1,310 research outputs found

    Spoiled Onions: Exposing Malicious Tor Exit Relays

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    Several hundred Tor exit relays together push more than 1 GiB/s of network traffic. However, it is easy for exit relays to snoop and tamper with anonymised network traffic and as all relays are run by independent volunteers, not all of them are innocuous. In this paper, we seek to expose malicious exit relays and document their actions. First, we monitored the Tor network after developing a fast and modular exit relay scanner. We implemented several scanning modules for detecting common attacks and used them to probe all exit relays over a period of four months. We discovered numerous malicious exit relays engaging in different attacks. To reduce the attack surface users are exposed to, we further discuss the design and implementation of a browser extension patch which fetches and compares suspicious X.509 certificates over independent Tor circuits. Our work makes it possible to continuously monitor Tor exit relays. We are able to detect and thwart many man-in-the-middle attacks which makes the network safer for its users. All our code is available under a free license

    Web Tracking: Mechanisms, Implications, and Defenses

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    This articles surveys the existing literature on the methods currently used by web services to track the user online as well as their purposes, implications, and possible user's defenses. A significant majority of reviewed articles and web resources are from years 2012-2014. Privacy seems to be the Achilles' heel of today's web. Web services make continuous efforts to obtain as much information as they can about the things we search, the sites we visit, the people with who we contact, and the products we buy. Tracking is usually performed for commercial purposes. We present 5 main groups of methods used for user tracking, which are based on sessions, client storage, client cache, fingerprinting, or yet other approaches. A special focus is placed on mechanisms that use web caches, operational caches, and fingerprinting, as they are usually very rich in terms of using various creative methodologies. We also show how the users can be identified on the web and associated with their real names, e-mail addresses, phone numbers, or even street addresses. We show why tracking is being used and its possible implications for the users (price discrimination, assessing financial credibility, determining insurance coverage, government surveillance, and identity theft). For each of the tracking methods, we present possible defenses. Apart from describing the methods and tools used for keeping the personal data away from being tracked, we also present several tools that were used for research purposes - their main goal is to discover how and by which entity the users are being tracked on their desktop computers or smartphones, provide this information to the users, and visualize it in an accessible and easy to follow way. Finally, we present the currently proposed future approaches to track the user and show that they can potentially pose significant threats to the users' privacy.Comment: 29 pages, 212 reference

    Computational Resource Abuse in Web Applications

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    Internet browsers include Application Programming Interfaces (APIs) to support Web applications that require complex functionality, e.g., to let end users watch videos, make phone calls, and play video games. Meanwhile, many Web applications employ the browser APIs to rely on the user's hardware to execute intensive computation, access the Graphics Processing Unit (GPU), use persistent storage, and establish network connections. However, providing access to the system's computational resources, i.e., processing, storage, and networking, through the browser creates an opportunity for attackers to abuse resources. Principally, the problem occurs when an attacker compromises a Web site and includes malicious code to abuse its visitor's computational resources. For example, an attacker can abuse the user's system networking capabilities to perform a Denial of Service (DoS) attack against third parties. What is more, computational resource abuse has not received widespread attention from the Web security community because most of the current specifications are focused on content and session properties such as isolation, confidentiality, and integrity. Our primary goal is to study computational resource abuse and to advance the state of the art by providing a general attacker model, multiple case studies, a thorough analysis of available security mechanisms, and a new detection mechanism. To this end, we implemented and evaluated three scenarios where attackers use multiple browser APIs to abuse networking, local storage, and computation. Further, depending on the scenario, an attacker can use browsers to perform Denial of Service against third-party Web sites, create a network of browsers to store and distribute arbitrary data, or use browsers to establish anonymous connections similarly to The Onion Router (Tor). Our analysis also includes a real-life resource abuse case found in the wild, i.e., CryptoJacking, where thousands of Web sites forced their visitors to perform crypto-currency mining without their consent. In the general case, attacks presented in this thesis share the attacker model and two key characteristics: 1) the browser's end user remains oblivious to the attack, and 2) an attacker has to invest little resources in comparison to the resources he obtains. In addition to the attack's analysis, we present how existing, and upcoming, security enforcement mechanisms from Web security can hinder an attacker and their drawbacks. Moreover, we propose a novel detection approach based on browser API usage patterns. Finally, we evaluate the accuracy of our detection model, after training it with the real-life crypto-mining scenario, through a large scale analysis of the most popular Web sites

    Dovetail: Stronger Anonymity in Next-Generation Internet Routing

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    Current low-latency anonymity systems use complex overlay networks to conceal a user's IP address, introducing significant latency and network efficiency penalties compared to normal Internet usage. Rather than obfuscating network identity through higher level protocols, we propose a more direct solution: a routing protocol that allows communication without exposing network identity, providing a strong foundation for Internet privacy, while allowing identity to be defined in those higher level protocols where it adds value. Given current research initiatives advocating "clean slate" Internet designs, an opportunity exists to design an internetwork layer routing protocol that decouples identity from network location and thereby simplifies the anonymity problem. Recently, Hsiao et al. proposed such a protocol (LAP), but it does not protect the user against a local eavesdropper or an untrusted ISP, which will not be acceptable for many users. Thus, we propose Dovetail, a next-generation Internet routing protocol that provides anonymity against an active attacker located at any single point within the network, including the user's ISP. A major design challenge is to provide this protection without including an application-layer proxy in data transmission. We address this challenge in path construction by using a matchmaker node (an end host) to overlap two path segments at a dovetail node (a router). The dovetail then trims away part of the path so that data transmission bypasses the matchmaker. Additional design features include the choice of many different paths through the network and the joining of path segments without requiring a trusted third party. We develop a systematic mechanism to measure the topological anonymity of our designs, and we demonstrate the privacy and efficiency of our proposal by simulation, using a model of the complete Internet at the AS-level

    JShelter: Give Me My Browser Back

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    The Web is used daily by billions. Even so, users are not protected from many threats by default. This position paper builds on previous web privacy and security research and introduces JShelter, a webextension that fights to return the browser to users. Moreover, we introduce a library helping with common webextension development tasks and fixing loopholes misused by previous research. JShelter focuses on fingerprinting prevention, limitations of rich web APIs, prevention of attacks connected to timing, and learning information about the computer, the browser, the user, and surrounding physical environment and location. We discovered a loophole in the sensor timestamps that lets any page observe the device boot time if sensor APIs are enabled in Chromium-based browsers. JShelter provides a fingerprinting report and other feedback that can be used by future security research and data protection authorities. Thousands of users around the world use the webextension every day

    PREDICTING THE UNKNOWN: MACHINE LEARNING TECHNIQUES FOR VIDEO FINGERPRINTING ATTACKS OVER TOR

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    In recent years, anonymization services such as Tor have become a popular resource for terrorist organizations and violent extremist groups. These adversaries use Tor to access the Dark Web to distribute video media as a way to recruit, train, and incite violence and acts of terrorism worldwide. This research strives to address this issue by examining and analyzing the use and development of video fingerprinting attacks using deep learning models. These high-performing deep learning models are called Deep Fingerprinting, which is used to predict video patterns with high accuracy in a closed-world setting. We pose ourselves as the adversary by passively observing raw network traffic as a user downloads a short video from YouTube. Based on traffic patterns, we can deduce what video the user was streaming with higher accuracy than previously obtained. In addition, our results include identifying the genre of the video. Our results suggest that an adversary may predict the video a user downloads over Tor with up to 83% accuracy, even when the user applies additional defenses to protect online privacy. By comparing different Deep Fingerprinting models with one another, we can better understand which models perform better from both the attacker and user’s perspective.Lieutenant, United States NavyApproved for public release. Distribution is unlimited
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