ENHANCING THE PERFORMANCE AND SECURITY OF ANONYMOUS COMMUNICATION NETWORKS

Abstract

With the increasing importance of the Internet in our daily lives, the private information of millions of users is prone to more security risks. Users data are collected either for commercial purposes and sold by service providers to marketeers or political purposes and used to track people by governments, or even for personal purposes by hackers. Protecting online users privacy has become a more pressing matter over the years. To this end, anonymous communication networks were developed to serve this purpose. Tors anonymity network is one of the most widely used anonymity networks online; it consists of thousands of routers run by volunteers. Tor preserves the anonymity of its users by relaying the traffic through a number of routers (called onion routers) forming a circuit. Tor was mainly developed as a low-latency network to support interactive applications such as web browsing and messaging applications. However, due to some deficiencies in the original design of Tors network, the performance is affected to the point that interactive applications cannot tolerate it. In this thesis, we attempt to address a number of the performance-limiting issues in Tor networks design. Several researches proposed changes in the transport design to eliminate the effect of these problems and improve the performance of Tors network. In our work, we propose "QuicTor," an improvement to the transport layer of Tors network by using Googles protocol "QUIC" instead of TCP. QUIC was mainly developed to eliminate TCPs latency introduced from the handshaking delays and the head-of-line blocking problem. We provide an empirical evaluation of our proposed design and compare it to two other proposed designs, IMUX and PCTCP.We show that QuicTor significantly enhances the performance of Tors network. Tor was mainly developed as a low-latency network to support interactive web browsing and messaging applications. However, a considerable percentage of Tor traffic is consumed by bandwidth acquisitive applications such as BitTorrent. This results in an unfair allocation of the available bandwidth and significant degradation in the Quality-of-service (QoS) delivered to users. In this thesis, we present a QoS-aware deep reinforcement learning approach for Tors circuit scheduling (QDRL). We propose a design that coalesces the two scheduling levels originally presented in Tor and addresses it as a single resource allocation problem. We use the QoS requirements of different applications to set the weight of active circuits passing through a relay. Furthermore, we propose a set of approaches to achieve the optimal trade-off between system fairness and efficiency. We designed and implemented a reinforcement-learning-based scheduling approach (TRLS), a convex-optimization-based scheduling approach (CVX-OPT), and an average-rate-based proportionally fair heuristic (AR-PF). We also compared the proposed approaches with basic heuristics and with the implemented scheduler in Tor. We show that our reinforcement-learning-based approach (TRLS) achieved the highest QoS-aware fairness level with a resilient performance to the changes in an environment with a dynamic nature, such as the Tor network