1. An ANN approach to identify malicious URLs ECE 539 – Final Project Jayneel Gandhi

2. Motivation Prevent users from visiting malicious webpage Lot of effort into reducing internet crimes Try to learn which URL is malicious from different sources Stop users from accessing such website in future

3. Data Set (1) Developed by SysNet group at University of California at San Diego Posted at UCI Machine Learning Repository http://archive.ics.uci.edu/ml/datasets/URL+Re putation http://archive.ics.uci.edu/ml/datasets/URL+Re putation

4. Data Set (2) Feature Space is made up of: – Lexical Features Hostname Primary Domain Path Tokens – Host Based Features WHOIS info IP prefix Geographical Feature Vector (sparse): 3,231,961 Number of instances: 2,396,130 HUGE data set !!! Takes long time to run … in the range of 20-30 days

5. Learning Model Source: Sysnet group webpage at University of California, San Diego

6. Experiments (1) Data set organized as URLs visited over the period of 121 days (Day0-Day120) Each day has roughly 15,000-40,000 URLs visited I will only be running experiments on Day0 consisting of 16000 URLs

7. Experiment (2) Experiment 1 – Use single perceptron model Online learning possible Has history of all the URLs visited is preserved Experiment 2 – Use Support Vector Machine (SVM) Online learning not possible Can only learn based on certain past history Losses certain history with time

8. THANK YOU…