The zombies strike back: Towards client-side beef detection

A web browser is an application that comes bundled with every consumer operating system, including both desktop and mobile platforms. A modern web browser is complex software that has access to system-level features, includes various plugins and requires the availability of an Internet connection. Like any multifaceted software products, web browsers are prone to numerous vulnerabilities. Exploitation of these vulnerabilities can result in destructive consequences ranging from identity theft to network infrastructure damage. BeEF, the Browser Exploitation Framework, allows taking advantage of these vulnerabilities to launch a diverse range of readily available attacks from within the browser context. Existing defensive approaches aimed at hardening network perimeters and detecting common threats based on traffic analysis have not been found successful in the context of BeEF detection. This paper presents a proof-of-concept approach to BeEF detection in its own operating environment – the web browser – based on global context monitoring, abstract syntax tree fingerprinting and real-time network traffic analysis

Why (and How) Networks Should Run Themselves

The proliferation of networked devices, systems, and applications that we depend on every day makes managing networks more important than ever. The increasing security, availability, and performance demands of these applications suggest that these increasingly difficult network management problems be solved in real time, across a complex web of interacting protocols and systems. Alas, just as the importance of network management has increased, the network has grown so complex that it is seemingly unmanageable. In this new era, network management requires a fundamentally new approach. Instead of optimizations based on closed-form analysis of individual protocols, network operators need data-driven, machine-learning-based models of end-to-end and application performance based on high-level policy goals and a holistic view of the underlying components. Instead of anomaly detection algorithms that operate on offline analysis of network traces, operators need classification and detection algorithms that can make real-time, closed-loop decisions. Networks should learn to drive themselves. This paper explores this concept, discussing how we might attain this ambitious goal by more closely coupling measurement with real-time control and by relying on learning for inference and prediction about a networked application or system, as opposed to closed-form analysis of individual protocols

A framework for analyzing RFID distance bounding protocols

Many distance bounding protocols appropriate for the RFID technology have been proposed recently. Unfortunately, they are commonly designed without any formal approach, which leads to inaccurate analyzes and unfair comparisons. Motivated by this need, we introduce a unied framework that aims to improve analysis and design of distance bounding protocols. Our framework includes a thorough terminology about the frauds, adversary, and prover, thus disambiguating many misleading terms. It also explores the adversary's capabilities and strategies, and addresses the impact of the prover's ability to tamper with his device. It thus introduces some new concepts in the distance bounding domain as the black-box and white-box models, and the relation between the frauds with respect to these models. The relevancy and impact of the framework is nally demonstrated on a study case: Munilla-Peinado distance bounding protocol

Defending against Sybil Devices in Crowdsourced Mapping Services

Real-time crowdsourced maps such as Waze provide timely updates on traffic, congestion, accidents and points of interest. In this paper, we demonstrate how lack of strong location authentication allows creation of software-based {\em Sybil devices} that expose crowdsourced map systems to a variety of security and privacy attacks. Our experiments show that a single Sybil device with limited resources can cause havoc on Waze, reporting false congestion and accidents and automatically rerouting user traffic. More importantly, we describe techniques to generate Sybil devices at scale, creating armies of virtual vehicles capable of remotely tracking precise movements for large user populations while avoiding detection. We propose a new approach to defend against Sybil devices based on {\em co-location edges}, authenticated records that attest to the one-time physical co-location of a pair of devices. Over time, co-location edges combine to form large {\em proximity graphs} that attest to physical interactions between devices, allowing scalable detection of virtual vehicles. We demonstrate the efficacy of this approach using large-scale simulations, and discuss how they can be used to dramatically reduce the impact of attacks against crowdsourced mapping services.Comment: Measure and integratio

The Flow Fingerprinting Game

Linking two network flows that have the same source is essential in intrusion detection or in tracing anonymous connections. To improve the performance of this process, the flow can be modified (fingerprinted) to make it more distinguishable. However, an adversary located in the middle can modify the flow to impair the correlation by delaying the packets or introducing dummy traffic. We introduce a game-theoretic framework for this problem, that is used to derive the Nash Equilibrium. As obtaining the optimal adversary delays distribution is intractable, some approximations are done. We study the concrete example where these delays follow a truncated Gaussian distribution. We also compare the optimal strategies with other fingerprinting schemes. The results are useful for understanding the limits of flow correlation based on packet timings under an active attacker.Comment: Workshop on Information Forensics and Securit