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International Journal on Emerging Trends in Technology (IJETT) ISSN: 2455 - 0124 (ONLINE) | 2350 - 0808 (PRINT) | (IF: 0.456) Volume 3 | Issue 2 | July - 2016 | Special Issue www.ijett.in Defending Primary User Emulation Attack in Cognitive Radio Networks Rajesh D. Kadu1, Dr. Pravin P. Karde2 [email protected], [email protected] Research Scholar, Computer science and Engineering, Information Technology2, SGB Amravati University, Amravati, India1, Government Residential Woman's Polytechnic, Yavatmal, India 3, ABSTRACT In cognitive radio network, an un-licensed user can use a vacant channel in a spectrum band of licensed user. Spectrum sensing is one of the critical function in spectrum management. Cognitive radio is the promising technology to solve the problem of spectrum scarcity in which the secondary users can sense the spectrum and utilize the licensed bands when the spectrum is not being utilized by the primary user. Many malicious secondary users can generate the signals similar to primary transmitter to confuse the good secondary users into thinking that a primary transmission is in progress. The good secondary users then will vacate the spectrum unnecessarily. The malicious users would then use the evacuated white space for themselves. This attack by malicious secondary users is called as primary user emulation attack (PUEA) and it considerably increase the spectrum access failure probability. In this paper we analyze the probability density function (PDF) at both good and malicious users. We also considered the cooperation between the secondary users to detect primary user emulation attack. General Terms Cognitive Radio Networks, Security Keywords Cognitive radio Networks, PUE attack, Probability density function (PDF), malicious user, spectrum sensing . 1. INTRODUCTION Recently it is observed that, the licensed radio spectrum band remains under-utilized [1], [2]. Cognitive radio networks [3] allow the utilization of vacant spectrum in one network by the users belonging to another network. Primary users are licensed users to use the allocated spectrum. Secondary users having no license to use the spectrum can use the spectrum while primary user is not using it. This can improve the spectrum utilization significantly and solve spectrum scarcity problem. These secondary users sense the spectrum to detect empty spectral bands to utilize for communication. While utilizing these empty band, secondary users need to avoid the interference with primary user. If secondary user detects the presence of primary user after sensing the spectral band, it must switch to other band. If any other secondary user is using the same sensed band then spectrum should be shared fairly by using coexistence mechanism. A malicious secondary user may modify the air interface of a cognitive radio (CR) to mimic a primary user signal’s characteristics, thereby causing good secondary user to falsely believe on presence of primary user. Good secondary user then vacate the occupied spectrum band for malicious secondary user believing that it is a primary user. This attack by malicious secondary users in cognitive radio networks is known as Primary User Emulation Attack (PUEA). Primary User Emulation Attack (PUEA) can be categorized as either a selfish PUE attack or a malicious PUE attack depending on intension behind launching it [7] [8]. Selfish PUEA: The aim of an attacker behind this attack is to maximize its own spectrum usage. When selfish PUE attackers sense the empty spectrum band, they start transmitting signals that imitate the signal characteristics of primary user signals. As a result, other secondary users stops from competing for the same spectrum band. Malicious PUEA: The aim of an attacker behind this attack is to block the dynamic spectrum access process of good or legitimate secondary users. This attack thwart legitimate secondary users from detecting and using empty licensed spectrum bands leading to denial of service. Matched filter detection, cyclostationary feature detection, and energy detection are the methods that can be used by good secondary users to sense the presence of the primary user in the spectrum. [4], [5]. Among these methods, the energy based detection is the most common way of spectrum sensing due to its low computational and implementation complexities. Energy based detection can be carried out in both the time and frequency domains. For the matchedfiltering processing, cognitive radio need to have perfect knowledge of the primary users signaling features. These ICSTSD 2016 | 1140 International Journal on Emerging Trends in Technology (IJETT) ISSN: 2455 - 0124 (ONLINE) | 2350 - 0808 (PRINT) | (IF: 0.456) Volume 3 | Issue 2 | July - 2016 | Special Issue www.ijett.in features includes bandwidth, center frequency, modulation type and order, pulse shaping and frame format. The cyclostationary feature detection method can differentiate modulated signals, interference, and noise in low SNR ratios. It can reduce the processing requirements and maintain a decent detection error probability [6]. In [8], two mechanisms have been proposed to detect PUEA. The distance ratio test (DRT) consider the correlation between transmitter receiver distance and the received signal strength. The distance difference test (DDT) is based on signal phase difference. Both DRT and DDT consider the transmitter verification procedure which uses a location verification method to make a distinction between primary signals and secondary signals. Both method may be unsuccessful if the attacker is near to TV tower and transmitting. In [7], author proposed a robust non-interactive localization method as a defense against PUEA. This localization based defense (LocDef) scheme uses received signal strength(RSS) which consider relationship between signal strength and a transmitter location. Transmitter location is calculated by smoothing the collected RSS measurements and obtaining the RSS peaks. Authentication of the primary user's signals using cryptographic and wireless link signatures via a helper node is suggested in [12]. In this approach, a helper node is positioned physically close to a primary user. Secondary user verifies cryptographic signatures carried by the helper node’s signals and then obtain the helper node’s authentic link signatures to verify the primary user’s signals. The network model proposed in [13] assume the presence of single attacker and single defender. Both the attacker and defender can apply estimation techniques and learning methods to obtain the key information of the environment and thus design better strategies. Also demonstration of advanced attack strategy using invariant of communication channels which defeats the naive defense technique focusing only on the received signal power is provided. In [9], proposed localization method use the Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA). TDOA method give motion vector as input to FDOA method, which in turn identify the precise location of the transmitting source. In [10], fingerprinting has been used to authenticate the transmission source. The Fingerprinting approach in [17] operates by erasing the modulation of all received signals to get the carrier with phase noise. Fingerprint of the signal is generated by applying wavelet and higher-order statistics analysis. This fingerprint is used for transmitter identification to secure against PUE attacks. In [11], authors studied the approaches for mitigating the attacks that can influence the spectral environment in the PHY layer of a cognitive radio network. A Bayesian game framework is proposed to analyze and study PUEA [14]. In this framework users are considered to be uncertain regarding the legitimacy of the claimed type of other users. The possibility of a PUEA in cognitive radio networks in a fading wireless environment was studied analytically in [15]. This was the first analytical approach which was based on Fenton’s approximation and Markov’s inequality used to conclude a lower bound on the probability of successful PUEA. A first analytical study of PUEA is given in [16] in the presence of multiple malicious users in fading wireless environments. Neyman-Pearson composite hypothesis test and Wald’s sequential probability ratio test analyzed to detect PUEA in fading wireless channels in the presence of multiple randomly located malicious users. The key idea behind cooperative sensing is to improve the sensing performance by exploiting the spatial diversity in the observations of spatially located CR users. In cooperative spectrum sensing, CR users exchange their sensing information with other users for making a combined decision more precise than the individual decisions [21]. 2. SYSTEM MODEL Consider the system model [16] in figure 1. The distribution of secondary users is within a circular grid of radius R. The primary transmitter is present at a minimum distance d p from all the users. We consider energy based detection mechanisms to detect the presence of the primary user. If the received signal strength is -93 dB m then energy based detection methods conclude the presence of the primary user [19]. There are M malicious users in the system and they transmits at power ‘��’. Primary user transmits at power ‘��’ and �� ≫ ≫ �� . The secondary user is present at the center of the exclusive region. The positions of the secondary and the malicious users are randomly distributed in the circular grid of radius R and their positions are statistically independent of each other. The transmission from primary transmitter and malicious users undergo path loss, log normal shadowing and Rayleigh fading. The Rayleigh fading is assumed to be averaged out and can hence be ignored [16]. Fig 1: A typical cognitive radio network in a circular grid of radius R consisting of good secondary users and malicious secondary users For any secondary user fixed at polar co-ordinates (r 0,θ0), no malicious user are present within a circle of radius centered at (r0,θ0). The path loss exponent chosen for transmission from primary transmitter is 2 and from malicious user are 4. No malicious users are present within a circle of radius ��, called as the exclusive radius from secondary user. There is no cooperation between the secondary users. ¿ 10εp 10 ¿ e Aεp 2 Gp 2 Gp is the shadowing random variable from primary transmitter and A= ln10 10 , �p represents the logarithmic shadowing in the unit of dB with a ICSTSD 2016 | 1141 International Journal on Emerging Trends in Technology (IJETT) ISSN: 2455 - 0124 (ONLINE) | 2350 - 0808 (PRINT) | (IF: 0.456) Volume 3 | Issue 2 | July - 2016 | Special Issue www.ijett.in 2 σp zero mean and variance 2 0, σ p εp N ¿ distribution ¿ e Aεj ). Similarly A= ln10 10 (−π , π ) ∀ j. The received power at the secondary user from the primary transmitter is given by (p) −2 2 Pr =Pt d p G p (2) , �j represents the logarithmic shadowing in the unit of dB with a zero mean and variance σ 2j Otherwise Where θm is uniformly distributed in G2j =10εj10 is the shadowing random variable from malicious user and 0 following a normal following a normal distribution 2 j 0, σ εj N ¿ ). As random variable is log normally distributed and takes only positive real values, the probability density function (PDF) of received powers follows a log- normally distribution. 3. ANALYTICAL MODEL Due to absence of cooperation between the secondary users, probability of PUEA on every user in the networks is same. Hence we analyze the probability function (PDF) of the received signal of one secondary user. Where 2 p G =10 εp 10 0, σ 2p εp N ¿ , ). Since Pt and dp are fixed, the PDF of the received power at the secondary user from the primary transmitter follows a log-normal distribution and can be written as: γ −μ p 10 log 10 ¿ ¿ ¿2 ¿(3) −¿ ¿ 1 (p) exp ¿ Pr ( γ ) = A σ p √ 2 πγ where γ is random variable and A= μ p=10 log 10 pt −20 log 10 pd p ln10 10 and (4) The total received power at the secondary user from all the malicious users is given by: M Pr =∑ Pm d j G j (5) (m ) Fig 2 : Scenario with transformed co-ordinates. The secondary user of interest is at (0,0). Malicious users are uniformly distributed in the annular region (R0,R). The primary is at (dp, θp). Malicious users coordinates are transformed such that the secondary user of concern lies at the origin (i.e., at (0, 0)). The transformed co-ordinates of the primary will then be (dp, θp). As dp >> R and thus it is acceptable to approximate the coordinates of the primary user to be (dp, θp) regardless of which secondary user we consider for the analysis. Figure 2 shows this scenario [16]. 3.1 Probability Density Function of Received Signal probability density function (PDF) of r j, P(rj), given by [16], [18] : 2r j 2 2 P(rj) = R −R0 rj ∈ [R0, R] ∀j is 2 j=1 where dj is the is the distance between the jth malicious user and the secondary user. G2j is the shadowing between the jth malicious user and the secondary user. 0, σ 2j εj N ¿ εj G2j =10 10 , ). Each term in the summation in the right hand of equation (5) is a log-normal distributed random variable of the μ j , σ 2m ). wj N ¿ μ j =10 log 10 pm −40 log 10 pd j (6) form Let M number of malicious users are positioned at coordinates (rj, θj) 1 ≤ j ≤ M. The position of the jth malicious user is uniformly distributed in the annular region between R 0 and R, and rj and θj are statistically independent . The −4 10wj 10 The PDF of and ) P(m r conditioned on the positions of all M malicious user can be written as: (1) ICSTSD 2016 | 1142 International Journal on Emerging Trends in Technology (IJETT) ISSN: 2455 - 0124 (ONLINE) | 2350 - 0808 (PRINT) | (IF: 0.456) Volume 3 | Issue 2 | July - 2016 | Special Issue www.ijett.in x−μ M 10 log 10 ¿ ¿ ¿2 ¿ (7) −¿ ¿ 1 m exp ¿ Pxǀr = A xσ p √ 2 π where r is the vector with elements r1 to rm. The expression for PDF can be written as: x−μ x 10 log 10 ¿ ¿ ¿2 ¿(8) −¿ ¿ 1 m exp ¿ P (x )= A xσ x √2 π If ) P(m r μx and is log normally distributed random variable then σ 2x can be written as: P(rm) ¿ P(rm) E [¿] ¿−2 ln ¿ (9) E¿ ln ¿ 1 2 σ x= 2 ¿ A used, determining signal modulation and estimation of the position of transmitters and receivers. After sensing the environment, the sensing results are used to determine radio settings. Secondary users may experience multipath fading and shadowing as wireless channels are prone to it. Due to this multipath fading and shadowing, secondary users may fail to accurately sense the presence of primary user signals. Consequently, without interference to primary users, spectrum access cannot be possible for secondary users. Collaborative spectrum sensing solve this problem. In collaborative spectrum sensing, sensing results of several secondary users are combined to improve the probability of detecting the primary user. Co-operation among cognitive radio networks is now regarded as a key technology for dealing with the problems in a practical implementation of cognitive radio. The correctness of primary user detection and improvement in performance is possible due to such co-operation among cognitive nodes. This collaboration is achieved by exchanging the information among cognitive radios, carrying out tasks cooperatively, negotiating with peers and considering peer information to conclude about their operating settings. Currently it is assumed that, secondary users are honest in exchanging such information during collaborative spectrum sensing. But some malicious secondary users can report false sensing information about spectrum sensing in order to use vacant spectrum. Although collaborative spectrum sensing in dynamic spectrum access (DSA) seems to be promising approach to improve the utilization of underutilized licensed spectrum bands, it requires the secondary users should not infringe any acceptable interference bounds specified by the primary users. In section 2 and 3, we considered the non-cooperation between secondary users. The detection of PUEA is based on individual secondary user's sensing result. There is no sharing of sensing observations among secondary users. Spectrum sensing based on energy detection method consider the predefined threshold (γ). If the received power at good secondary user is below this threshold then spectrum band is considered to be vacant. Otherwise secondary user concludes that primary user is present. We assume that this spectrum sensing report, good secondary user shares with other secondary users in the network. Based on the reports from other secondary users, good secondary users conclude that spectrum band is vacant or primary user is present. We assume that, good secondary user can detect whether received signals are from malicious users or from primary user. A P(rm) (¿) P(rm) E[¿] ¿2 (10) 1 E[¿]− ln ¿ 2 2 ln ¿ 1 μx= ¿ A threshold ( α ¿ is set to measure the suspicious level of the secondary node. If suspicious level of all secondary nodes is greater than set threshold α then good secondary user concludes that primary user is present. Otherwise good secondary user conclude that primary user emulation attack is launched. 4. COOPERATIVE SPECTRUM SENSING Algorithm: Sensing of the frequency spectrum comprises the tasks like: determination of when and which frequency bands are being 1. ICSTSD 2016 | 1143 Let the predefined threshold is γ. International Journal on Emerging Trends in Technology (IJETT) ISSN: 2455 - 0124 (ONLINE) | 2350 - 0808 (PRINT) | (IF: 0.456) Volume 3 | Issue 2 | July - 2016 | Special Issue www.ijett.in If good secondary user find the received signal above set threshold γ then concludes signal is either from primary user or malicious user. If primary user is present then sensing result is broadcasted to all N secondary users in network. 3. If good secondary user observe the presence of malicious user then received signal is due to PUEA, STOP. Else go to step 4. 4. 6 x 10 8 7 6 5 4 3 2 1 0 0 If calculated suspicious level of each secondary user > threshold α then a) Good secondary user concludes that primary user is present, else b) The good secondary user concludes that malicious user is present and PUEA is launched. 5. SIMULATIONS Figure 3 show the PDF of the received power at the secondary user when the primary transmitter is at distance (d p) 150Km. In this simulation, considered parameter values are, R = 1Km, R0 = 50m, M = 30, P t = 150Kw, Pm = 5w and m=¿ σ¿ p=¿ σ¿ 8dB, simulation computation 9 Good secondary user receive the results from all N secondary users and calculate the suspicious level of each secondary user. 5. 10 PDF of received power 2. 1 2 3 4 5 6 6 Received power at the secondary receiver from malicious users: x 10 Fig 4: PDF of received power at the secondary receiver from malicious users 6. CONCLUTION In this paper, we considered analytical approach to find probability density function (PDF) of received power at the secondary users. This received power at secondary user is considered from malicious users and from the primary transmitter as well. we assumed no co-operation between secondary users. The obtained experimental result of PDF considerably matches with theoretical result. 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