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D24/D6.4 Second Open Workshop Proceedings

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This is the deliverable for the second wombat open workshop, BADGERS, that took place within the EuroSys 2011 conference on April 10 in Salzburg (Austria). In this document we discuss the preparation of the second workshop, our expectations vs. feedback and impressions we collected by authors and attenders. Proceedings are included.


FP7-ICT-216026-Wombat_WP6_D24_V01_Second-Open-Workshop-Proceedings-BADGERS-2011.pdf

D22/D5.2 Root Causes Analysis: Experimental Report

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This deliverable offers an extensive report of all experiments carried out with respect to root cause analysis techniques. This final deliverable for Workpackage 5 (Threats Intelligence ) builds upon D12 (D5.1 - Technical Survey on Root Cause Analysis) and benefits from the modifications made to the various software modules developed in WP4, following up the experimental feedback.
The R&D efforts carried out in WP5 with respect to root cause analysis have produced a novel framework for attack attribution called triage. This framework has been successfully applied to various wombat datasets to perform intelligence analyses by taking advantage of several structural and contextual features of the data sets developed by the different partners. These experiments enabled us to get insights into the underlying root phenomena that have likely caused many security events observed by sensors deployed by wombat partners.
In this deliverable, we provide an in-depth description of experimental results obtained with triage, in particular with respect to (i) the analysis of Rogue AV campaigns (based on  HARMUR data), and (ii) the analysis of different malware variants attributed to the Allaple malware family (based on data from SGNET, VirusTotal and Anubis).
Finally, we describe another experiment performed on a large spam data set obtained from Symantec.Cloud (formerly MessageLabs), for which triage was successfully used to analyze spam botnets and their ecosystem, i.e., how those botnets are used by spammers to organize and coordinate their spam campaigns. Thanks to this application, we are considering a possible technology transfer of triage to Symantec.Cloud, who is interested in carrying out regular intelligence analyses of their spam data sets, and may ralso consider the integration of triage to their Skeptic ○ spam filtering technology.



FP7-ICT-216026-Wombat_WP5_D22_V01_Root-Cause-Analysis-Experimental-report.pdf

D21/D4.7 Consolidated report with evaluation results

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This is the final deliverable for Workpackage 4 within the wombat project. In this document we discuss the final extensions and improvements to our data collection and analysis techniques that were implemented as part of wombat. Furthermore, we present some additional results obtained from the analysis of data collected within wombat.


FP7-ICT-216026-Wombat_WP4_D21_V01_Consolidated-reports-with-evaluation-results.pdf

The Wombat API (WAPI) is now available on sourceforge

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WAPI, or WOMBAT API, is a SOAP-based API built in the context of the project to facilitate the remote access and exploration of security-related datasets.

The package contains all the essential code to start using the WAPI. The WAPI represents an attempt to tackle two main challenges for security data providers:

- Many of the data access primitives are not easily scriptable. Many data sources provide web-based interfaces that, while easily accessible by human operators, are not convenient for automated analysis.

- The interfaces for security datasets are very diverse in structure and methodology. The analyst who wants to take advantage of multiple data sources to perform correlations among them is thus forced to implement ad-hoc plugins and parsers for each data feed. This process is not necessarily a simple task, and requires the analyst to fully understand, for example, the schema of the SQL database provided by the data owner.



You can find the package on sourceforge : http://sourceforge.net/projects/wombat-api/


More information and details on WAPI are available in the deliverable D10/D6.3.

Wombat Deliverable D18/D4.6 Final description of contextual features

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The objective of Workpackage 4 is to develop techniques to characterize the malicious
code that is collected in the previous workpackage. The main idea is to enrich the
collected code thanks to metadata that might reveal insights into the origin of the code
and the intentions of those that created, released or used it.
This deliverable is an extension of D15 (D4.5), and provides a final description of the
contextual features collected within the wombat consortium. Furthermore, it presents
initial results, statistics, and insights obtained by analyzing the collected contextual
features.

FP7-ICT-216026-Wombat_WP4-D18_V01_Final-Contextual-features.pdf

Lecture at ZISC by Marc Dacier from Symantec

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Marc Dacier from symantec has presented a one hour lecture at the ZISC Information Security colloquium (http://www.zisc.ethz.ch/events/infseccolloquium_FS2009) including pointers to WOMBAT.

In order to assure accuracy and realism of resilience assessment methods and tools, it is essential to have access to field data that are unbiased and representative. Several initiatives are taking place that offer access to malware samples for research purposes. Papers are published where techniques have been assessed thanks to these samples. Definition of benchmarking datasets is the next step ahead. In this presentation, we report on the lessons learned while collecting and analyzing malware samples in a large scale collaborative effort. Three different environments are described and their integration used to highlight the open issues that remain with such data collection. Three main lessons are offered to the reader. First, creation of representative malware samples datasets is probably an impossible task. Second, false negative alerts are not what we think they are. Third, false positive alerts exist where we were not used to see them. These three lessons have to be taken into account by those who want to assess the resilience of techniques with respect to malicious faults.

These are the results of a joint work carried out in the context of the European funded WOMBAT project, together with partners from Hispasec Systemas, EURECOM institute and Symantec Research Labs Europe (see http://wombat-project.eu/ for more on the WOMBAT project). Zurich_ZISC_presentation.pdf

WOMBAT participation at the ICT 2008 Conference in Lyon

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The WOMBAT project will be represented by the following people at the ICT 2008 Conference:
  • Vincent Boutroux, France Télécom R&D/Orange Labs
  • Marc Dacier, Symantec

PhD Defense of Corrado Leita

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M. Corrado LEITA will publicly defend his UNS Doctoral Thesis 
on Thursday, December 4th 2008 at 2:00 pm, in the Amphitheater MARCONI at EURECOM.

Topic of the Thesis:

"SGNET: automated protocol learning for the observation of malicious threats"

Jury members :

  • Marc DACIER (Symantec)
  • Vern PAXSON (ICSI)
  • Hervé DEBAR (France Télécom R&D/Orange Labs)
  • Engin KIRDA (Eurecom)
  • Christopher KRUEGEL (UCSB)
  • Mohamed KAANICHE (LAAS CNRS)
  • Sotiris IOANNIDIS (FORTH)

One of the main prerequisites for the development of reliable defenses to protect a network resource consists in the collection of quantitative data on  Internet threats. This attempt to "know your enemy" leads to an increasing interest in the collection and exploitation of datasets providing intelligence on network attacks. The creation of these datasets is a very challenging task. The challenge derives from the need to cope with the spatial and quantitative diversity of malicious activities. The observations need to be performed on a broad perspective, since the activities are not uniformly distributed over the IP space. At the same time, the data collectors need to be sophisticated enough to extract a sufficient amount of information on each activity and perform meaningful inferences. How to combine the simultaneous need to deploy a vast number of data collectors with the need of sophistication required to make meaningful observations? This work addresses this challenge by proposing a protocol learning technique based on bioinformatics algorithms. The proposed technique allows to automatically generate low-cost protocol responders starting from a set of samples of network interaction. Its characteristics are exploited in a distributed honeypot deployment that collected information on Internet attacks for a period of 8 months in 23 different networks distributed all over the world (Europe, Australia, United States). This information is organized in a central dataset enriched with contextual information from a number of sources and analysis tools. Simple data mining techniques proposed in this work allow the generation of a valuable overview on the propagation techniques employed by nowadays malware.

Symantec announces participation to the WOMBAT project

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