PU signal and PUE attack. The received signal by SU can be formulated as: () () ()

( ) {

() () ()

() 4

i= 1,2,3… K ( )}

(2)

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Where ( ) is the thermal noise of the signal between any transmitted signal and the SU. This noise is ( ) ( ) are complex additive white Gaussian signal with zero variance and mean { +. the signals transmitted by PU and MU, respectively, at the time instant. It is assumed that ( ) ( ) are distributed Gaussian random variables with zero variance and mean ( ), and they are independent from ( ). is the channel gain between the PU and the SU, while is the channel gain between the MU and the SU. These channel gains deal with the loss and fading of the channels. For each SU, Z samples are determined for each SU to calculate the sum of received signal energy over timing interval, as shown in 3: ∑ = | ( )|2

(3)

where Z is the number of samples in the timing interval, the signal energy detected by SU should give gaussian random variable under the four hypotheses , , and . The Signal -to -Noise ratio (SNR) between the PU and the SU can be calculated, by the energy detector of SU, as shown: (

)

(4)

The received signal strength (RSS) [11] detects the presence of PU’s or PUE signals by measuring the energy signal strength. Each SU is capable of measuring the RSS, as follows: (

)

(5)

( )

Where is the propagation distance between the PU to SU. are the channel gains between PU, PUE and SU, respectively, which accommodate the channel fading and loss that might occurs. The received signal energy of each SU is then transmitted to the FC to give a global decision about presence or absence of the PU signal. (6)

In conventional cooperative spectrum sensing, the SU take its local decision based on one predefined threshold, to give a Hard-binary decision "1" or "0" to the FC, indicating the availability of PU signal. However, its crucial in spectrum sensing process to consider the attacks that might occur in the CRN. In this context, the PUE attacker tries to emulate PU signal and hence one threshold can’t detect a PUE attack signal. There are four possibilities in the proposed system model, , , , , as shown in table 1. The decision sent to FC will consists of two-bit binary decision presenting the four states of the signal. PU 0 1 0

PUEA 0 0 1

1

1

Represent absence of the PU signal presence of the PU signal absence of PU signal with the presence of PUE attack presence of both PU signal and PUE attack

In this paper, there are two main predefined thresholds PUE, respectively:

5

and

Possibilities

Decision sent to FC 00 10 01 11

to indicate the presence of PU and

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{

}

(7)

Where is the received signal energy by the SU. These thresholds provide a higher probability of distinguishing between PU and PUE signals. If is less than threshold and threshold , then SU declares the absence of both PU and PUE and sent “00” to FC. If is less than and bigger than , then it sent “10” to FC that represent the present of PU and absence of PUE too. If is less than and bigger than then it sent “01” to FC that represent the present of PUE only. Finally, if is bigger than and then the SU declares the presence of both PU and PUE. Then, the FC combines the sensing reports collected from all SUs and assigns non-negative weights to them, and calculates the global statistic , as shown: ∑ =1

(8)

where is the weight coefficient assigned to the SU. Then, the FC will make the global decision about the PU spectrum availability and detect the PUE attacks using the proposed GABC algorithm, described in the next section. The main goal in any communication detection system is to maximize the probability of detection and minimize the probability of false alarm and missed detection . The thresholds and are determined based on this concept. The probability of detection of the SU can be defined as: *

The probability of false alarm of the

}= *

|

|

}

(9)

SU can be defined as

*

}= *

|

The probability of miss detection of the

|

} (10)

SU can be defined as *

|

}=

(11)

Where is the FC’s decision of the presence of the PU signal, while is the FC’s decision of the absence of the PU signal. The total probability of error is the probability of the FC to make a wrong decision. The total error probability can be denoted as: *

|

}+

*

|

+

(12)

The effect of changing the thresholds values on probability of error, false alarm and miss detection is tested, and shown in figure 3.

6

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Figure 3: The Threshold effect on the probability of error, false alarm and miss detection

As shown in figure, when the threshold value increases the probability of false alarm decreases. In contrast, probability of miss detection increases along with the threshold.

4. The Proposed Hybrid Genetic Artificial Bee Colony (GABC) Algorithm for PUE Detection In this section, the GABC algorithm for the PUE attack detection will be introduced, GABC algorithm combines the advantages of the GA along with the ABC algorithm, and accommodate the SU in optimal space in the spectrum. GABC select the more effective genes in order to optimize spectrum sensing without reaching the local optima caused by applying GA only. The GABC algorithm is depicted in figure 4. The detection process is performed through six phases, which are; (1) Pre-processing phase, (2) Initial population phase, (3) Employee bee phase, (4) Onlooker bee phase, (5) Scout bee phase and (6) Termination phase.

7

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Scout Bee Phase Check best

Determine Childs(CH)

𝔦𝔱 ( 𝐶𝐻)

New Population repository

Termination Phase >= Iter. Limit

Bit Mutation Check Crossover Rate (CR)

Parent Selection

Calculate 𝒫𝑖( 𝔦𝔱 (𝔾𝑖 ) ) Uniform Crossover

Neighbors Selection NS

Hive

Calculate 𝒫𝑖( 𝔦𝔱 (𝐶𝐻) Solution positions repository

Replace 𝔦𝔱 (𝔾𝑖𝑗 ) Yes 𝔦𝔱(𝑁𝔾𝑖𝑗 ) 𝔦𝔱 (𝔾𝑖𝑗 )

Parent Neighbor Child 1 Child 2 0.81

0.26

0.76

0.42

CR=0.7

Calculate 𝔦𝔱 (𝑁𝔾𝑖𝑗 )

Remain 𝔦𝔱 (𝔾𝑖𝑗 ) No

Update Best solution Yes CRN configured

Employee Bee Phase

New Solution repository

Update solutions

Updated solutions repository

No

Check Mutation Rate (MR)

Employee Bee Phase

Calculate Minimum DRT(NS)

Best Solution Selection

Parent

CRN PU

Mutated solution2 0.01 0.24 0.11 0.02 MR =0.01

FC Genes representation of solutions

𝔾𝟏𝟏

𝔾𝟏𝟐

..

..

𝔾𝒊𝒋

Initial Population Phase

Evaluate Fitness Fun 𝔦𝔱 (𝔾𝑖𝑗 )

Random solutions generation (𝔾𝑖𝑗 )

Share to Hive with Onlooker bee

Chromosome structure converter

)

Sensing period list creator

MU

SU Reports repository

Start

Pre- Processing Phase

Onlooker Bee Phase

Reports extractor

Filter Noise signals RSS calculation DRT calculation

Location Verification DB

Figure 4. The proposed hybrid Genetic Artificial Bee Colony (GABC) algorithm for PUE detection

As a starting point, in the pre- processing phase, the input to the GABC algorithm is the signals extracted from CRN in the form of the reports sent from SUs, so there must be some kind of preprocessing should be performed. The reports sent to the FC is extracted and the noise level is estimated. The noise is filtered based on the Multiple Signal Classification (MUSIC) algorithm proposed in [16]. Each SU uses the energy detection method [17], as mentioned above in equation (5), to calculate the Received Signal Strength (RSS). RSS estimates the location of a signal, which help in defending against PUE attacks by distinguishing between the received signal energy of the PU (using prior information about PU’s location) and the PUE attacker. Then, the Distance Ratio Test (DRT) technique is used to determine the exact location of the PU, by applying a test on the RSS of the signals received and then calculates the reference distance ratio between the signals. DRT uses a location verification database to compare the location of received signals with the locations stored in the database and test its consistency. In the initial population phase, its assumed that the SU senses the spectrum during a sensing time. In order to reduce the sensing overhead, its paramount important to determine the sensing time and sensing period. Sensing overhead rate can be denoted as: ∑ =1 (13) Where is the sensing time, which is the time interval between two sequential sensing processes, and is sensing period, which means the time taken by SU to determine RSS for a signal. F is the number of signals. Genetic algorithm GA, used in GABC, depends on developing a number of solutions, represented by the chromosomes, over the sensing period determined. The convenient representation of the chromosome will help searching for the optimal solution. The chromosome structure converter is used 8

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to generate the appropriate genes (parameters) to form the chromosome, as shown in figure 4. A number of chromosomes formulate a population which consists of population size ( ). Then these chromosomes are converted into possible solutions for a given fitness function. The chromosome with the smallest fitness value will be replaced with the largest fitness value. In GABC, the original ABC algorithm [18] is modified and combined with GA to optimize the accuracy and the probability of detecting PU spectrum. There are three types of bees in the ABC algorithm [19]: Employed bees, onlooker bees, and scout bees. The main goal of all bees is to maximize the nectar amount of a food source. The employed bees exploit food sources. A random solution generation is applied of size ( ), the population size is determined based on the number of SU. These solutions are considered as the food sources of the GABC algorithm, as shown below:

Number of solutions

Number of Genes/ solution j = 1 . . . . . . 𝑔𝑛 i =1 . . . . 𝒫𝑜𝑝𝒮

𝔾𝟏𝟏

𝔾𝟏𝟐

𝔾𝟐𝟏

𝔾𝟐𝟐

𝔾𝒫𝑜𝑝𝒮

Each solution is represented as group of genes, defined as , where i = 1,2,3,…. . Each solution contains number of genes, where j = 1, 2, 3 …. . A number of random solutions are initially generated, as denoted: . ( ) (14) Where is a uniform random number in the range [0,1], and and are the upper and lower bounds of the gene, respectively, for the dimension j. After the initial generation of the random solution, the GABC begins the search for the optimal solution through some iterations. Each iteration of the search process consists of three phases: The employee, onlooker, and scout phase. The maximum number of iterations ( ) is predefined from the beginning, and when its reached the search process will terminated. Then, the best solution is chosen which represent the highest fitness solution obtained. In the Employee Bee phase, the employee bees search in the random solutions and generate new food source , as follows: (

)

(15)

Where is a random number in the range [-1,1], Q is a random number in the range [1, ]. Each signal (solution) is identified by number of genes(parameters). In the proposed GABC, four cognitive radio parameters are considered for the solution representation: The Signal-to-NoiseRate , the received signal strength , the Distance Ratio Test DRT, and the signal sensing overhead , described in previous section. The fitness value for each gene is calculated as shown: (

)

∑ =1

(16)

These fitness values are assigned with different weights. Then, the fitness function is evaluated for each solution, as denoted: 9

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(

)

∑ =1 ∑

(

=1

)

(17)

The number of employed bees is equal to the number of solutions. Then, the employed bees share the fitness value calculated (nectar of food sources) to the hive with the onlooker bees. The employee bees return to the original solutions and choose new food sources, and then evaluate their new fitness values ( ). Then, these new fitness values are compared with the previous one. If the new values are greater than old ones, then the employee bees replace the old values with the new values of food sources, otherwise the old food sources are retained, as shown in figure 5. Employee bee phase Inputs: 𝓟𝒐𝒑𝓢: number of solution: population size 𝒈𝒏 : number of genes 𝑀𝑎𝑥𝑖𝑡 : Maximum number of iterations

13. 14. 15. 16.

1. Start: 2. Do 3. Iter =iter + 1; 4. Initial Population= {𝔾11 , 𝔾12 𝔾13 … 𝔾𝒫𝑜𝑝𝒮 𝑔𝑛 } 5. For I =1 to 𝒫𝑜𝑝𝒮 6. For j =1 to 𝑔𝑛 7. Calculate Fitness Fun 𝔦𝔱 (𝔾𝑖𝑗 ) 8. Share to Hive with Onlooker bee 9. End For 10. End for 11. Return to Population 12. Generate 𝑁𝔾𝒊𝒋 𝔾𝒊𝒋 𝜶𝒊𝒋 (𝔾𝒊𝒋 𝔾𝑸𝒋 )

17. 18. 19. 20. 21. 22. 23. 24. 25. 26.

For I =1 to 𝒫𝑜𝑝𝒮 For j =1 to 𝑔𝑛 Calculate new 𝔦𝔱 (𝑁𝔾𝑖𝑗 ) 𝒊𝒇 𝔦𝔱(𝑁𝔾𝑖𝑗 ) 𝔦𝔱 (𝔾𝑖𝑗 ) then Replace 𝔦𝔱 (𝔾𝑖𝑗 ) Update solutions Else Remain 𝔦𝔱 (𝔾𝑖𝑗 ) End For End for Update solutions Solution positions repository While (iter <= max iteration 𝑀𝑎𝑥𝑖𝑡 ) End

Figure 5. The Pseudo code for the employee bees process

In Onlooker Bee phase, the crossover operation of GA is applied to share knowledge between employee and onlooker bees in the hive to select the neighbor solution. Unlike the original ABC algorithm, which randomly selects any employee bee as a neighbor. The crossover operation is implemented between the parent solution , which is the solution with highest fitness values obtained from equation (17), and the neighbor solution , which is randomly selected by the Neighbors Selection NS module using the roulette wheel selection [20]. The onlooker bees calculate the probability value for each solution had been shared on hive, based on this equation: (

∑

)

(

)

(18)

( ) is the fitness of the solution Where . Based on the calculated probabilities for all solutions, the highest probability gained by a solution is chosen to be exploited, and will be the neighbor solution, as depicted in figure 4. First, a Crossover Rate (CR) is set as a predefined parameter for the GABC algorithm. The parent solution is combined with the neighbor solution to produce one or more child, based on the value of CR. In addition, the minimum DRT is calculated to the NS, in order to determine the closest solution to the parent and minimize the wide variances of solution that might be generated. Then, the crossover is applied to each gene of the chromosome of parent and NS solutions. A string ( ) with the same number of parent’s gene is randomly created containing random values from [0,1], as shown in figure 4. Each gene value in this string is compared to CR’s value. If it’s value is less than the CR, then the same gene of neighbor solution is assigned to child1 while child2 takes the gene of parent. Otherwise, child1 takes the gene of parent and child2 takes the neighbor’ gene, as shown in the pseudo code in figure 6.

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Onlooker bee phase Inputs: 𝑪𝑹: The Crossover Rate =0.7 𝒈𝒏 : number of genes 𝑵𝔾𝒊𝒋 : new generated Solutions= {𝔾11 , 𝔾12 𝔾13 … 𝔾𝒫𝑜𝑝𝒮 𝑔𝑛 } 1. Start: 2. For each onlooker bee 3. Select Parent solution PS = Maximum ( 𝔦𝔱 (𝔾𝑖 )) 𝒫𝑜𝑝𝒮 4. Calculate 𝒫𝑖 𝔦𝔱 (𝔾𝑖 )/ ∑𝑖=1 𝔦𝔱 (𝔾𝑖 ) 5. Select Neighbor solution NS = Maximum (𝒫𝑖 ) 6. For each 𝔾𝒋 in solution 𝑵𝔾𝒊𝒋 //perform crossover 7. RandomSelect ( 𝒮𝔱 ∈ , -) 8. IF Str >= 𝑪𝑹 THEN 9. 𝐶ℎ𝑖𝑙𝑑 𝑖𝑗 = 𝑃𝑆𝑖𝑗 10. 𝐶ℎ𝑖𝑙𝑑2𝑖𝑗 j = 𝑁𝑆𝑖𝑗 11. ELSE 12. 𝐶ℎ𝑖𝑙𝑑 𝑖𝑗 = 𝑁𝑆𝑖𝑗 13. 𝐶ℎ𝑖𝑙𝑑2𝑖𝑗 j = 𝑃𝑆𝑖𝑗 14. End If 15. End For 16. End for 17. End Figure 6. The Pseudo code for the Onlooker Bees Phase

As mentioned before, the original ABC algorithm suffers from exploitation problem, while GA suffers from exploration problem. In the proposed GABC, the adaptation between the two algorithms helps to overcome these shortages. In order to keep the balance between the exploitation and exploration of solutions, the mutation operation of GA will be applied in the Scout Bees Phase. In the basic scout bee phase, the employee bee is converted into scout bee if the fitness value of a solution is not improving for a predefined number of iteration, and this will lead to remaining on the local optimizer with no enhancing in solutions. However, in GABC the Scout bee’s process is modified by mutation operation to replace the solution that is not improving. As shown in figure 4, the fitness value is calculated for child solutions, generated in onlooker phase, and then a selection for the best solution is applied by comparing their fitness values. If the fitness value of (child1) greater than the fitness value of (child2) then child1 solution will be appended to the new solution repository, otherwise the child2 will be appended. Then a comparison will be made between the old and new solution generated to determine the parent solution using the mutation operation. Also, the scout bee generates new solutions under the constraints of the mutation operation, as follows: A Mutation Rate (MR) is set as a predefined parameter for the GABC algorithm, as shown in figure 7. If the random solution gene is below the MR value, then the scout bee mutates each gene in the parent solution based on this equation: ( ) (19) Where is the new solution generated by scout bee, is the parent solution, is a random number in the range [-1,1]. is the random solution selected from the food sources generated by employee and onlooker phase, equation (15). 0.71

0.33

0.94

0.55

0.33

0.94

0.55

0.01

0.22

0.89

0.32

0.28

0.22

0.89

0.28

0.32

0.01

0.13

𝑵𝔾𝒊𝒋

𝑅𝑆𝑖𝑗 11

𝑃𝑆𝑖𝑗 𝒮𝑐𝐵𝑒𝑖𝑗

MR = 0.01 If 𝑅𝑆𝑖𝑗 <= 0.01 then Mutate (𝑃𝑆𝑖𝑗 )

Sally Elghamrawy Figure 7: The mutation process in scout bee phase of GABC

In the termination Phase, the terminate criterion is evaluated, as follows: After the scout bee finish its process, the index of the current iteration is checked. If the index of the current iteration reached the predefined limit ( ), then the new generated solutions are chosen, which represent the highest fitness solutions obtained. Then the database will be updated and the CRN will be configured with these solutions. Otherwise, the GABC process is repeated, by implementing the employee phase again.

5. Experimental Evaluation A number of simulated experiments were performed to validate the effectiveness of the proposed GABC algorithm. Some parameters are set for the CRN, as shown in table 2. Table 2: The parameters of the GABC algorithm

Parameter

Value

Parameter

Value

Number of generations(cycle)

300

Signals to Noise Ratio

From -20 to l0 dB

Population size Maximum number of iterations Number of genes/solution Number of SU (K)

100 10 4 10

Crossover rate CR Mutation Rate MR The sensing time Number of MU

0.7 0.01 1 MS 3

Experiment one: The GABC’s probability of detection ( ), miss detection ( ), and error( ) are measured, based on equation (9), (11), (12), respectively, when varying the Signal to Noise Ratio ( ). The results obtained are compared to DSND [6] and AATS-EGC [21] algorithms results, to evaluate the performance of GABC in detecting the PU signals, as shown in figure 8. It is noticed in figure 8(a) that GABC introduced significant improvements in detecting the PU signals over the other techniques. The GABC reduces the probability of miss detection when comparing with to DSND [5] and AATS-EGC [20] algorithms in different SNRs, as shown in figure 8(b). Figure 8(c) shows that he probability of the total error ( ) of GABC algorithm is below the DSND and AATS-EGC at the same values of SNR.

Figure 8(a): The vs Signal to Noise

Probability of Detection Ratio

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Sally Elghamrawy

Figure 8(b): The Probability of Miss Detection vs Signal to Noise Ratio

Figure 8(c): The Probability of error vs Signal to Noise Ratio Figure 8: Performance comparison between GABC, DSND and AATS-EGC in terms of Probability of error, detections, miss detection

Experiment two: Compares the performance of the original ABC algorithm [18] and the Efficient Adaptive (EA-ABC) algorithm [15] with the proposed GABC algorithm in detecting the PU signal at different generations number, as shown in figure 9.

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Sally Elghamrawy

Figure 9: Probability of detections of GABC, ABC and EA-ABC with varying number of generation

Its noticed from figure 9 that the GABC algorithm presented substantial improvements in the probability of detection when compared with ABC and EA-ABC algorithms, at every generation. Moreover, the performance of the detection is enhanced, with the increasing of generations. Experiment three: The performance of the proposed GABC algorithm is compared, as shown in figure 10, with recent algorithms: ABC [18], EA-ABC [15], AATS-EGC [21] and DSND [6]. Figure 10(a) compares the probability of detection versus the probability of false alarms for the 10 SU in SNR of -5 db. Also, figure 10(b) shows the ROC curve of the different algorithms that compares the probability of miss detection with the probability of false alarms. GABC

1.2

DSND

AATS-EGC

EA-ABC

ABC

Propability of detection Pd

10 0.8 0.6 1

0.4 0.2 02

2

0

1 4 8 Propability of False Alarms Pf

0

12

Probability Of Miss Detection Pm

Figure 10(a): GABC probability of detection vs probability of false alarms

GABC

6

DSND

AATS-EGC

EA-ABC

ABC

0

5 4 31 2 1 2

0 0 . 0 1 20 . 0 8

0.1

0.2

0.3

0.4 1 0.5

0.6

0.7

0.8

0.9

10

Probability Of False Alarm Pf Figure 10(b): GABC probability of miss detection vs probability of false alarms

It is shown that GABC gives better performance in signals detection than other state-of-the-art algorithms. This experiment proves the promising performance of GABC in detecting the PUE attacks 14

Sally Elghamrawy

when compared to recent detection algorithms. GABC achieves high probability of detection ( ) and low probability of miss detection ( ) and false alarms ( ). The experiments results show that the performance of the proposed GABC is better than the algorithms developed in [6,21,15,18]. There are two reasons for this: First, combining the GA with ABC algorithm achieves the balance between exploitation and exploration of solution, this lead to the optimization of the spectrum sensing without reaching the local optima caused by applying GA. Second, using two main predefined thresholds ( and ) in comparing signal energy received from SUs, improves the detection probability that indicates the presence of PU and PUE, in contrast with most recent proposed algorithms, that uses only one predefined threshold which can’t detect the PUE attack signals.

6. Conclusions and Future Work The spectrum sensing in CRN was studied in the existence of malicious users that emulate the primary user signals. These Primary User Emulation (PUE) attacks diminishes the effectiveness of CRN performance. A new Genetic Artificial Bee Colony (GABC) algorithm is proposed to optimize the spectrum utilization in CRN, by distinguishing between the PU and PUE signals. GABC combines the advantages of the GA along with the ABC algorithm to optimize the spectrum sensing without reaching the local optima caused by applying GA. GABC uses two main predefined thresholds to indicate the presence of PU and PUE. In addition, four cognitive radio parameters are considered for the solution representation. The simulations results showed that the GABC is more robust to PUE attacks, due to its ability to provide high probability of detection compared to the other convenient detection methods and achieves low probability of miss detection and false alarms, in different environments. As a future work, a hardware deployment experiments will be considered for validating the simulated results obtained in this paper. In addition, an energy efficient solution will be studied as a future work for cooperative sensing in cognitive radio sensor networks. REFERENCES [1]

Powell, K. M., J. K. Martin, and S. J. Adelstein. Notice of proposed rulemaking and order: Facilitating opportunities for flexible efficient and reliable spectrum use employing cognitive radio technologies. Federal Communications Commission, Notice of Proposed Rule Making and Order (2003). [2] Yucek, Tevfik, and Huseyin Arslan. A survey of spectrum sensing algorithms for cognitive radio applications. IEEE communications surveys & tutorials 11.1 (2009): 116-130. [3] Kieu-Xuan, Thuc, and Insoo Koo. A cooperative spectrum sensing scheme using adaptive fuzzy system for cognitive radio networks. Information Sciences 220 (2013): 102-109. [4] G. Sharma and R. Sharma. A Review on n Recent Advances in S Spectrum Sensing, Energy Efficiency and Security y Threats in Cognitive Radio Network, (2015) 114–117. [5] R. Chen, J.-M. Park, Ensuring Trustworthy Spectrum Sensing in Cognitive Radio Networks, Netw. Technol. Softw. Defin. Radio Networks, 2006. SDR ’06.1st IEEE Work. (2006) 110–119. [6] N. Gul, A. Naveed, A Combination of Double Sided Neighbor Distance And Genetic Algorithm in Cooperative Spectrum Sensing Against Malicious Users, (2017) 746–753. [7] D. Das, S. Das, Adaptive resource allocation scheme for cognitive radio vehicular ad-hoc network in the presence of primary user emulation attack, IET Networks. 6 (2017) 5–13. doi:10.1049/iet-net.2016.0033. [8] uan, Z., Niyato, D., Li, H., & Han, Z . Defense against primary user emulation attacks using belief propagation of location information in cognitive radio networks. Wireless Communications and Networking Conference (WCNC), 2011 IEEE. IEEE, 2011. [9] Y. Zheng, Y. Chen, C. Xing, J. Chen, T. Zheng, A Scheme Against Primary User Emulation Attack Based on Improved Energy Detection, (2016) 2056–2060. [10] R. Yu, Y. Zhang, Y. Liu, S. Gjessing, M. Guizani, Securing Cognitive Radio Networks against Primary User Emulation Attacks, IEEE Netw. 30 (2016) 62–69. [11] Ghaznavi, Mahsa, and Ali Jamshidi. "Defence against Primary User Emulation Attack Using Statistical Properties of the Cognitive Radio Received Power." IET Communications (2017). 15

Sally Elghamrawy [12] F. Jin, V. Varadharajan, U. Tupakula, Improved Detection of Primary User Emulation Attacks in Cognitive Radio Networks, (2015) 274–279. [13] I.C. Letters, N. Gao, Robust Collaborative Spectrum Sensing Using PHY-Layer Fingerprints in Mobile Cognitive Radio Networks, (2017). 1063-1066. [14] [M. Ghanem, Walid; Shokair, Mona; Desouky, An improved Primary User Emulation Attack Detection in Cognitive Radio Networks Based on Firefly Optimization Algorithm, (2016) 178–187. [15] X.. Li, L.. Lu, L.. Liu, G.. Li, X.. Guan, Cooperative spectrum sensing based on an efficient adaptive artificial bee colony algorithm, Soft Comput. 19 (2014) 597–607. [16] Ciuonzo, Domenico, Gianmarco Romano, and Raffaele Solimene. "Performance analysis of time-reversal MUSIC." IEEE Transactions on Signal Processing 63.10 (2015): 2650-2662. [17] O. Fatemieh, A. Farhadi, R. Chandra, C.A. Gunter, Using Classification to Protect the Integrity of Spectrum Measurements in White Space Networks, (n.d.) , 2011. [18] Xiang, Wan-Li, and Mei-Qing An. "An efficient and robust artificial bee colony algorithm for numerical optimization." Computers & Operations Research 40.5 (2013): 1256-1265. [19] Li, Guoqiang, Peifeng Niu, and Xingjun Xiao. "Development and investigation of efficient artificial bee colony algorithm for numerical function optimization." Applied soft computing 12.1 (2012): 320-332. [20] Colin, R. Reeves, and E. R. Jonathan. Genetic algorithms-Principles and perspectives, A guide to GA Theory." USA: Kluwer Academic Publisher 19 (2002): 60. [21] Sharifi, Abbas Ali, Morteza Sharifi, and Mir Javad Musevi Niya. Secure cooperative spectrum sensing under primary user emulation attack in cognitive radio networks: Attack-aware threshold selection approach. AEU-International Journal of Electronics and Communications 70.1 (2016): 95-104.

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  Sally  M.  El‐G Ghamrawy is  an  Assisstant  Professsor  at  the  MISR  Engineering  &  nstitute  and d  part  time  Assistant  Professor  at  ccomputers  eengineering  Technology  in department  ‐‐Faculty  of  engineering,,  Mansoura  University  in  Egypt.  From  same  d  a  Ph.D.  deegree  in  201 12  in  Distributed  Decisio on  Support  department,  she  received ntelligent Aggents and recceived a M. SSC degree in  Automatic  Syystems Based on Multi In C Control Syste ms Engineerring in 2006,,  and receiveed B. Sc. in C Computers EEngineering  and  Systems  in  2003.  SShe  deliverin ng  lectures,  supervisingg  graduation n  projects,  master'ss  thesis, and  doctoral disssertations. SShe  was  delivering lecturres and  gavee a  practical  training  in  the  gran nts  from  the   Ministry  of  Communicaations  and  in nformation  ttechnology  w with  collaborration  with  IBM.  Shee  received  a  certificate  A A+  Internatio onal  Inc.  Com mpTIA.  She  is  a  memberr  in  Scientificc  Research  Group in n Egypt. Her  research foccuses on Big  Data analysis, No‐SQL databases, Haadoop and M MapReduce  techniqu ues,  and  sofftware  engineering.  Shee  is  the  au uthor  of  num mber  peer‐rreviewed  pu ublications,  receivingg best paper awards. Shee is also an IEEE Member. 

Highlights • • • • •

A Genetic Artificial Bee Colony (GABC) algorithm is proposed for spectrum optimization. GABC secures the Cognitive Networks from the Primary User Emulation (PUE) attacks. GABC enhances the probability of detecting using the genetic and ABC algorithms. The simulations results showed that the GABC is robust to PUE attacks. GABC achieves low probability of miss detection in different environments.