Byzantine Attack & Defense in Cognitive Radio Network

A
Seminar
on
Byzantine Attack & Defence
in
Conginitive Radio Network
August 25, 2015Chandramohansharma.cms@gmail.com
Guided By Presented By
Dr. Sandip Chakraborty Chandra Mohan Sharma
Assitant Professor IIT Kharagpur 15CS60D04, M. Tech. Ist Year
IIT Kharagpur

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
IIT Kharagpur

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, 2015Chandramohansharma.cms@gmail.com 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)
IIT Kharagpur

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
IIT Kharagpur

BYZANTINE ATTACK MODELS
1. Centralized Independent
Probabilistic Small-Scale (CIPS)
Attack
P( X'm
= Xm
+ ∆|Xm
< η) = α0
P( X'm
= Xm
- ∆|Xm
> η) = α1
2. Centralized Dependent
Probabilistic Small-Scale
(CDPS)Attack
Fig: CIPS
Fig: CDPS
IIT Kharagpur

BYZANTINE ATTACK MODELS
1. Centralized Dependent Non
Probabilistic Small-Scale (CDNS)
Attack
2. Decentralized Independent
Probabilistic Small-Scale
(DIPS)Attack
Fig: CDNS
Fig: DIPS
IIT Kharagpur

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
IIT Kharagpur

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
IIT Kharagpur

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
IIT Kharagpur

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
IIT Kharagpur

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
IIT Kharagpur

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
IIT Kharagpur

Thank You...
IIT Kharagpur

Byzantine Attack & Defense in Cognitive Radio Network

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