Survey: Various Attacks on Cognitive Radio

IJSRD - International Journal for Scientific Research & Development| Vol. 4, Issue 05, 2016 | ISSN (online): 2321-0613

Survey: Various Attacks on Cognitive Radio Sadiya Khanam1 Amandeep Kaur2 Department of Information Technology 1,2 Chandigarh Engineering College, Landran, Mohali, Punjab, India 1,2

Abstract— Cognitive radio seemed as a prominent technology that improves spectrum efficiency. Due to speedy growth of wireless technology, there is a growing demand for radio spectrum. Cognitive Radio provides a solution to the spectrum shortage problem by allowing secondary user to use the spectrum when the spectrum is vacant. Spectrum sensing detects the unutilized spectrum or white spaces to know if the licensed user is present or absent. The interference can occur to licensed spectrum if there is inefficiency in spectrum sensing. To make network robust and less harmed, it is necessary to provide security of data transmission over these networks against the various types of attacks. This survey paper has presented different types of attack.

Key words: Cognitive radio, CR network, White spaces, Spectrum Sensing I. INTRODUCTION Cognitive radio is an emerging and intelligent radio technology [1] that detects the vacant channels in a wireless spectrum. There are two users [2] in wireless network viz. primary user and secondary user. The primary users are the one that has privilege to use the band and are also called as licensed user and the other is secondary user that can occupy the vacant band only when the licensed user is not using that band. The secondary users are also called as unlicensed user.

Cognitive radio technology is done by using spectrum sensing method. Spectrum sensing senses the entire underutilized spectrum. Theses under-utilized spectrums are also called as white spaces [3] or spectrum holes. Whenever the primary users are absent and the spectrum is available, the secondary user will immediately occupy that channel. And when primary user is back in cognitive radio network, the unlicensed user has to free the band to the licensed user.

Types Attacks

of

Physical layer Attacks Primary User Emulation Attack (PUEA)

Objective Function Attack

Jamming Attack

Link Layer Attacks Spectrum Sensing Data Falsification

Fig. 1: Cognitive Radio Network II. ATTACKS IN CR NETWORK Different types of attacks are found in wireless communication. Some of them are categorized [6] into four layers:

Control Channel Saturation DoS Attack (CCSD) Network Layer Attacks

Description PUEA occurs when a malicious [7] unlicensed user emulates a primary user to get the resources of the channel without sharing these resources with other unlicensed users. This leads to utilization of full band of spectrum by unlicensed user (attacker).PUE attack has two types: Self PUE attack and Malicious PUE attack.

CR has the capability to adjust its transmission parameters in accordance with the environment [8]. The cognitive engine in an adaptive radio is responsible for adjusting the transmission parameters to meet explicit requirements such as high data rate, high security, and low energy consumption. When the cognitive engine moves to search the radio parameters according to the current environment, the attacker send off the attack by manipulating the parameters he has control on to make the results biased.

Attacker also known as jammer here maliciously sends out packets to interrupt the licensed user in a communication session, creating a denial of service scenario. Due to continuous sending of data packets by attackers, licensed users never sense a channel as idle. The jammer can also send these packets to the licensed users and compel them to receive junk packets.

Spectrum Sensing Data Falsification occurs when a false spectrum sensing results is sent by the attackers to its neighbors. This will make the receiver to takes a wrong spectrum-sensing decision [9][10]. This attack mostly target centralized and distributed CRNs. SSDF attack is more dangerous in a distributed CRN. This SSDF attack also known as the Byzantine Attack.

When various CRs want to communicate at the simultaneously, traffic occurs at common control channel because the channel can support only a fixed number of data channels. The attacker makes use of this and generates a false MAC control frames to reach at the control channel.

This decrease performance of the network due to collisions of link layers.

The attacker in a sinkhole attack announces themselves as the most appropriate path to a desired destination,

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Survey: Various Attacks on Cognitive Radio (IJSRD/Vol. 4/Issue 05/2016/343)

Sinkhole Attacks

offering the neighboring nodes to make use of this path to transmit their packets [11]. The attacker can also use this route to carry out another type of attack called selective forwarding. Attacker in selective forwarding is capable of modifying or discarding packets from any node in CRN. The HELLO flood attack takes place when a broadcast message is sent to all the nodes in a network by the attacker with a HELLO Flood sufficient power of making them believe Attacks that it is their neighbor.

The Lion attack makes use of primary user emulation (PUE) attack to interrupt the Transport Transmission Control Protocol linking.

Layer Attacks The Lion attack is accomplished at the physical layer and at the transport layer it Lion Attack is targeted thus it is considered as across layer attack.

Table 1: Attacks Classification in CR III. RELATED WORK NafeesMansoor et.al, 2013[12]presented an architecture based on clusters for Cognitive Radio Ad-Hoc Networks wherein variation in space of spectrum are considered for clustering. This architecture breaks down the cognitive radio ad-hoc network into clusters. The CR nodes are grouped into clusters to sense if the free channels are in range of leader node to communicate. The leader node is called cluster head. Planned clusters dynamically adapt themselves according to the accessibility of spectrum, and the haste movement of the nodes.

Minho Jo et.al, 2013 [13] presented a work on selfish attacks .The selfish attack occurred when a fake signal is sent by SUs to other competing SUs prohibiting them to use the licensed band. This work also proposed anefficient detection technique called COOPON. COOPON detection technique provides very reliable selfish attack detection results.

Chaorui Zhang et.al, 2012 [14] studied the performance of CRNs under Primary User Emulation Attacks. A Markov model is developed to evaluate performance of system under PUEA. This paper also introduced a new performance parameter called CCC recovery time.

ZhaoyuGao et.al, 2013 [3] identified a attack for location privacy of database-driven cognitive radio networks. This paper also proposed a Location Inferring Algorithm that is based on spectrum utilization to make the attacker to locate the secondary user.

Ramesh babu B et.al, 2015 [15] presented a work on black hole and gray hole attack. This paper analysed that the features of result that are obtained from black hole and gray hole were same. But the influence of gray hole attack on CR ad-hoc network was less as compared to black hole attack. This proved that black hole attacks were more dangerous.

IV. ADVANTAGES OF COGNITIVE RADIO 1) Cognitive radio overcomes problem of spectrum shortage by properly utilizing the spectrum.

2) Cognitive radio improves satellite communications and communication quality.

3) Cognitive radio also improves Quality of Service (QoS). CR senses environmental and unintentional man-made interference and select channels with higher SNR. 4) Cognitive radio avoids deliberate jamming by jammers by sensing the availability of channels and by examining the jammer’s skill. CR switches dynamically to high quality channels to evade jamming.

V. CONCLUSION This paper primarily focused on various attacks in cognitive radio that are carried out on different layers of network.

Also in this approach, work was done on the effect of these attacks on cognitive radio network and how this attack happens.

REFERENCES [1] J. Mitola and G. Q. Maguire,” Cognitive radio: making software radios more personal,” IEEE Personal Communications, 6(4): pp.13-18, 1999.

[2] FeijingBao, Huifang Chen, Lei Xie, “Analysis of Primary User Emulation Attack with Motional Secondary Users in Cognitive Radio Networks,” IEEE 2012.

[3] Zhaoyu Gao, Haojin Zhu, Yao Liu, Muyuan Li, and Zhanfu Cao, “Location Privacy In Database-Driven Cognitive Radio Networks: Attacks And Countermeasures,” IEEE 2013 [4] T. Yucek and H. Arslan, “A survey of spectrum sensing algorithms for cognitive radio applications,” IEEE Communications Surveys and Tutorials, vol. 11, no. 1, pp. 116-130, 2009.

[5] Ghasemi, Amir, and Elvino S. Sousa, “Spectrum sensing in cognitive radio networks: requirements, challenges and design trade-offs,” IEEE Communications Magazine, 46.4 (2008): 32-39.

[6] Dr. AnubhutiKhare, Manish Saxena, et al. "Attacks and preventions of cognitive radio network-A survey,” International Journal of Advanced Research in Computer Engineering and Technology 2.3 (2013).

[7] Wassim El-Hajj, HaidarSafa, Mohsen Guizani, “Survey of Security Issues in Cognitive Radio Networks,” Journal of Internet Technology Volume 12 (2011) No.2.

[8] Qusay Mahmoud, “Cognitive Networks: Towards SelfAware Networks,” Wiley E-Book, New York, 2007.

[9] Chris Karlof and David Wagner, “Secure Routing in Wireless Sensor Networks: Attacks and Countermeasures,” Proceedings of the First IEEE International Workshop on Sensor Network Protocols and Applications, Berkeley, CA, May, 2003, pp.113127.

[10] Chetan Mathur and KoduvayurSubbalakshmi, “Security Issues in Cognitive Radio Networks,” Cognitive Networks: Towards Self-Aware Networks, Wiley, New York, 2007, pp.284-293.

[11] Chris Karlof and David Wagner, “Secure Routing in Wireless Networks: Attacks and Countermeasures,” Ad Hoc Networks, Vol.1, 2003, pp.293-315.

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[12] Nafees Mansor, et al. "Spectrum aware cluster-based architecture for cognitive radio ad-hoc networks," Advances in Electrical Engineering (ICAEE), 2013 International Conference on.IEEE, 2013.

[13] Minho Jo, Longzhe Han, Dohocn Kim, and Hah Peter In,” Selfish Attacks and Detection In Cognitive Radio Ad-Hoc Networks,” IEEE Network 2015.

[14] ChaoruiZhang ,Rong Yu, Yan Zhang,” Performance Analysis of Primary User Emulation Attack in Cognitive Radio Networks,”IEEE 2012.

[15] Ramesh babu B, MeenakshiTripathi , Manoj Singh Gaur, Dinesh Gopalani, Dharm Singh Jat, “Cognitive Radio Ad-Hoc Networks: Attacks and Its Impact,” IEEE 2015.

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