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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

D23/D5.3 Early Warning System: Experimental report

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A large part of Workpackage 5 concerns the Early Warning System functionality. This deliverable offers a report of the experiments carried out as part of the effort to create the Early Warning System. Several specialized alerting systems are presented, including FIRE, Exposure, BANOMAD and HoneyBuddy myIMhoneypot


FP7-ICT-216026-Wombat_WP5_D23_V01_Early-warning-system-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

Wombat Deliverable D13/D3.3 Sensor Deployment

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This deliverable reports the deployment of all types of sensors implemented in the WOMBAT project and includes descriptions of experiences with the sensors from several months of deployment and experimentation. The sensors that are deployed are the SGNET, HARMUR, Shelia, Paranoid Android, HoneySpider Network, Bluebat and NoAH. The early experiences show that the WOMBAT Project is fulfilling our preliminary expectations about having powerful tools for collecting data. These data are useful for categorizing attackers and malware behaviors. Moreover our experiments reveal that the sensors can cooperate with each other, enriching in this way the information offered for analysis.

FP7-ICT-216026-Wombat_WP3_D13_V01-Sensor-deployment.pdf

WOMBAT Participation at the FIA Conference in Madrid, Dec. 2008

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The WOMBAT proect will be represented at the Future Internet Assembly conference in Madrid, December 2008, by the following people:
  • Vincent Boutroux, France Télécom R&D/Orange Labs
  • Sotiris Ioannidis, FORTH (also representing FORWARD)
  • Philip Homburg, VU (Also representing FORWARD)
  • Paolo Milani Comparetti, TUV

Wombat Deliverable D06/D3.1 Infrastructure Design

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This document contains a description of the wombat architecture and a high level design
of the new sensors. The wombat architecture is covered by a comprehensive review of
all its components. Part of this architecture is also the data sources and especially the
new ones that will be implemented as part of the wombat project. Each of them will
be described in the design level, focusing on the way that they will be integrated with
the wombat infrastructure

FP7-ICT-216026-Wombat-WP3-D06_V02_Infrastructure_design.pdf

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.