Tor fingerprinting using supervised machine learning

مقدم أطروحة جامعية

al-Mubayyid, Ala al-Din

مشرف أطروحة جامعية

Hadi, Ali
al-Atum, Jalal Umar

أعضاء اللجنة

Ujan, Arafat
Umar, Khamis

الجامعة

جامعة الأميرة سمية للتكنولوجيا

الكلية

كلية الملك الحسين لعلوم الحوسبة

القسم الأكاديمي

قسم علم الحاسوب

دولة الجامعة

الأردن

الدرجة العلمية

ماجستير

تاريخ الدرجة العلمية

2014

الملخص الإنجليزي

Tor is the low-latency anonymity tool and short of “ ” one of the prevalent used open source anonymity tools for anonymizing TCP traffic on the Internet used by around 500,000 people every day.

Tor protects ’ k x difficult for an observer to correlate visited websites in the Internet with the real physical-world identity.

Tor accomplished that by ensuring adequate protection of Tor traffic against traffic analysis and feature extraction techniques.

Further, Tor ensures anti-website fingerprinting by implementing different defenses like TLS encryption, padding, and packet relaying.

However, in this thesis, an analysis has been performed against Tor from a local observer in order to bypass Tor protections, the method consists of a feature extraction from a l w k w ’ possible for a local observer to fingerprint top monitored sites on Alexa and Tor traffic can be classified amongst other HTTPS traffic in the network ’ x ent, several supervised machine-learning algorithms have been employed.

The attack assumes a local observer sitting on a local network fingerprinting top 100 site on Alexa, results gave an improvement amongst previous results by achieving an accuracy of 99.6% and 0.01% false positive.

التخصصات الرئيسية

تكنولوجيا المعلومات وعلم الحاسوب

الموضوعات

• الإجراءات الأمنية

عدد الصفحات

146

قائمة المحتويات

• APA
• MLA
• AMA

لغة النص

الإنجليزية

نوع البيانات

رسائل جامعية

رقم السجل

BIM-413618