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Erational Realization of Algorithmic Complexity Theory) Are a Critical Component of Our Approach University of Alberta Detecting Visually Similar Web Pages: Application to Phishing Detection By Teh-Chung, Chen A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Software Engineering and Intelligent Systems Department of Electrical and Computer Engineering © Teh-Chung, Chen Spring 2011 Edmonton, Alberta Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission. Examining Committee Dr. James Miller, Department of Electrical and Computer Engineering Co-supervisor Dr. Scott Dick, Department of Electrical and Computer Engineering Co-supervisor Dr. Vicky Zhao, Department of Electrical and Computer Engineering Committee Member Dr. Osmar Zaiane, Department of Computing Science Committee Member Dr. Jens Weber, Department of Software Engineering, University of Victoria Committee Member Dedicated to the World of Computer Security Computer security is simply just like a blindblind----fightfight of Gladiators. Only by hearing the clang of sword and shield, I know I’m still alive to fight against my enemy in the Coliseum. ~ Antonio Chen 以子之矛矛矛 攻子之盾 ~ 韓非子 <<<難一<難一>>> (Be ready to set your own spear against your own shield ~ Han-Fei-Tzy Fable) Abstract We propose a novel approach for detecting visual similarity between two web pages. The proposed approach applies Gestalt theory and considers a webpage as a single indivisible entity. The concept of supersignals, as a realization of Gestalt principles, supports our contention that web pages must be treated as indivisible entities. We objectify, and directly compare, these indivisible supersignals using algorithmic complexity theory. We apply our new approach to the domain of anti-Phishing technologies, which at once gives us both a reasonable ground truth for the concept of “visually similar,” and a high-value application of our proposed approach. Phishing attacks involve sophisticated, fraudulent websites that are realistic enough to fool a significant number of victims into providing their account credentials. There is a constant tug-of-war between anti-Phishing researchers who create new schemes to detect Phishing scams, and Phishers who create countermeasures. Our approach to Phishing detection is based on one major signature of Phishing webpage which can not be easily changed by those con artists –Visual Similarity . The only way to fool this significant characteristic appears to be to make a visually dissimilar Phishing webpage, which also reduces the successful rate of the Phishing scams or their criminal profits dramatically. For this reason, our application appears to be quite robust against a variety of common countermeasures Phishers have employed. To verify the practicality of our proposed method, we perform a large-scale, real-world case study, based on “live” Phish captured from the Internet. Compression algorithms (as a practical operational realization of algorithmic complexity theory) are a critical component of our approach. Out of the vast number of compression techniques in the literature, we must determine which compression technique is best suited for our visual similarity problem. We therefore perform a comparison of nine compressors (including both 1-dimensional string compressors and 2-dimensional image compressors). We finally determine that the LZMA algorithm performs best for our problem. With this determination made, we test the LZMA-based similarity technique in a realistic anti-Phishing scenario. We construct a whitelist of protected sites, and compare the performance of our similarity technique when presented with a) some of the most popular legitimate sites, and b) live Phishing sites targeting the protected sites. We found that the accuracy of our technique is extremely high in this test; the true positive and false positive rates reached 100% and 0.8%, respectively. We finally undertake a more detailed investigation of the LZMA compression technique. Other authors have argued that compression techniques map objects to an implicit feature space consisting of the dictionary elements generated by the compressor. In testing this possibility on live Phishing data, we found that derived variables computed directly from the dictionary elements were indeed excellent predictors. In fact, by taking advantage of the specific characteristic of dictionary compression algorithm, we slightly improve on our accuracy when using a modified/refined LZMA algorithm for our already perfect NCD classification application. Acknowledgement I would like to thank my supervisors, Dr. Miller and Dr. Dick, for their knowledgeable guidance and generous support. Without their help, I definitely can not reach the bay shore of my destination-the final defense. Thank you my bosses! You are the best!! Table of Contents Chapter 1 Introduction ........................................................................................................ 1 1.1 Webpage similarity.................................................................................................. 1 1.2 Anti-Phishing Application ....................................................................................... 3 1.3 Optimization and Evaluation ................................................................................... 4 1.4 Contributions and Dissertation Outline ................................................................... 5 Chapter 2 Methods for Visually Similar Web Pages Detection ......................................... 7 2.1 Feature-based similarity measures........................................................................... 7 2.2 What can we count on for visual similarity identification? ..................................... 9 Chapter 3 Theoretical Foundation .................................................................................... 14 3.1 Gestalt Theory........................................................................................................ 14 3.2 Inattentional Blindness........................................................................................... 16 3.3 Supersignals ........................................................................................................... 17 3.4 Similarity Metric.................................................................................................... 20 3.4.1 Normalized Compression Distance.................................................................. 21 3.4.2 Compression Algorithms and Supersignals..................................................... 23 Chapter 4 Existing Anti-Phishing Mechanisms................................................................ 25 4.1 Phishing Problem................................................................................................... 25 4.2 Existing Anti-Phishing Solutions........................................................................... 27 4.3 A Key characteristic of Phishing websites............................................................. 30 4.4 Goals, research and hypothesis.............................................................................. 35 Chapter 5 Our Empirical Proof-of-Concept Evaluation ................................................... 37 5.1 The Twelve-pairs Experiment................................................................................ 39 5.1.3 Design and methodology ................................................................................. 39 5.1.4 Interpretation of results.................................................................................... 40 5.2 The Clustering Experiment.................................................................................... 45 5.2.1 Design and methodology ................................................................................. 46 5.2.2 Interpretation of results.................................................................................... 48 5.3 The Large Scale Experiment.................................................................................. 48 5.3.1 Design .............................................................................................................. 49 5.3.2 Methodology.................................................................................................... 51 5.3.3 Interpretation of Results................................................................................... 53 5.4 Effectiveness as an Anti-Phishing Classifier ......................................................... 54 5.5 Robustness against Countermeasures .................................................................... 59 5.5.1 Non-Structural Distortions............................................................................... 62 5.5.2 Structural Distortions....................................................................................... 65 5.6 Other related works...............................................................................................
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