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Machine Learning and Data Mining for Computer Security
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Machine Learning and Data Mining for Computer Security
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Marcus A. Maloof (Ed.)
Machine Learning
and Data Mining for
Computer Security
Methods and Applications
With 23 Figures
FM.qxd 07/16/2005 11:24 AM Page iii
Marcus A. Maloof, BS, MS, PhD
Department of Computer Science
Georgetown University
Washington DC 20057-1232
USA
British Library Cataloguing in Publication Data
A catalogue record for this book is available from the British Library
Library of Congress Control Number: 2005928487
Advanced Information and Knowledge Processing ISSN 1610-3947
ISBN-10: 1-84628-029-X
ISBN-13: 978-1-84628-029-0
Printed on acid-free paper
© Springer-Verlag London Limited 2006
Apart from any fair dealing for the purposes of research or private study, or criticism or review,
as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be
reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing
of the publishers, or in the case of reprographic reproduction in accordance with the terms of licences
issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms
should be sent to the publishers.
The use of registered names, trademarks, etc. in this publication does not imply, even in the absence of
a specific statement, that such names are exempt from the relevant laws and regulations and therefore
free for general use.
The publisher makes no representation, express or implied, with regard to the accuracy of the
information contained in this book and cannot accept any legal responsibility or liability for any
errors or omissions that may be made.
Printed in the United States of America (MVY)
9 8 7 6 5 4 3 2 1
Springer Science+Business Media
springeronline.com
FM.qxd 07/16/2005 11:24 AM Page iv
To my mom and dad, Ann and Ferris
Foreword
When I first got into information security in the early 1970s, the little research
that existed was focused on mechanisms for preventing attacks. The goal was
airtight security, and much of the research by the end of decade and into the
next focused on building systems that were provably secure. Although there
was widespread recognition that insiders with legitimate access could always
exploit their privileges to cause harm, the prevailing sentiment was that we
could at least design systems that were not inherently faulty and vulnerable
to trivial attacks by outsiders.
We were wrong. This became rapidly apparent to me as I witnessed the
rapid evolution of information technology relative to progress in information
security. The quest to design the perfect system could not keep up with market
demands and developments in personal computers and computer networks. A
few Herculean efforts in industry did in fact produce highly secure systems,
but potential customers paid more attention to applications, performance, and
price. They bought systems that were rich in functionality, but riddled with
holes. The security on the Internet was aptly compared to “Swiss cheese.”
Today, it is widely recognized that our computers and networks are unlikely
to ever be capable of preventing all attacks. They are just way too complex.
Thousands of new vulnerabilities are reported to the Computer Emergency
Response Team Coordination Center (CERT/CC) annually. We might signifi-
cantly reduce the security flaws through good software development practices,
but we cannot expect foolproof security as technology continues to advance
at breakneck speeds. Further, the problems do not reside solely with the ven-
dors; networks must also be properly configured and managed. This can be
a daunting task given the vast and growing number of products that can be
networked together and interact in unpredictable ways.
In the middle 1980s, a small group of us at SRI International began inves-
tigating an alternative approach to security. Recognizing the limitations of a
strategy based solely on prevention, we began to design a system that could
detect intrusions and insider abuse in real time as they occurred. Our research
and that of others led to the development of intrusion detection systems. Also
VIII Foreword
in the 1980s, computer viruses and worms emerged as a threat, leading to
software tools for detecting their presence. These two types of detection tech-
nologies have been largely separate but complementary. Intrusion detection
systems focus on detecting malicious computer and network activity, while
antiviral tools focus on detecting malicious code in files and messages.
To succeed, a detection system must know what to look for. This has been
easier to achieve with viral detection than intrusion detection. Most antiviral
tools work off a list containing the “signatures” of known viruses, worms, and
Trojan horses. If any of the signatures are detected during a scan, the file
or message is flagged. The main limitation of these tools is that they cannot
detect new forms of malicious code that do match the existing signatures.
Vendors mitigate the exposure of their customers by frequently updating and
distributing their signature files, but there remains a period of vulnerability
that has yet to be closed.
With intrusion detection, it is more difficult to know what to look for,
as unauthorized activity on a system can take so many forms and even re-
semble legitimate activity. In an attempt to not miss something that is po-
tentially malicious, many of the existing systems sound far too many false or
inconsequential alarms (often thousands per day), substantially reducing their
effectiveness. Without a means of breaking through the false-alarm barrier,
intrusion detection will fail to meet its promise.
This brings me to this book. The authors have made significant progress in
our ability to distinguish malicious activity and code from that which is not.
This progress has come from bringing machine learning and data mining to
the detection task. These technologies offer a way past the false-alarm barrier
and towards more effective detection systems.
The papers in this book address one of the most exciting areas of research
in information security today. They make an important contribution to that
area and will help pave the way towards more secure systems.
Monterey, CA Dorothy E. Denning
January 2005
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