
Outline

1. Cognitive Radio 2. Evolution of radios 3. Cognitive Radio Network 4. Congnitive Radio Key Terms 5. Byzantine Attack 6. Byzantine Attack Models 7. Byzantine Defence 7. Byzantine Defence Models 8. Conclusion

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COGNITIVE RADIO

A Cognitive Radio is an intelligent radio which is aware of its environment, adapt its own parametes according to environment to optimise communication. A conginitve radio Detect/Sense Adapt/Reconfigure Use Cooperate A congnitive radio function as autonomous unit in the communication environment, exchage information about the environment with the network it access and other CR in the network.

August 25, 2015 [email protected] IIT Kharagpur

EVOLUTION OF RADIO

1. Hardware driven radios: Transmit & Recieve frequencies, modulation & demodulation type and other radio frequency (RF) parameters are determined by hardware and cannot be changed. Recieve frequencies can be tuned/changed within the range using electro-mechanical tuner. 2. Digital radios: Digital radios are able to performs part of the signal processing or transmission electronically, but these are not configurable in the field. 3. Software Defined Radios: All transmitter & reciever parameters, modes and applications can be configured and reconfigured by Software. But these cannot adapt according to environment 4. Congnitive radios: These radios are able to sense their environment & can adapt accordingly to perform operations.


COGNITIVE RADIO NETWORK


In some licensed bands such as GSM bands there is a large number of user, spectrum is highly utilized & now they cannot support more users. While in some licensed dedicated bands such TV, defense bands there is low utilization. So the cognitive radios (Secondary User) can be deployed to use licensed bands in cooperation with the Primary User (PU)

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COGNITIVE RADIO TERMINOLGY

1. Primary User (PU): Primary User is the user of the spectrum which have obtained regulatory permission/license to operate in that spectrum band. 2. Secondary User (SU): Secondary user is the unlicensed user which uses the spectrum band in cooperation with the primary user. 3. Spectrum Sensing: Spectrum sensing is the term associated with detection of all wireless channel that are available to use in the vicinity of secondary user. 4. Cooperative Spectrum Sensing (CSS): It is the spectrum sensing scheme in which CRs shares spectrum information with each other. 5. Data Falsification: Data Falsification means reporting of wrong data about the different parameters by a CR in the network.


BYZANTINE ATTACK

Byzantine Attack means Spectrum Sensing Data Falsification Attack (SSDF) Byzantine Attack Insider attack on Physical layer Occurs in process of CSS Objective of Byzantine attacker Vandalism Objective: Interference to primary user Exploitation Objective: Exclusion of idle channel


BYZANTINE ATTACK PARAMETERS

Characterstic of Byzantine attack is the flexibility and diversity


Byzantine attack classified on the basis of four parameters 1. Attack Scenario: where to attack? 2. Attack Basis: how to attack? 3. Attack Oppurtunity: when to attack? 4. Attack Population: who to attack?

Fig: Taxonomy of Byzantine Attack

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BYZANTINE ATTACK MODELS


1. Centralized Independent Probabilistic Small-Scale (CIPS) Attack P( X'

m = X

m + ∆|X

m < η) = α

0 P( X'

m = X

m - ∆|X

m > η) = α

1

2. Centralized Dependent Probabilistic Small-Scale (CDPS)Attack

Fig: CIPS

Fig: CDPS

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BYZANTINE ATTACK MODELS


1. Centralized Dependent Non Probabilistic Small-Scale (CDNS) Attack 2. Decentralized Independent Probabilistic Small-Scale (DIPS)Attack

Fig: CDNS

Fig: DIPS

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BYZANTINE DEFENCE


Byzantine Defence Objective – Find out Byzantine attackers – Mitigate negative effect of falsified sensing report

Byzantine Defence is based on wireless channel characterstic Byzantine Defence Algorithm can be classified as

– Homogeneous Sensing Scenario • Global Decision • Mean • Underlying Distribution • Utility

– Heterogenous Sensing Scenario • Propogation Model Based • Likelyhood Detection Based

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BYZANTINE DEFENCE


Global Decision Based Defence: In this, the fusion center(FC) uses deviation of global decision and local decision of SUs to detect attackers from the honest ones

δi(t) = deviation between global & local decision Ao = null hypothesis that there exists no malicious user A1 = alternate hypothesis K = Number of SU PB, PH = probability of inconsistency of Byzantine or honest SU

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BYZANTINE DEFENCE


Mean-Based Defence: In this, mean value of SU reports are used to find out the outliers with large deviations.

Robust statistics is essential for the success of this method. Different techniques are used to find consistent statistics. The below mention method take advantage of fluctions in the robust statistics.

K is the sensing slot

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BYZANTINE DEFENCE


Underlying Distribution-Based Defence: This scheme uses the fact that in homogenous environment, the report of SU must obey the same distribution. So some metric representing the distribution may be extracted from the SU reports. Assuming that the true spectrum is Markovian, some metrics are derived to detect the malicious users.

are metrics of honest users

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BYZANTINE DEFENCE


Utility-Based Defence: In this scheme, instead of identifying the attacker, all SU are guided to report honest reports by use of penalties and incentives of the system.

U = utility before the attack Û = utility after the attack

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BYZANTINE DEFENCE


Propogation Model-Based Defence: In Propogation Model-Based Defence, deviations in the SUs sensing result can be mapped using difference in channel characterstics. But falsified reports can deviates from this mapping. This relation is used to find the malicious users.

β = fading factor of channel d = distance between PU and SU

degree of similarity in SU reports is proportional to the distance gap |di - dj|, i ≠ j

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BYZANTINE DEFENCE


Likelihood Detection-Based Defence: The FC use prior knowledge to calculate probability of SU being malicious using hisotry reports of SU. The Bayesian approach is used to find the probability.

To find mutiple malicious users, the onion peeling and belief propogation approach is used. In an alternate approach, SUs are divided into classes based on detection & false alarm probabilties Class parameters are estimated using iterative expectation maximization algorithm Malicious users are detected using the class parameters Likelihood Detection is powerful against CIPS attacks

Tn = M denotes n-th SU is malicious and Ft is all observation of t sensing slots

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BYZANTINE DEFENCE


Fig: Depicts deferent defence algorithms usefulness against the model of attack

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Conclusion


CRN is the new rodio technology that can provide optimum utilization of the scarce spectrum resource Spectrum Sensing is essential for existense of the CRN CRN cannot be successfully adopted till there are robust alogrithms are available for mitigating the risk of Byzantine attacks Byzantine attack and defence is like an interactive game of Spear & Shield between the different stakeholders.

Ref: Linyuan Zhang, Guoru Ding, Qihui Wu, Yulong Zou, Zhu Han & Jinlong Wang, “Byzantine Attack and Defense in Cognitive Radio Networks: A Survey” IEEE Communication Surveys & Tutorials, 2015

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Thank You...

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byzantine attack & defense in cognitive radio network

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