Detecting Traffic Snooping in Anonymity Networks Using Decoys

Anonymous communication networks like Tor partially protect the confidentiality of their users' traffic by encrypting all intra-overlay communication. However, when the relayed traffic reaches the boundaries of the overlay network towards its actual destination, the original user traffic is inevitably exposed. At this point, unless end-to-end encryption is used, sensitive user data can be snooped by a malicious or compromised exit node, or by any other rogue network entity on the path towards the actual destination. We explore the use of decoy traffic for the detection of traffic interception on anonymous proxying systems. Our approach is based on the injection of traffic that exposes bait credentials for decoy services that require user authentication. Our aim is to entice prospective eavesdroppers to access decoy accounts on servers under our control using the intercepted credentials. We have deployed our prototype implementation in the Tor network using decoy IMAP and SMTP servers. During the course of six months, our system detected eight cases of traffic interception that involved eight different Tor exit nodes. We provide a detailed analysis of the detected incidents, discuss potential improvements to our system, and outline how our approach can be extended for the detection of HTTP session hijacking attacks

K-resolver: Towards Decentralizing Encrypted DNS Resolution

Centralized DNS over HTTPS/TLS (DoH/DoT) resolution, which has started being deployed by major hosting providers and web browsers, has sparked controversy among Internet activists and privacy advocates due to several privacy concerns. This design decision causes the trace of all DNS resolutions to be exposed to a third-party resolver, different than the one specified by the user's access network. In this work we propose K-resolver, a DNS resolution mechanism that disperses DNS queries across multiple DoH resolvers, reducing the amount of information about a user's browsing activity exposed to each individual resolver. As a result, none of the resolvers can learn a user's entire web browsing history. We have implemented a prototype of our approach for Mozilla Firefox, and used it to evaluate the performance of web page load time compared to the default centralized DoH approach. While our K-resolver mechanism has some effect on DNS resolution time and web page load time, we show that this is mainly due to the geographical location of the selected DoH servers. When more well-provisioned anycast servers are available, our approach incurs negligible overhead while improving user privacy.Comment: NDSS Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb) 202

Smashing the Gadgets: Hindering Return-Oriented Programming Using In-Place Code Randomization

The wide adoption of non-executable page protections in recent versions of popular operating systems has given rise to attacks that employ return-oriented programming (ROP) to achieve arbitrary code execution without the injection of any code. Existing defenses against ROP exploits either require source code or symbolic debugging information, or impose a significant runtime overhead, which limits their applicability for the protection of third-party applications. In this paper we present in-place code randomization, a practical mitigation technique against ROP attacks that can be applied directly on third-party software. Our method uses various narrow-scope code transformations that can be applied statically, without changing the location of basic blocks, allowing the safe randomization of stripped binaries even with partial disassembly coverage. These transformations effectively eliminate about 10%, and probabilistically break about 80% of the useful instruction sequences found in a large set of PE files. Since no additional code is inserted, in-place code randomization does not incur any measurable runtime overhead, enabling it to be easily used in tandem with existing exploit mitigations such as address space layout randomization. Our evaluation using publicly available ROP exploits and two ROP code generation toolkits demonstrates that our technique prevents the exploitation of the tested vulnerable Windows 7 applications, including Adobe Reader, as well as the automated construction of alternative ROP payloads that aim to circumvent in-place code randomization using solely any remaining unaffected instruction sequences

Shadow Honeypots

We present Shadow Honeypots, a novel hybrid architecture that combines the best features of honeypots and anomaly detection. At a high level, we use a variety of anomaly detectors to monitor all traffic to a protected network or service. Traffic that is considered anomalous is processed by a "shadow honeypot" to determine the accuracy of the anomaly prediction. The shadow is an instance of the protected software that shares all internal state with a regular ("production") instance of the application, and is instrumented to detect potential attacks. Attacks against the shadow are caught, and any incurred state changes are discarded. Legitimate traffic that was misclassified will be validated by the shadow and will be handled correctly by the system transparently to the end user. The outcome of processing a request by the shadow is used to filter future attack instances and could be used to update the anomaly detector. Our architecture allows system designers to fine-tune systems for performance, since false positives will be filtered by the shadow. We demonstrate the feasibility of our approach in a proof-of-concept implementation of the Shadow Honeypot architecture for the Apache web server and the Mozilla Firefox browser. We show that despite a considerable overhead in the instrumentation of the shadow honeypot (up to 20% for Apache), the overall impact on the system is diminished by the ability to minimize the rate of false-positives

CloudFence: Enabling Users to Audit the Use of their Cloud-Resident Data

One of the primary concerns of users of cloud-based services and applications is the risk of unauthorized access to their private information. For the common setting in which the infrastructure provider and the online service provider are different, end users have to trust their data to both parties, although they interact solely with the service provider. This paper presents CloudFence, a framework that allows users to independently audit the treatment of their private data by third-party online services, through the intervention of the cloud provider that hosts these services. CloudFence is based on a fine-grained data flow tracking platform exposed by the cloud provider to both developers of cloud-based applications, as well as their users. Besides data auditing for end users, CloudFence allows service providers to confine the use of sensitive data in well-defined domains using data tracking at arbitrary granularity, offering additional protection against inadvertent leaks and unauthorized access. The results of our experimental evaluation with real-world applications, including an e-store platform and a cloud-based backup service, demonstrate that CloudFence requires just a few changes to existing application code, while it can detect and prevent a wide range of security breaches, ranging from data leakage attacks using SQL injection, to personal data disclosure due to missing or erroneously implemented access control checks

Network Monitoring Session Description

SUMMARY Network Monitoring is a complex distributed activity: we distinguish agents that issue requests and use of the results, other that operate the monitoring activity and produce observations, glued together by other agents that are in charge of routing requests and results. We illustrate a comprehensive view of a such architecture, taking into account scalability and security requirements, concentrating on the definition of the information exchanged between such agents

Prototype Implementation Of A Demand Driven Network Monitoring Architecture

SUMMARY - The capability of dynamically monitoring the perfomance of the communication infrastructure is one of the emerging requirements for a Grid. We claim that such a capability is in fact orthogonal to the more popular collection of data for scheduling and diagnosis, which needs large storage and indexing capabilities, but may disregard real-time performance issues. We discuss such claim analyzing the gLite NPM architecture, and we describe a novel network monitoring infrastructure specifically designed for demand driven monitoring, named gd2, that can be potentially integrated in the gLite framework. We describe a Java implementation of gd2 on a virtual testbed