Visible to the public "P2V: Effective Website Fingerprinting Using Vector Space Representations"Conflict Detection Enabled

Title"P2V: Effective Website Fingerprinting Using Vector Space Representations"
Publication TypeConference Paper
Year of Publication2015
AuthorsK. Alnaami, G. Ayoade, A. Siddiqui, N. Ruozzi, L. Khan, B. Thuraisingham
Conference Name2015 IEEE Symposium Series on Computational Intelligence
Date PublishedDec
ISBN Number978-1-4799-7560-0
Accession Number15718317
Keywordsanonymous communication, Computational modeling, Computer crime, Context, cyber security, data privacy, feature extraction, features extraction, Fingerprint recognition, Internet users, language vector space models, learning (artificial intelligence), machine learning, Mathematical model, natural language processing, online activists, P2V, packet to vector approach, passive traffic analysis attack, pubcrawl, pubcrawl170105, real-valued vector, Servers, users navigation privacy, vector space representations, VSM, Web page destination, Web pages, Web site fingerprinting attack, Web sites, word vector representations

Language vector space models (VSMs) have recently proven to be effective across a variety of tasks. In VSMs, each word in a corpus is represented as a real-valued vector. These vectors can be used as features in many applications in machine learning and natural language processing. In this paper, we study the effect of vector space representations in cyber security. In particular, we consider a passive traffic analysis attack (Website Fingerprinting) that threatens users' navigation privacy on the web. By using anonymous communication, Internet users (such as online activists) may wish to hide the destination of web pages they access for different reasons such as avoiding tyrant governments. Traditional website fingerprinting studies collect packets from the users' network and extract features that are used by machine learning techniques to reveal the destination of certain web pages. In this work, we propose the packet to vector (P2V) approach where we model website fingerprinting attack using word vector representations. We show how the suggested model outperforms previous website fingerprinting works.

Citation Key7376592