Design and Implementation of Algorithms for Traffic Classification

Traffic analysis is the practice of using inherent characteristics of a network flow such as timings, sizes, and orderings of the packets to derive sensitive information about it. Traffic analysis techniques are used because of the extensive adoption of encryption and content-obfuscation mechanisms, making it impossible to infer any information about the flows by analyzing their content. In this thesis, we use traffic analysis to infer sensitive information for different objectives and different applications. Specifically, we investigate various applications: p2p cryptocurrencies, flow correlation, and messaging applications. Our goal is to tailor specific traffic analysis algorithms that best capture network traffic’s intrinsic characteristics in those applications for each of these applications. Also, the objective of traffic analysis is different for each of these applications. Specifically, in Bitcoin, our goal is to evaluate Bitcoin traffic’s resilience to blocking by powerful entities such as governments and ISPs. Bitcoin and similar cryptocurrencies play an important role in electronic commerce and other trust-based distributed systems because of their significant advantage over traditional currencies, including open access to global e-commerce. Therefore, it is essential to the consumers and the industry to have reliable access to their Bitcoin assets. We also examine stepping stone attacks for flow correlation. A stepping stone is a host that an attacker uses to relay her traffic to hide her identity. We introduce two fingerprinting systems, TagIt and FINN. TagIt embeds a secret fingerprint into the flows by moving the packets to specific time intervals. However, FINN utilizes DNNs to embed the fingerprint by changing the inter-packet delays (IPDs) in the flow. In messaging applications, we analyze the WhatsApp messaging service to determine if traffic leaks any sensitive information such as members’ identity in a particular conversation to the adversaries who watch their encrypted traffic. These messaging applications’ privacy is essential because these services provide an environment to dis- cuss politically sensitive subjects, making them a target to government surveillance and censorship in totalitarian countries. We take two technical approaches to design our traffic analysis techniques. The increasing use of DNN-based classifiers inspires our first direction: we train DNN classifiers to perform some specific traffic analysis task. Our second approach is to inspect and model the shape of traffic in the target application and design a statistical classifier for the expected shape of traffic. DNN- based methods are useful when the network is complex, and the traffic’s underlying noise is not linear. Also, these models do not need a meticulous analysis to extract the features. However, deep learning techniques need a vast amount of training data to work well. Therefore, they are not beneficial when there is insufficient data avail- able to train a generalized model. On the other hand, statistical methods have the advantage that they do not have training overhead

Embedding Strength Criteria for AWGN Watermark, Robust Against Expected Distortion

In this paper we engage in AWGN watermark for grayscale image (the message is embedded by adding of white Gaussian noise matrix; detection is blind, correlation based). We search criteria for ``the best'' (minimal one which guaranties watermark detectability) embedding strength for watermark robust against expected attack. These criteria we find for AWGN watermarks, which are embedded in spatial or in transform domains; for one bit message or for a longer message; into whole image or into some of its coefficients. This paper peculiarity is that we do not propose new watermarking algorithm; for well known, robust algorithm we find the best embedding strength for robust watermark