TEXAS TECH UNIVERSITY
DEPARTMENT OF COMPUTER SCIENCE
WHITACRE COLLEGE OF ENGINEERING
U-REASON SEMINAR SERIES FALL - 2013
URLDOC: Learning To Detect Malicious URLs Using
Online Logistic Regression
By Mohammed Nazim Feroz
Texas Tech University
Date: November 26th, 2013 (Tuesday)
Time: 3:40pm-4:40pm
Venue:ECE 226 (Bullen Room)
Faculty Coordinator: Dr. Yong Chen (yong.chen@ttu.edu)
Student Coordinators: Navaneeth Thiagarajan, Dan Ferguson, Lakhan Jhawar
Abstract:
Web services such as online banking, gaming, and social networking have rapidly evolved as has the reliance
upon them by people to perform everyday tasks. As a result, a large amount of information is uploaded on a
daily basis to the web. The openness of the web exposes opportunities for criminals to upload malicious
content. Despite extensive research, email based spam filtering techniques are unable to protect other web
services. Therefore, a counter measure must be taken that generalizes across web services to protect the user
from malicious hosts. This paper describes an approach that classifies URLs automatically based on their
lexical and host-based features. The usability of Mahout is demonstrated for such scalable machine learning
problems and online learning is considered over batch learning due to its useful properties. The classifier
achieves 93-97% accuracy by detecting a large number of malicious hosts, with a modest false positive rate.
Speaker Bio:
Mohammed Nazim Feroz is a graduate student majoring in Computer
Science at Texas Tech University. He is currently working on his thesis with
Dr. Susan Mengel in Computer Security. His primary interests lie in the field
of Artificial Intelligence, Computer security, and Web application
development. Mohammed completed his Bachelor’s degree in Information
Technology, at Anna University, India, 2011. During his undergraduate
study, he has done his research on Healthcare IT and Artificial Intelligence
areas and submitted three papers on international conferences.