Dynamic Authentication of Typing Patterns

Slides:

Advertisements

Similar presentations

Neural networks Introduction Fitting neural networks

Advertisements

NEURAL NETWORKS Perceptron

High Throughput Computing and Protein Structure Stephen E. Hamby.

Machine Learning Neural Networks

Security A system is secure if its resources are used and accessed as intended under all circumstances. It is not generally possible to achieve total security.

CS Team 5 Alex Wong Raheel Khan Rumeiz Hasseem Swati Bharati Biometric Authentication System.

Prénom Nom Document Analysis: Artificial Neural Networks Prof. Rolf Ingold, University of Fribourg Master course, spring semester 2008.

Presented by Li-Tal Mashiach Learning to Rank: A Machine Learning Approach to Static Ranking Algorithms for Large Data Sets Student Symposium.

Keystroke Biometric Studies Keystroke Biometric Identification and Authentication on Long-Text Input Book chapter in Behavioral Biometrics for Human Identification.

Classification of Music According to Genres Using Neural Networks, Genetic Algorithms and Fuzzy Systems.

Silberschatz and Galvin  Operating System Concepts Module 20: Security The Security Problem Authentication Program Threats System Threats Threat.

Cristian Urs and Ben Riveira. Introduction The article we chose focuses on improving the performance of Genetic Algorithms by: Use of predictive models.

Using Neural Networks in Database Mining Tino Jimenez CS157B MW 9-10:15 February 19, 2009.

Keystroke Dynamics Etem DENİZ, Buğra KOCATÜRK, Gülşah YILDIZOĞLU, Ömer UZUN Boğaziçi University, CMPE, May 2010.

Keystroke Biometric System Client: Dr. Mary Villani Instructor: Dr. Charles Tappert Team 4 Members: Michael Wuench ; Mingfei Bi ; Evelin Urbaez ; Shaji.

User Authentication Using Keystroke Dynamics Jeff Hieb & Kunal Pharas ECE 614 Spring 2005 University of Louisville.

NEURAL NETWORKS FOR DATA MINING

Lecture 19 Page 1 CS 111 Online Authentication for Operating Systems What is authentication? How does the problem apply to operating systems? Techniques.

Genetic Algorithms Michael J. Watts

From Machine Learning to Deep Learning. Topics that I will Cover (subject to some minor adjustment) Week 2: Introduction to Deep Learning Week 3: Logistic.

SBox is a Unique Package that maximize the interaction between instructors and students in easy reliable,efficient way. Sbox is Devided into 4 main parts.

Handwritten Recognition with Neural Network Chatklaw Jareanpon, Olarik Surinta Mahasarakham University.

1 Chapter 6. Classification and Prediction Overview Classification algorithms and methods Decision tree induction Bayesian classification Lazy learning.

REVISITING DEFENSES AGAINST LARGE SCALE ONLINE PASSWORD GUESSING ATTACKS Mansour Alsaleh,Mohammad Mannan and P.C van Oorschot.

Securing Passwords Against Dictionary Attacks Presented By Chad Frommeyer.

 Based on observed functioning of human brain.  (Artificial Neural Networks (ANN)  Our view of neural networks is very simplistic.  We view a neural.

Back-Propagation Algorithm AN INTRODUCTION TO LEARNING INTERNAL REPRESENTATIONS BY ERROR PROPAGATION Presented by: Kunal Parmar UHID:

Accurate Robot Positioning using Corrective Learning Ram Subramanian ECE 539 Course Project Fall 2003.

Learning to Detect Faces A Large-Scale Application of Machine Learning (This material is not in the text: for further information see the paper by P.

Team Members Ming-Chun Chang Lungisa Matshoba Steven Preston Supervisors Dr James Gain Dr Patrick Marais.

Typing Pattern Authentication Techniques 3 rd Quarter Luke Knepper.

INTRODUCTION TO BIOMATRICS ACCESS CONTROL SYSTEM Prepared by: Jagruti Shrimali Guided by : Prof. Chirag Patel.

Chapter 12: Authentication Basics Passwords Challenge-Response Biometrics Location Multiple Methods Computer Security: Art and Science © Matt.

An Introduction to Biometrics

Access control Presented by: Pius T. S. : Christian C. : Gabes K. : Ismael I. H. : Paulus N.

Machine Learning Artificial Neural Networks MPλ ∀ Stergiou Theodoros 1.

Chapter 11 – Neural Nets © Galit Shmueli and Peter Bruce 2010 Data Mining for Business Intelligence Shmueli, Patel & Bruce.

SQL INJECTION Diwakar Kumar Dinkar M.Tech, CS&E Roll Diwakar Kumar Dinkar M.Tech, CS&E Roll

Supervised Learning in ANNs

Fall 2004 Perceptron CS478 - Machine Learning.

Challenge/Response Authentication

QianZhu, Liang Chen and Gagan Agrawal

Ranga Rodrigo February 8, 2014

Creating fuzzy rules from numerical data using a neural network

Accurate Robot Positioning using Corrective Learning

Estimating Link Signatures with Machine Learning Algorithms

Neural Networks A neural network is a network of simulated neurons that can be used to recognize instances of patterns. NNs learn by searching through.

Forecasting The Future of Movies

Unable to Login AOL Account Troubleshoot Issues When you are trying to sign in to your AOL mail account then revealed the “unusual activity” or.

Generalization ..

cs638/838 - Spring 2017 (Shavlik©), Week 7

12: Security The Security Problem Authentication Program Threats

Lecture 2 Introduction to Computer Science (continued)

Web Service Accounting System

of the Artificial Neural Networks.

Predicting Student Performance: An Application of Data Mining Methods with an Educational Web-based System FIE 2003, Boulder, Nov 2003 Behrouz Minaei-Bidgoli,

The main cause for that are the famous phishing attacks, in which the attacker directs users to a fake web page identical to another one and steals the.

Dynamic Authentication of Typing Patterns

Capabilities of Threshold Neurons

REVISITING DEFENSES AGAINST LARGE SCALE ONLINE PASSWORD GUESSING ATTACKS Mansour Alsaleh,Mohammad Mannan and P.C van Oorschot.

IP Control Gateway (IPCG)

Exercise: Hashing, Password security, And File Integrity

Operating System Concepts

Information Retrieval

Computer Vision Lecture 19: Object Recognition III

Operating System Concepts

POLICIES & EDUCATION COURSES Employee Training

Learning Combinational Logic

Neural Network Typing Authentication

Presentation transcript:

Dynamic Authentication of Typing Patterns Luke Knepper

Introduction Passwords are outdated Many biometrics are expensive Typing patterns differ by person People can be authenticated by their typing patterns Cheap and flexible to implement

Agenda Background Final Process Experimentation Current Results Goals

Background Measures users' typing patterns, compares to a previous standard Technique first used in WWII Commercial packages available Experiments have been done on static passwords → easy to hack No research yet on dynamic text blocks

Process (front-end) On account set-up, user will type large amounts of dynamic text On subsequent log-ins, user will type small amount of dynamic text User may still need to use username, password, etc.

Process (back-end) Set-up data will be used to breed (i.e. train) a neural network The optimal weight vector can be generated efficiently via back-propagation, genetic algorithms, parallel processing Log-in data will be fed through neural network: either meets threshold (admitted) or does not meet (rejected)

Experimentation Requirements: Must develop optimal neural network and threshold value for back-end Must develop optimal GUI / Corpus size Must determine accuracy

Experimentation Neural Network Optimization: Develop online data collection applet Collect massive amounts of data Use data to train multiple neural network types Test different network types to determine optimal network and threshold

Experimentation GUI / Corpus Optimization: Collect ~20 test subjects Have them set up dummy accounts Subjects attempt to log into their accounts and accounts of others Repeat for different GUI layouts, corpus sizes

Experimentation Accuracy Testing: Collect large number of test subjects Subjects set up dummy accounts Subjects attempt to log into their accounts and accounts of others on subsequent sittings (spaced out by 1 week and 1 month) Measure final accuracy

Current Results Proof-of-concept program Determines the mystery typer between two known users Uses simple single-layer neural network Correct 18 / 20 = 90%

Goals Final program will be: Easily implementable Difficult to crack Accurate above 90% Will be combined with password security to make inexpensive and secure system

Fin Who wants a cookie?