FLAIM: A Multi-level Anonymization Framework for Computer and Network Logs

FLAIM (Framework for Log Anonymization and Information Management) addresses two important needs not well addressed by current log anonymizers. First, it is extremely modular and not tied to the specific log being anonymized. Second, it supports multi-level anonymization, allowing system administrators to make fine-grained trade-offs between information loss and privacy/security concerns. In this paper, we examine anonymization solutions to date and note the above limitations in each. We further describe how FLAIM addresses these problems, and we describe FLAIM's architecture and features in detail.Comment: 16 pages, 4 figures, in submission to USENIX Lis

Simpleweb/University of Twente Traffic Traces Data Repository

The computer networks research community lacks of shared measurement information. As a consequence, most researchers need to expend a considerable part of their time planning and executing measurements before being able to perform their studies. The lack of shared data also makes it hard to compare and validate results. This report describes our efforts to distribute a portion of our network data through the Simpleweb/University of Twente Traffic Traces Data Repository

Anonymizing cybersecurity data in critical infrastructures: the CIPSEC approach

Cybersecurity logs are permanently generated by network devices to describe security incidents. With modern computing technology, such logs can be exploited to counter threats in real time or before they gain a foothold. To improve these capabilities, logs are usually shared with external entities. However, since cybersecurity logs might contain sensitive data, serious privacy concerns arise, even more when critical infrastructures (CI), handling strategic data, are involved. We propose a tool to protect privacy by anonymizing sensitive data included in cybersecurity logs. We implement anonymization mechanisms grouped through the definition of a privacy policy. We adapt said approach to the context of the EU project CIPSEC that builds a unified security framework to orchestrate security products, thus offering better protection to a group of CIs. Since this framework collects and processes security-related data from multiple devices of CIs, our work is devoted to protecting privacy by integrating our anonymization approach.Peer ReviewedPostprint (published version

Preserving Both Privacy and Utility in Network Trace Anonymization

As network security monitoring grows more sophisticated, there is an increasing need for outsourcing such tasks to third-party analysts. However, organizations are usually reluctant to share their network traces due to privacy concerns over sensitive information, e.g., network and system configuration, which may potentially be exploited for attacks. In cases where data owners are convinced to share their network traces, the data are typically subjected to certain anonymization techniques, e.g., CryptoPAn, which replaces real IP addresses with prefix-preserving pseudonyms. However, most such techniques either are vulnerable to adversaries with prior knowledge about some network flows in the traces, or require heavy data sanitization or perturbation, both of which may result in a significant loss of data utility. In this paper, we aim to preserve both privacy and utility through shifting the trade-off from between privacy and utility to between privacy and computational cost. The key idea is for the analysts to generate and analyze multiple anonymized views of the original network traces; those views are designed to be sufficiently indistinguishable even to adversaries armed with prior knowledge, which preserves the privacy, whereas one of the views will yield true analysis results privately retrieved by the data owner, which preserves the utility. We present the general approach and instantiate it based on CryptoPAn. We formally analyze the privacy of our solution and experimentally evaluate it using real network traces provided by a major ISP. The results show that our approach can significantly reduce the level of information leakage (e.g., less than 1\% of the information leaked by CryptoPAn) with comparable utility

Dynamic 3D Network Data Visualization

Monitoring network traffic has always been an arduous and tedious task because of the complexity and sheer volume of network data that is being consistently generated. In addition, network growth and new technologies are rapidly increasing these levels of complexity and volume. An effective technique in understanding and managing a large dataset, such as network traffic, is data visualization. There are several tools that attempt to turn network traffic into visual stimuli. Many of these do so in 2D space and those that are 3D lack the ability to display network patterns effectively. Existing 3D network visualization tools lack user interaction, dynamic generation, and intuitiveness. This project proposes a user-friendly 3D network visualization application that creates both dynamic and interactive visuals. This application was built using the Bablyon.js graphics framework and uses anonymized data collected from a campus network

HORNET: High-speed Onion Routing at the Network Layer

We present HORNET, a system that enables high-speed end-to-end anonymous channels by leveraging next generation network architectures. HORNET is designed as a low-latency onion routing system that operates at the network layer thus enabling a wide range of applications. Our system uses only symmetric cryptography for data forwarding yet requires no per-flow state on intermediate nodes. This design enables HORNET nodes to process anonymous traffic at over 93 Gb/s. HORNET can also scale as required, adding minimal processing overhead per additional anonymous channel. We discuss design and implementation details, as well as a performance and security evaluation.Comment: 14 pages, 5 figure