Mukul A. Khairatkar Building CognitiveRadio for Intrusion Detection at Physical Layer Under The Guidance of Dr. Tulin Mangir California State University.

Mukul A. Khairatkar

Building CognitiveRadiofor Intrusion Detection at Physical

Layer

Under The Guidance of

Dr. Tulin Mangir

California State University Long Beach

AgendaNecessity of Cognitive radioIntroduction to Cognitive RadioBasic Building Blocks for CRSystem OperationResults and ObservationsIssuesConclusionFuture Work

Background

Wireless systems are under attackDifferent devices, protocols, power levelNo physical layer security architecture2.4 GHz ISM band is most popular

Electromagnetic InterferenceReduced signal strengthErrors in received communicationPoor connectivityHome/Office/Hospital/Government/Educational

System

Density of Wireless APs

Image source : InSSIDER (Software by Metageek.net)

Other services in 2.4GHzBluetooth (802.15.1)

Zigbee (802.15.4)

Cordless telephone (CT-2)

Wireless security cameras

X10 devices

Wireless mouse and keyboard

Other ( Microwave, Baby Monitors… and

Intruders)

Possible ways to mitigate

2.4GHz ISM band security probeMonitor and control illegal activityMonitor EMI level at given locationSense-Adapt-Learn System

Opportunistic spectrum sensingSense spectrum other than 2.4 GHz If primary signal is absent, start secondary

communicationSense-Adapt-Learn System

Find out Who is doing What?

Jim’s Bluetooth Module

Robin’s Cordless phone

Lilly's Wireless camera

Adam’s cell phone

Juan’s Wireless USB

Barbara’s Home Router

Jim’s Bluetooth

Barbara’s Home Router

Robin’s Cordless Phone

Conventional RF Receiver

Local OscOr

Freq. Synth.

FilterDemodula

tor

Baseband Signal

RFAmplifier Mixer

IFAmplifier

Cognitive Radio

Intelligent form of Software Defined Radio Primary Signal and Secondary Signal Sensing presence of Primary Signal

Primary SignalS-Band

Secondary Signal

ISM band

Block Diagram of Cognitive Radio

RF FRONT

END

ADC

DAC

DDC

DUC

BASEBAND PROCESSI

NG

RF Section IF Section Baseband Section

RX

TX

Hardware Basic Components

12 bit 64Ms/s

14 bit 128Ms/s

SIDE A

FPGA

ADC

ADC

DAC

DAC

RF Front end(Receive)

RF Front end(Transmit)

USB 2.0

Host Computer

The Signal Flow‘In-phase’

Output

‘Quadrature’

Output

LOW PASS

FILTERLOW PASS

FILTER

Local Oscillator

SAW Filter

& Amplifi

er

90°Sin θ

Cos θ

MixerI

Q

ADC12 bit

64 Ms/s2.4 GHz

FPGA Implementation

CORDIC Rotator

CIC Decimation Filter

Gain Adj.

(Shift Oper.)

FIR Clean-

up Filter

Phase Accumula

tor

Complex

Signal Input

Phase Increme

nt

USB2.0Host

Computer

Software Building Blocks

Attaching signal window to Spectrum Analyzer

Arranging data on scale

USB Data Capture

Software Spectrum Analyzer (Display)

USB InterfaceSoftware Radio

HardwareUSRP

LINUX

802.11b System

Software Spectrum Analyzer

Result-1 Channel 5

Result-2 Channel 1

Result – 3 Detecting Unknown Activity

Result- 4 Detecting Unknown Activity

Implementation IssuesUSB data bus allows access to 8MHz maximum.

ADC sampling at 64 Ms/sUSB data rate 32 Mbps 24 Mbps with USB over

headWith minimum decimation of 8 bandwidth is 8 MHz

Receiver sensitivityS = -174dbm + 10 log 10 (20MHz) + 9dbm = -91dbm

Decoding packets on host computer

Cognitive Radio Issues

No fixed Spectrum Sensing Algorithm

Difficult to apply to system using Frequency hopping

Requires more high speed computer for complex

systems

Conclusion

Cognitive Radio is useful for detecting

wireless intrusions

Building Software radio for 802.11b

Scan ISM band for different services

Detect unknown activity

Future WorkSystem for different protocols like 802.11 g,

Zigbee.etc.Design Jamming system for a particular

protocolMap ISM band channels to another license

band (for ex. S- band)

Reference Claudio, R. C. M. Da, Silva, Choi Brian, and Kim Kyouwoong. 2007. Distributed

Spectrum Sensing for Cognitive Radio Systems. Wireless @ Virginia Tech: Virginia Polytechnic Institute and State University.

Jae-Kwon Lee, Ju-Hyun Yoon, and Jin-Up Kim. 2007. A new spectral correlation approach to spectrum sensing for 802.22 WRAN system.

Jongmin Lee, Taehoon Kim, Sangwook Han, Seungmo Kim, and Youngnam Han. 2008. An Analysis of Sensing Scheme Using Energy Detector for Cognitive Radio Networks.

Parthapratim, De, ching Ying Liang. 2008. Blind Spectrum Sensing Algorithm for Cognitive Radio Networks. IEEE Transactions on Vehicular Technology, 2834.

Wei, Zhang, K. M. Ranjan, and Ben Khaled Letaief. 2008. Cooperative spectrum sensing optimization in cognitive radio networks. In ICC 20083411.

Wei, Zhang, Ben Khaled Lataief. December, 2008. Coooperative Spectrum Sensing with Transmit and Relay Diversity in Cognitive Radio Networks. IEEE Transactions on Wireless COmmunication 7, no. 12: 4761.

Zhu, Han, Jiang Hai. 2008. Replacement of spectrum sensing and avoidance of hidden terminal for cognitive radio. In Wireless communication national conference 2008, edited by IEEE Communication Society, 1448IEEE.

Contd. Andrew, Cole. Satellite's time has come. 2007. Internet on-

line. Available from <http://telephonyonline.com/access/commentary/telecom_satellites_time/>. [January 22.

FCC. Ancillary terrestrial component (ATC) Significant satellite rulemaking. 2009. Internet on-line. Available from <http://www.fcc.gov/ib/sd/ssr/atc.html>. [March 04.

Helena, Leeson, Hansell Paul, Burns John, and Spasojeviç Zoran. 2002. Demand for use of the 2.4GHz ISM Band. Aeigis System Ltd.

Nada, Golmie, Mouveaux Frederic. 2007. Interference in the 2.4 GHz ISM Band: Impact on the Bluetooth Access Control Performance.

DEMO

Thank You