An ANN approach to identify malicious URLs

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+Reputation

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…