DOCSLIB.ORG

The Monitoring and Early Detection of Internet Worms Cliff C

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
The Monitoring and Early Detection of Internet Worms Cliff C IEEE/ACM TRANSACTIONS ON NETWORKING, VOL. 13, NO. 5, OCTOBER 2005 961 The Monitoring and Early Detection of Internet Worms Cliff C. Zou, Member, IEEE, Weibo Gong, Fellow, IEEE, Don Towsley, Fellow, IEEE, and Lixin Gao, Member, IEEE Abstract—After many Internet-scale worm incidents in re- scale worm-monitoring and early-warning system. (The U.S. cent years, it is clear that a simple self-propagating worm can Department of Homeland Security launched a “Cybersecurity quickly spread across the Internet and cause severe damage to Monitoring Project” in October 2003 [40]). our society. Facing this great security threat, we need to build an early detection system that can detect the presence of a worm A straightforward way to detect an unknown (zero-day) in the Internet as quickly as possible in order to give people worm is to use various anomaly detection systems. There are accurate early warning information and possible reaction time many well-studied methods or systems in the anomaly “intru- for counteractions. This paper first presents an Internet worm sion detection” research area, for example, the “IDES” [13], monitoring system. Then, based on the idea of “detecting the trend, not the burst” of monitored illegitimate traffic, we present “NIDES” [5] and “eBayes” [39] from SRI International; the a “trend detection” methodology to detect a worm at its early anomaly intrusion detection method [15] based on “sequences propagation stage by using Kalman filter estimation, which is of system calls”; the automatic model-construction intrusion robust to background noise in the monitored data. In addition, for detection system based on data-mining of audit data [24], etc. uniform-scan worms such as Code Red, we can effectively predict Anomaly intrusion-detection systems usually concentrate on the overall vulnerable population size, and estimate accurately how many computers are really infected in the global Internet detecting attacks initiated by hackers. In the case of Internet based on the biased monitored data. For monitoring a nonuniform worm detection, we find that we can take advantage of the dif- scan worm, especially a sequential-scan worm such as Blaster, we ference between a worm’s propagation and a hacker’s intrusion show that it is crucial for the address space covered by the worm attack. A worm code exhibits simple attack behaviors; all com- monitoring system to be as distributed as possible. puters infected by a worm send out infection traffic that has Index Terms—Computer network security, early detection, In- similar statistical characteristics. Moreover, a worm’s propaga- ternet worm, network monitoring. tion in the Internet usually follows some dynamic models be- cause of its large-scale distributed infection. On the other hand, I. INTRODUCTION a hacker’s intrusion attack, which is more complicated, usually targets one or a set of specific computers and does not follow INCE the Morris worm in 1988 [33], the security threat any well-defined dynamic model in most cases. S posed by worms has steadily increased, especially in the last several years. Code Red appeared on July 19, 2001 [27], Based on this observation, we present a new detection which began the new wave of Internet-scale worm attacks. methodology, “trend detection,” by using the principle “de- After that, Code Red II, Nimda, Slammer, Blaster, Sasser, and tecting monitored traffic trend, not burst” [45]. Our “trend Witty have repeatedly attacked the Internet [9] and caused great detection” system attempts to detect the dynamic trend of mon- damage to our society. itored traffic based on the fact that, at the early stage, a worm Currently, some organizations and security companies, such propagates exponentially with a constant, positive exponential as the CERT, CAIDA, and SANS Institute [7], [8], [32], are rate. The “trend” we try to detect is the exponential growth monitoring the Internet and paying close attention to any ab- trend of monitored traffic. normal traffic. When they observe abnormal network activi- Based on worm propagation dynamic models, we detect the ties, their security experts immediately analyze these incidents. presence of a worm in its early propagation stage by using the Given the fast-spreading nature of Internet worms and their se- Kalman filter estimation algorithm, which is robust to back- vere damage to our society, it is necessary to set up a nation- ground noise existing in the monitored data. The Kalman filter is activated when the monitoring system encounters a surge of illegitimate scan activities. If the infection rate estimated by the Manuscript received February 13, 2004; revised August 17, 2004; approved by IEEE/ACM TRANSACTIONS ON NETWORKING Editor V. Paxson. This Kalman filter, which is also the exponential growth rate of a work was supported in part by the Army Research Office under Contract worm’s propagation at its early stage, stabilizes and oscillates DAAD19-01-1-0610, the Defense Advanced Research Projects Agency under slightly around a constant positive value, we claim that the il- Contract DOD F30602-00-0554, the National Science Foundation under Grants EIA-0080119, ANI9980552, and ANI-0208116, and the Air Force Research legitimate scan activities are mainly caused by a worm, even if Laboratory. the estimated worm infection rate is still not well converged. C. C. Zou is with the School of Computer Science, University of Central If the monitored traffic is caused by nonworm noise, the traffic Florida, Orlando, FL 32816-2362 USA (e-mail: [email protected]). W. Gong and L. Gao are with the Department of Electrical and Computer will not have the exponential growth trend, and the estimated Engineering, University of Massachusetts, Amherst, MA 01003 USA (e-mail: value of the infection rate would converge to zero or oscillate [email protected]; [email protected]). around zero. In other words, the Kalman filter is used to detect D. Towsley is with the Department of Computer Science, University of Mass- achusetts, Amherst, MA 01003-9264 USA (e-mail: [email protected]). the presence of a worm by detecting the trend, not the burst,of Digital Object Identifier 10.1109/TNET.2005.857113 the observed illegitimate traffic. In this way, the noisy illegiti- 1063-6692/$20.00 © 2005 IEEE 962 IEEE/ACM TRANSACTIONS ON NETWORKING, VOL. 13, NO. 5, OCTOBER 2005 mate traffic in the Internet we observe everyday will not cause the log data collected from intrusion detection sensors or fire- too many false alarms in our detection system. walls for current monitoring systems. In addition, we present a formula to predict a worm’s vulner- In the area of worm modeling, Kephart, White, and Chess able population size when the worm is still at its early propaga- of IBM performed a series of studies from 1991 to 1993 on tion stage. We also present a formula to correct the bias in the viral infection based on epidemiology models [21], [20], [22]. number of infected hosts observed by a monitoring system. This Staniford et al. [37] used the classical epidemic model to model bias has been mentioned in [10] and [29], but neither of them has the spread of Code Red right after the Code Red incident on presented methods to correct it. In this way, we can know how July 19, 2001; they also proposed several more vicious worms many computers in the global Internet are really infected based in the same paper. Zou et al. [46] presented a “two-factor” worm on local monitored data. Furthermore, we point out that in de- model that considered both the effect of human countermeasures signing a worm monitoring system, the address space covered and the effect of the congestion caused by extensive worm scan by a monitoring system should be as distributed as possible in traffic. Chen et al. [10] presented a discrete-time version worm order to monitor and detect nonuniform scan worms, especially model that considered the patching and cleaning effect during a a sequential scan worm such as Blaster. worm’s propagation. The rest of this paper is organized as follows. Section II sur- For a fast spreading worm such as Slammer, it is necessary veys related work. Section III introduces the worm-propagation to have automatic response and mitigation mechanisms. Moore models used in this paper. Section IV describes briefly the mon- et al. [28] discussed the effect of Internet quarantine for con- itoring system. Data collection and the bias correction formula taining the propagation of a worm. Williamson [42] proposed for monitored biased data are described in Section V.Section VI a general rate-limiting “throttling” method to greatly constrain presents the Kalman filters for early worm detection, and the infection traffic sent out by infected hosts while not affecting formula to predict the vulnerable population size. We conduct normal traffic. Zou et al. [47] presented a feedback dynamic extensive simulation experiments and show the major results in quarantine system for automatic mitigation by borrowing two Section VII. In Section VIII, we discuss limitations and possible principles used in the epidemic disease control in the real world: future work. Section IX concludes this paper. “preemptive quarantine” and “feedback adjustment.” Staniford [36] presented automatic worm quarantine for enterprise net- works by using CounterMalice devices to separate an enterprise II. RELATED WORK network into many isolated subnetworks. Weaver et al. [41] fur- ther improved the CounterMalice quarantine by designing hard- In recent years, people have paid attention to the necessity of ware-centered quarantine algorithms. Jung et al. [18], [19] pro- monitoring the Internet for malicious activities. Symantec Cor- posed a “threshold random walk” algorithm to quickly detect poration has an “enterprise early warning solution” [1], which and block worm scans based on the excessive illegal scans sent collects IDS and firewall attack data from the security systems out by worm-infected hosts.
Load more

Recommended publications
  • Storm: When Researchers Collide 7 Based on the Kademlia DHT Algorithm [5]
    When It comes to Internet threats, B R a n d o n E n R i g h t, g E o ff V o E l k er , few topics get researchers and the media as Stefan SaVagE, ChRiS kaniCh, and kiRill LevchEnko excited as the propagation speed and vital- ity of modern malware. One such example is the SQL Slammer worm, which was the first so-called Warhol Worm, a term used to Storm: when describe worms that get their “15 minutes of fame” by spreading at an exponential researchers collide rate—infecting every vulnerable machine Brandon Enright is a network security analyst at in under 15 minutes [1]. It is ironic, then, that the University of California, San Diego. He is pri- the latest malware to capture the attention marily interested in malware and exploit research. of researchers is not one of the shortest- [email protected] lived but one of the longest, largest, and most successful bots ever: Storm. Geoff Voelker is an associate professor of computer science at the University of California, San Diego. He works in computer systems and networking. Storm got its name from a particular self-propa- [email protected] gation spam email subject line used in early 2007: “230 dead as storm batters Europe.” Storm, also known as the Storm worm, is not actually a worm. It is hybrid malware: part worm, part bot (a pro- Stefan Savage is an associate professor of computer gram designed to perform automated tasks), part science at the University of California, San Diego.
    [Show full text]
  • Detection, Propagation Modeling and Designing of Advanced Internet Worms
    DETECTION, PROPAGATION MODELING AND DESIGNING OF ADVANCED INTERNET WORMS By PARBATI KUMAR MANNA A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2008 1 °c 2008 Parbati Kumar Manna 2 To my family, friends, and teachers 3 ACKNOWLEDGMENTS I want to take this opportunity to thank all the people who helped me during my doctoral sojourn. I understand that it is rather late to acknowledge their contributions, but as the saying goes, better late than never! First, I want to thank my committee, starting with my advisor and Chair, Dr. Sanjay Ranka. He expressed his intention to work with me during my very first week of class at University of Florida, and has been a true guide to me in every aspect since then. He offered me complete freedom in pursuing my research in any area that I felt passionate about, and provided ample research direction from time to time. I am truly thankful and honored to work as his student for the past six years. It has also been a pleasure to work with Dr. Shigang Chen, who served as my co-chair. A stalwart in the network research community, he has been instrumental in providing his domain expertise to my research area in a very big way. Without his help, I can barely imagine myself to be where I am now. I would also like to thank Dr. Alin Dobra, Dr. Christopher Germaine, Dr. Sartaj Sahni and Dr. Malay Ghosh who helped me in various academic as well as non-academic matters throughout my stay at Gainesville.
    [Show full text]
  • A Research on Different Types of Malware and Detection Techniques
    International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-2S8, August 2019 A Research on Different Types of Malware and Detection Techniques Chandini S B, Rajendra A B,Nitin Srivatsa G Abstract—Malware has become a serious threat. Malware that load on to memory by inserting infected code into an analysis is one of the challenging domain. The increase in the executable file. malware exploitation has made the detailed study of the malware, understand the different types of malware and its behavior model Resident Virus and analyze the existing detection system with their short comes to This type of virus hides within the computer memory and identify the research gaps [8] to solve the specific problem. So in gets activated whenever the operating system starts or this paper, we have presented the different malware taxonomy and different malware detection techniques with its features and also execute a specific action. presented the malware model and the research gaps in the Non-resident Virus malware analysis domain. This type of virus does not reside in memory. It infects the Keywords: Polymorphic virus, Malware genesis, target and transfers the control to the infected application Self-replicating cellular automata, Zero-day threat, obfuscation program. It consists of finder module and replicating modules technique, and Anomaly-based detection. finder will find the new targets to infect the new file and the replicates will infect the file. I. INTRODUCTION Macro Virus Malware is a malicious code which comes with the intention to destruct the system [1]. Some of the symptoms of This virus is written in a macro language.
    [Show full text]
  • Computer Viruses and Malware Advances in Information Security
    Computer Viruses and Malware Advances in Information Security Sushil Jajodia Consulting Editor Center for Secure Information Systems George Mason University Fairfax, VA 22030-4444 email: [email protected] The goals of the Springer International Series on ADVANCES IN INFORMATION SECURITY are, one, to establish the state of the art of, and set the course for future research in information security and, two, to serve as a central reference source for advanced and timely topics in information security research and development. The scope of this series includes all aspects of computer and network security and related areas such as fault tolerance and software assurance. ADVANCES IN INFORMATION SECURITY aims to publish thorough and cohesive overviews of specific topics in information security, as well as works that are larger in scope or that contain more detailed background information than can be accommodated in shorter survey articles. The series also serves as a forum for topics that may not have reached a level of maturity to warrant a comprehensive textbook treatment. Researchers, as well as developers, are encouraged to contact Professor Sushil Jajodia with ideas for books under this series. Additional tities in the series: HOP INTEGRITY IN THE INTERNET by Chin-Tser Huang and Mohamed G. Gouda; ISBN-10: 0-387-22426-3 PRIVACY PRESERVING DATA MINING by Jaideep Vaidya, Chris Clifton and Michael Zhu; ISBN-10: 0-387- 25886-8 BIOMETRIC USER AUTHENTICATION FOR IT SECURITY: From Fundamentals to Handwriting by Claus Vielhauer; ISBN-10: 0-387-26194-X IMPACTS AND RISK ASSESSMENT OF TECHNOLOGY FOR INTERNET SECURITY.'Enabled Information Small-Medium Enterprises (TEISMES) by Charles A.
    [Show full text]
  • Network Intrusion Detection: Monitoring, Simulation and Visualization
    University of Central Florida STARS Electronic Theses and Dissertations, 2004-2019 2005 Network Intrusion Detection: Monitoring, Simulation And Visualization Mian Zhou University of Central Florida Part of the Computer Sciences Commons, and the Engineering Commons Find similar works at: https://stars.library.ucf.edu/etd University of Central Florida Libraries http://library.ucf.edu This Doctoral Dissertation (Open Access) is brought to you for free and open access by STARS. It has been accepted for inclusion in Electronic Theses and Dissertations, 2004-2019 by an authorized administrator of STARS. For more information, please contact [email protected]. STARS Citation Zhou, Mian, "Network Intrusion Detection: Monitoring, Simulation And Visualization" (2005). Electronic Theses and Dissertations, 2004-2019. 520. https://stars.library.ucf.edu/etd/520 Network Intrusion Detection: Monitoring, Simulation and Visualization by Mian Zhou B.E. Beijing University, 1998 M.S. University of Central Florida, 2001 A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the School of Computer Science in the College of Engineering and Computer Science at the University of Central Florida Orlando, Florida Summer Term 2005 Major Professor: Sheau-Dong Lang c 2005 by Mian Zhou Abstract This dissertation presents our work on network intrusion detection and intrusion sim- ulation. The work in intrusion detection consists of two different network anomaly-based approaches. The work in intrusion simulation introduces a model using explicit traffic gen- eration for the packet level traffic simulation. The process of anomaly detection is to first build profiles for the normal network activity and then mark any events or activities that deviate from the normal profiles as suspicious.
    [Show full text]
  • Outline Malicious Software Trojan (Horse)
    Outline CSci 5271 Malware and the network Introduction to Computer Security Malware and Denial of Service Announcements intermission Stephen McCamant University of Minnesota, Computer Science & Engineering Denial of service and the network Malicious software Trojan (horse) Shortened to Mal. ware Software whose inherent goal is Looks benign, has secret malicious malicious functionality Not just used for bad purposes Key technique: fool users into Strong adversary installing/running High visibility Concern dates back to 1970s, MLS Many types (Computer) viruses Worms Attaches itself to other software Completely automatic self-propagation Propagates when that program runs Requires remote security holes Once upon a time: floppy disks Classic example: 1988 Morris worm More modern: macro viruses “Golden age” in early 2000s Have declined in relative importance Internet-level threat seems to have declined Fast worm propagation Getting underneath Initial hit-list Lower-level/higher-privilege code can Pre-scan list of likely targets Accelerate cold-start phase deceive normal code Permutation-based sampling Rootkit: hide malware by changing Systematic but not obviously patterned kernel behavior Pseudorandom permutation MBR virus: take control early in boot Approximate time: 15 minutes Blue-pill attack: malware is a VMM “Warhol worm” Too fast for human-in-the-loop response running your system Malware motivation User-based monetization Once upon a time: curiosity, fame Adware, mild spyware Now predominates: money Keyloggers, stealing financial Modest-size
    [Show full text]
  • Use Style: Paper Title
    CORE Metadata, citation and similar papers at core.ac.uk Provided by Greenwich Academic Literature Archive © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The definitive version is available at: http://dx.doi.org/10.1109/ICCCNT.2014.6963124 A Pseudo-Worm Daemon (PWD) for Empirical Analysis of Zero-Day Network Worms and Countermeasure Testing Khurram Shahzad and Steve Woodhead Internet Security Research Laboratory Department of Electrical, Electronic and Computer Engineering University of Greenwich London, UK {sk81,ws01}@gre.ac.uk Abstract— The cyber epidemiological analysis of computer sophistication of worm’s code, which have made them highly worms has emerged a key area of research in the field of cyber infectious and capable of causing a denial of service attack on security. In order to understand the epidemiology of computer the internet as in the case of SQL Slammer outbreak. The worms; a network daemon is required to empirically observe SQL Slammer worm is considered to be the fastest random- their infection and propagation behavior. The same facility can scanning worm in the history as it has achieved its full also be employed in testing candidate worm countermeasures. aggregate scanning rate, of over 55 million scans per seconds, In this paper, we present the architecture and design of Pseudo- only after 3 minutes of its release [1], while infecting 90% of Worm Daemon; termed (PWD), which is designed to perform susceptible machines within 10 minutes [1,2].
    [Show full text]
  • Painting the Internet a Different Kind of Warhol Worm.Pdf
    Painting the Internet: A Different Kind of Warhol Worm John Aycock Department of Computer Science University of Calgary 2500 University Drive N.W. Calgary, Alberta, Canada T2N 1N4 [email protected] TR 2006-834-27, May 2006 Abstract Some people have argued that software is artistic. If so, what about malware? Only occasional, small-scale attempts have been made to create art using mal- ware. We present “art worms,” worms which allow an artist to use the entire In- ternet as a canvas. These worms could be interactive, allowing an artist to stage a global performance, or non-interactive and automatic. Examples are given of art- works that could result from these worms. Art worms raise a variety of questions about the very nature of art: what constitutes art? must art be seen in order to exist? should art be destroyed? Two major technical aspects of art worms are communication and geolocation. Both aspects ensure that art worms behave correctly to create an overall picture. We look at a number of ways that malware can perform these tasks, which have broader applications to malware targeted at specific countries for the purposes of terrorism or information warfare. 1 Introduction ‘There is no reason not to consider the world as one gigantic painting.’ – Robert Rauschenberg, American artist1 It is unusual to find any connections to art in the field of computer security. Even Warhol worms, named after Andy Warhol, refer not to Warhol’s art but his famous quote [22]: ‘In the future everybody will be world famous for fifteen minutes.’ (Warhol worms may infect their targets in less than fifteen minutes [35].) There is no reason that art and computer security cannot meet.
    [Show full text]
  • CS 457 - Network Security
    CS 457 - Network Security Fall 2014 Major Security Threats Today • Worms/viruses • DDoS attacks • Phishing, key-loggers, etc. • SPAM • Attacks against infrastructure (electricity, nuclear, financial , etc.) • Privacy Worms • Worms vs. viruses vs. Trojan Horses • How do worms propagate? – Apply an exploit to gain access to a machine (typically a buffer overflow) – Scan the Internet for other vulnerable machines – Infect and repeat Worms are Yesterday’s News • Code Red I, July 2001 – Used Microsoft web server vulnerability – Randomly generates IP addresses, intrudes machines – Set to attack whitehouse.gov • Code Red II – Used same vulnerability – Slightly more sophisticated address probing technique • Nimda (reverse of “admin”) – Scanning technique different from Code Red II • Sapphire/Slammer, Jan 2003 – Uses small UDP packets, so very fast – By contrast, previous worms had larger scanners and used TCP 4 NIMDA in NBNL Modeling Worm Propagation • Random Constant Spread (RCS) • Assumptions – Good random generator – No infections are cured – Compromise rate independent from CPU or network speed – Can only be compromised once RCS Model Nda = (Na) K(1-a)dt • <Constants> – N: total number of vulnerable servers – K: initial compromise rate – T: time which fixes when the incident happens • <Variables> – a: proportion of vulnerable machines which have been compromised – t: time (in hours) RCS Model and Measurement Better Worms How can hackers improve worm spread? • Hit-list scanning • Permutation scanning • Topologically aware worms Hit-list scanning
    [Show full text]
  • Limiting Vulnerability Exposure Through Effective Patch Management: Threat Mitigation Through Vulnerability Remediation
    Limiting Vulnerability Exposure through effective Patch Management: threat mitigation through vulnerability remediation Submitted in fulfilment of the requirements of the degree MASTER OF SCIENCE in the Department of Computer Science of Rhodes University Dominic Stjohn Dolin White <[email protected]> January 2006 Abstract This document aims to provide a complete discussion on vulnerability and patch management.It looks first at the trends relating to vulnerabilities, exploits, attacks and patches. These trends provide the drivers of patch and vulnerability management. Understanding these allows the fol- lowing chapters to present both policy and technical solutions to the problem. The policy lays out a comprehensive set of steps that can be followed by any organisation to implement their own patch management policy, including practical advice on integration with other policies, manag- ing risk, strategies for reducing downtime and vulnerability and generating patch metrics. It then discusses how best a vendors should implement a related patch release policy that will allow end-users to most effectively and timeously mitigate vulnerabilities. The next chapter discussed the technical aspect of automating parts of such a policy and how defence in depth can be ap- plied to the field of patch management. The document then concludes that patch management is becoming more difficult and the guidelines described will go a long way into creating a workable and effective means for mitigating exposure to vulnerabilities. However, more research is needed into vulnerabilities, exploits and particularly into threats. Contents 1 Introduction 1 1.1 Backgrounds .................................... 1 1.2 PatchManagement ................................. 3 1.2.1 Definitions ................................. 4 1.3 TheNeedforPatchManagement.
    [Show full text]
  • Effective Worm Detection for Various Scan Techniques
    1 Effective Worm Detection for Various Scan Techniques Jianhong Xia, Sarma Vangala, Jiang Wu and Lixin Gao Department of Electrical and Computer Engineering University of Massachusetts at Amherst Amherst, MA 01003 fjxia, svangala, jiawu, [email protected] Kevin Kwiat Air Force Research Lab Information Directorate 525 Brooks Road, Rome, NY 13441 [email protected] Abstract— In recent years, the threats and damages vulnerable machines in the Internet within several hours caused by active worms have become more and more or even a few minutes. The Slammer worm [16] is an serious. In order to reduce the loss caused by fast- example of such worms that spread to all potential targets spreading active worms, an effective detection mechanism in less than 10 minutes. In order to find vulnerable to quickly detect worms is desired. In this paper, we first hosts, a worm can scan the entire IPv4 address space explore various scan strategies used by worms on finding vulnerable hosts. We show that targeted worms spread randomly. Such a mechanism of finding vulnerable hosts much faster than random scan worms. We then present a is called random scan. In this paper, we analyze various generic worm detection architecture to monitor malicious scan techniques that help worms spread faster than a worm activities. We propose and evaluate our detection random scan worm does. In particular, a worm that scans mechanism called Victim Number Based Algorithm. We IP address space selectively, for example, the only IP show that our detection algorithm is effective and able addresses used in the Internet, can spread faster than to detect worm events before 2% of vulnerable hosts a worm that randomly scans the entire IPv4 address are infected for most scenarios.
    [Show full text]
  • 9780321623980 Sample.Pdf
    Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and the publisher was aware of a trademark claim, the designations have been printed with initial capital letters or in all capitals. The author and publisher have taken care in the preparation of this book, but make no expressed or implied warranty of any kind and assume no responsibility for errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of the use of the information or programs contained herein. Symantec Press Publisher: Linda McCarthy Editor in Chief: Karen Gettman Acquisitions Editor: Jessica Goldstein Cover Designer: Alan Clements Managing Editor: Gina Kanouse Senior Project Editor: Kristy Hart Copy Editor: Christal Andry Indexers: Cheryl Lenser and Larry Sweazy Compositor: Stickman Studio Manufacturing Buyer: Dan Uhrig The publisher offers excellent discounts on this book when ordered in quantity for bulk purchases or special sales, which may include electronic versions and/or custom covers and content particular to your business, training goals, marketing focus, and branding interests. For more information, please contact: U. S. Corporate and Government Sales (800) 382-3419 [email protected] For sales outside the U. S., please contact: International Sales [email protected] Visit us on the Web: www.awprofessional.com Library of Congress Number: 2004114972 Copyright © 2005 Symantec Corporation All rights reserved. Printed in the United States of America. This publication is protected by copyright, and permission must be obtained from the publisher prior to any prohibited reproduction, storage in a retrieval system, or transmission in any form or by any means, electronic, mechanical, photocopying, recording, or likewise.
    [Show full text]