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AUTHENTICATION
Submited by
Name : V.Vinotha
Class : II-M.Sc.,Computer Science
Batch : 2017-2019
Incharge Staff :Ms.M.Florence Dayana
WHAT IS AUTHENTICATION
 Positive verification of identity (man or
machine)
 Verification of a person’s claimed identity
 It is used for Security Purpose
EXAMPLE:
 Password
 Passphrase
 PIN
ETOKEN
 Can be implemented on a USB key fob or a smart card
 Data physically protected on the device itself
 On the client side, the token is accessed via password
 Successful client-side authentication with the password
invokes the token to generate a stored or generated
passcode, which is sent to the server-side for
authentication.
ETOKEN
 May store credentials
such as passwords,
digital signatures and
certificates, and private
keys
 Can offer on-board
authentication and digital
signing
SMART CARDS
 Size of a credit card
 Usually an embedded microprocessor with
computational and storage capabilities
 Programmable platforms:
 C/C++
 Visual Basic
 Java
 .Net (beta)
SMART CARDS CONT’D
 Contact vs. contactless
 Memory vs. microprocessor
RFID
 RFID - Radio Frequency IDentification
 Integrated circuit(s) with an antenna that can respond to
an RF signal with identity information
 No power supply necessary—IC uses the RF signal to
power itself
 Susceptible to replay attacks and theft
 Examples:
 Smart Tag, EZPass
 Garage parking permits
RFID
 13.56Mhz read/write support
 May communicate with a variety of
transponders (ISO15693, ISO14443 Type A
& B, TagIt, Icode, etc.)
 Reader is controlled via PCMCIA interface
using an ASCII protocol
PHYSICAL BIOMETRICS
• Fingerprint Smell
• Thermal Face
• Hand Vein
• Nail Bed
• DNA
• Palm Print
 Iris
 Hand Geometry
 Finger Geometry
 Face Geometry
 Ear Shape
 Retina
BEHAVIORAL BIOMETRICS
 Signature
 Voice
 Keystroke
 Gait
FINGERPRINTS
 Vast amount of data available on fingerprint
pattern matching
 Data originally from forensics
 Over 100 years of data to draw on
 Thus far all prints obtained have been unique
FINGERPRINT BASICS
 Global features
 Features that can be seen with the naked eye
 Basic ridge patterns
 Local features
 Minutia points
 Tiny unique characteristics of fingerprint ridges used
for positive identification
FINGERPRINT SCANNERS
BIOMETRIC AUTHENTICATION
TERMS
 False Acceptance Rate (FAR)
 False Match Rate (FMR)
 Percentage of access attempts by unauthorized individuals
which are nevertheless successful
 False Rejection Rate (FRR)
 False Non-Match Rate (FNMR)
 Percentage of access attempts by enrolled individuals who
are nevertheless rejected
 Equal Error Rate
 FAR = FRR
ENROLLMENT
RawImage
Data
EnrollmentDatabase
Biometric
Template
Sampled
ImageDataBiometricScanner
ImageProcessing
(Enrollment
Computer)
Biometric
Algorithm
(Enrollment
Computer)
VERIFICATION
RawImage
Data
EnrollmentDatabase
Biometric
Template
Sampled
ImageDataBiometricScanner
ImageProcessing
(Enrollment
Computer)
Biometric
Algorithm
(Enrollment
Computer)
Comparison
Algorithm
Match?Yesor
No
MOTIVATION
 Real-world considerations:
 What you know and what you have
Can be stolen or forgotten
Susceptible to replay attacks
 Who you are
Unique biometrics that hinder replay attacks and
imposters
Privacy issues arise
X.509 CERTIFICATES
 Use of digital certificates issued by a trusted Certificate Authority
(e.g. VeriSign)
 A Digital Certificate contains information to assert an identity
claim
 Name
 Serial number
 Expiration dates
 Certificate holder’s public key (used for encrypting/decrypting
messages and digital signatures)
 Digital signature of Certificate Authority (so recipient knows
that the certificate is valid)
 The recipient may confirm the identity of the sender with the
Certificate Authority
Authentication V.Vinotha II M.Sc computer science Bon secours college for women

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Authentication V.Vinotha II M.Sc computer science Bon secours college for women