Connectionists: NIPS 2007 WORKSHOP: Machine Learning in Adversarial Environments for Computer Security

[Pavel Laskov]

                *** Apologies for multiple posting ***

========================= CALL FOR ABSTRACTS =========================

                          NIPS 2007 Workshop on
               Machine Learning in Adversarial Environments
                          for Computer Security

	               7 or 8 December, 2007
		    Whistler, British Columbia, Canada

              Supported by the PASCAL network of excellence
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Organizers:
Pavel Laskov (Fraunhofer Institute FIRST, Germany)
Richard Lippmann (MIT Lincoln Laboratory, USA)

Deadlines:
Extended abstract submission:   October 19, 2007
Notification of acceptance:     November 2, 2007

Contact:
Email:            mls-nips07 at first.fraunhofer.de
Web page:         mls-nips07.first.fraunhofer.de

Computer and network security has become an important research area
due to the alarming recent increase in hacker activity motivated by
profit and both ideological and national conflicts. Increases in spam,
botnets, viruses, malware, key loggers, software vulnerabilities,
zero-day exploits and other threats contribute to growing concerns
about security. In the past few years, many researchers have begun to
apply machine learning techniques to these and other security
problems. Security, however, is a difficult area because adversaries
actively manipulate training data and vary attack techniques to defeat
new systems. A main purpose of this workshop is examine adversarial
machine learning problems across different security applications to
see if there are common problems, effective solutions, and theoretical
results to guide future research, and to determine if machine learning
can indeed work well in adversarial environments.  Another purpose is
to initiate a dialog between computer security and machine learning
researchers already working on various security applications, and to
draw wider attention to computer security problems in the NIPS
community.

The workshop will consist of invited and contributed presentations as
well as panel discussions. Contributions are sought where researchers
are applying machine learning to various areas of computer security
including but not limited to the following:

* Anomaly detection                     * Automatic signature generation
* Intrusion detection                      * Learning adversary behavior
* Spam detection            * Performance evaluation of adaptive systems
* Software vulnerability discovery       * Learning with malicious noise
* Adversary modeling             * Hiding and detecting virtual machines
* Adapting to adversarial behavior                   * Rootkit detection

Submissions should be no longer than two pages and include author
contact information and appropriate references to other work.
Submissions can contain original contributions as well as summarize
prior and recent work. Submissions should be aimed at initiating
fruitful discussion of critical issues related to machine learning and
computer security, for example by raising controversial issues,
sharing open problems, and comparing competing approaches.

A limited number of presentations will be given 12 minute oral
presentation slots, the remaining accepted submissions will be
presented as posters with a 3 minute spotlight. Authors of selected
oral presentations are encouraged to present further details as a
poster. Substantial time will be reserved for questions and
discussion.

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   Pavel Laskov, Ph.D.

   Fraunhofer Institute FIRST IDA
   Kekulestr. 7, 12489 Berlin 

   tel: +49 30 6392 1870
   fax: +49 30 6392 1805
   email: pavel.laskov at first.fraunhofer.de

   University of Tuebingen WSI-RI
   Sand 13, 72076 Tuebingen
   tel: +49 7071 29 70574
   email: laskov at ri.uni-tuebingen.de

   http://ida.first.fhg.de/~laskov/
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