Introduction. Emerging Issues-Spectrum Management. Characteristics of Cognitive Radio.• Cognitive Capability.• Reconfigurability. Attacks and Detection Techniques.• Incumbent Emulation attack.• Spectrum Sensing Data Falsification attack. Advantages of CRN. Applications of CRN. Conclusion. References.
A number of wireless applications have been growing over thelast decade. Most of the frequency spectrum has already beenlicensed by government agencies, such as FederalCommunications Commission (FCC). Therefore, there exists an apparent spectrum scarcity for newwireless applications and services. Cognitive radio canefficiently utilize the unused spectrum for secondary usagewithout interfering a primary licensed user.
A Cognitive radio is a fully reconfigurable device which canobserve and change or adapt its communication parameters forenabling secondary usage of the spectrum and yield anefficient usage of the spectrum. The key motivation behind this technology is to increasespectral utilization and to optimize the use of radio resources.
The concept of cognitive radio was first proposed by JosephMitola III in a seminar at KTH(the Royal Institute ofTechnology) in 1998. Depending on transmission and reception parameters, there aretwo main types of cognitive radio:• Full Cognitive Radio(Mitola Radio).• Spectrum-Sensing Cognitive Radio.
Determine which portions of the spectrum are available:Spectrum Sensing. Select the best available channel: Spectrum Decision. Coordinate access to this channel with other users: SpectrumSharing. Vacate the channel when a licensed user is detected:Spectrum Mobility.
Cognitive Radio have two main characteristics:• Cognitive Capability.• Reconfigurability. Cognitive Capability: Identify the unused spectrum at aspecific time or location (Spectrum Holes/ White Spaces) Reconfigurability: Transmit and Receive on a variety offrequencies. Use different access technologies.
We define an attack on cognitive networks as anyactivity that results in(a) unacceptable interference to the licensed primary usersor(b) missed opportunities for secondary usersHere we describe the attacks against CRs and CRNs Incumbent Emulation attacks. Spectrum Sensing Data Falsification attacks. Cross-layer attacks.
We have identified and discussed two security threatsto CR networks: IE attacks and SSDF attacks. Bothattacks potentially pose a great threat to CRnetworks. There are other types of attacks that candisrupt operations in a CR network. For instance, simple jamming attacks may be veryeffective in interfering with the spectrum sensingprocess. However, we have limited our discussions tosecurity issues that are unique to CR networks, withparticular focus on security threats to DSS.
When an incumbent is detected in a given band, allsecondaries avoid accessing that band. In an incumbent emulation (IE) attack, a malicioussecondary tries to gain priority over other secondariesby transmitting signals that emulate thecharacteristics of an incumbent’s. There may be “selfish” IE attack or “malicious” IEattack.
Malicious secondary users may take advantage of thecooperative spectrum sensing and launch SSDFattacks by sending false local spectrum sensingresults to others, resulting in a wrong spectrumsensing decision. Three attack models are presented as follows:• Selfish SSDF.• Interference SSDF.• Confusing SSDF
Detection of SSDF attacks assume a model where anumber of SUs sense the environment and reporttheir findings to a FC. Fusion Centre fuses the reports provided by the restof nodes, uses several fusion rules to evaluate thereports. Furthermore, reports are provided by SUs can be oftwo types :• Binary.• Continuous.
Binary type of reporting:o In the proposed detection algorithm the Trust Valuesof SUs and Consistency Values of each user iscomputed.• If both the values falls below a threshold the SU ischaracterized as an outlier.• A drawback of this work is that only one adversary has beenconsidered.
o In an another model proposed a Reputation Metric isused to detect and isolate attackers from legitimateSUs.• For the computation of this metric the output of each SU iscompared to the decision made by the FC.o E. Noon and H. Li study a specific case of anattacker, the “hit-and-run” attacker.• Deviates between an honest mode and a lying mode.• The detection scheme combines a point system approach.
Continuous type of reporting:o A detection method using statistics is described.• Here a grid of sensors, divided into clusters, sendinformation about their RSS, along with their location to theFC.• This approach has two phases.• This approach does not restore the reputation of SU thattemporarily misbehaves as it increases a blacklist countereach time if the filter’s output does not lie between thedefined thresholds.
o F. Yu, M. Huang, Z. Li and P. Mason propose ascheme to defend against SSDF attack in a distributedfashion for Cognitive ad-hoc radio networks.• A key difference of this work is that no FC is used.• SUs exchange information and decide independently uponthe presence of the primary transmissions.• Each SU computes the max deviation of received from themean value.• The simulation results show that distributed consensusapproach gives the best results.
Unused spectrum are determined and made use ofthem automatically. Improves the spectrum utilization by neglecting theover occupied spectrum channels and filling theunused spectrum channels Automatically improves and accomplishes itsprogress and minimize interference.
Spectrum Awareness concept. Spectrum sharing techniques can help us fill theregulatory “gaps” in a particular interferenceenvironment. A great deal of research still needs to be done onsimulating and explore these intelligent networkideas. Cognitive radio technology can solve the problem ofspectrum underutilization.
Simulation framework for security threats in cognitive radio networks-E. Romero A. Mouradian J. Blesa J.M. Moya A. AraujoETSI Telecomunicación n, Universidad Polite cnica de Madrid, 28040Madrid, Spain. Security Aspects in Software Defined Radio and Cognitive RadioNetworks: A Survey and A Way Ahead-Gianmarco Baldini, Member, IEEE, Taj Sturman, Member, IEEE, AbdurRahim Biswas, Member, IEEE, RuedigerLeschhorn, Member, IEEE, Gy oz o G odor, Member, IEEE, and MichaelStreet. A Survey on Security Threats and Detection Techniques in CognitiveRadio Networks-Alexandors G. Fragkiadakis,Elias Z.Tragos, Ioannis G. Askoxylakis.Contd…
International Journal of Computer Applications (0975 – 8887) Volume 30–No.1, September 2011 31 : Cognitive Radios: Need, Capabilities,Standards, Applications and Research Challenges-Prabhjot Kaur Department of Electronics and Communications ITMUniversity Gurgaon, India.Moin Uddin Delhi Technological UniversityDelhi, India.Arun Khosla , Department of Electronics and CommunicationsNational Institute of Technology, Jalandhar, India. Attack prevention for collaborative spectrum sensing in cognitive radionetworks.-Lingjie Duan, Alexander W. Min†, Jianwei Huang, Kang G. Shin† Network Communications and Economics Lab, Dept. of InformationEngineering, The Chinese University of Hong Kong, Hong Kong†Real-Time Computing Laboratory, Dept. of EECS, The University ofMichigan, Ann Arbor, MI 48109-2121.
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