Slide 1MotivationData Set (1)Data Set (2)Learning ModelExperiments (1)Experiment (2)Thank You…An ANN approach to identify malicious URLsECE 539 – Final ProjectJayneel GandhiMotivation•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 futureData 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+ReputationData 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:2,396,130HUGE data set !!!Takes long time to run… in the range of 20-30 daysLearning ModelSource: Sysnet group webpage at University of California, San DiegoExperiments (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 URLsExperiment (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 timeTHANK
View Full Document
Unlocking...