Steve R White, Morton Swimmer, Edward Pring, William Arnold, David Chess, John F Morar, Anatomy of a Commercial-Grade Immune System, Proceedings of the Ninth International Virus Bulletin Conference, September/October 1999, pp 203-228. Home/Search Document Details and Download
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Data Mining Methods for Detection of New Malicious.. - Schultz, Eskin, Zadok.. (5 citations) (Correct)
....data. One of the primary problems faced by the virus community is to devise methods for detecting new malicious programs that have not yet been analyzed [26] Eight to ten malicious programs are created every day and most cannot be accurately detected until signatures have been generated for them [27]. During this time period, systems protected by signature based algorithms are vulnerable to attacks. Malicious executables are also used as attacks for many types of intrusions. In the DARPA 1999 intrusion detection evaluation, many of the attacks on the Windows platform were caused by malicious ....
....Naive Bayes, and a Multi Classifier system. We describe the signature based method first. 5.1 Signature Methods We examine signature based methods to compare our results to traditional anti virus methods. Signature based detection methods are the most commonly used algorithms in industry [27]. These signatures are picked to differentiate one malicious executable from another, and from benign programs. These signatures are generated by an expert in the field or an automatic method. Typically, a signature is picked to illustrate the distinct properties of a specific malicious ....
[Article contains additional citation context not shown here]
Steve R. White, Morton Swimmer, Edward J. Pring, William C. Arnold, David M. Chess, and John F. Morar. Anatomy of a Commercial-Grade Immune System. IBM Research White Paper, 1999. http://www.av.ibm.com/ScientificPapers/White/ Anatomy/anatomy.html. 12
MEF: Malicious Email Filter - Unix Mail Filter (Correct)
....time. With a low false positive rate the inconvenience to the end user would be minimal while providing ample defense during the time before an update of models is available. Virus scanners are updated about every month, and 240 300 new malicious executables are created in that time (8 10 a day [12]) Our method may catch roughly 216 270 of those new malicious executables without the need for an update whereas traditional methods would catch only 87 109. Our method tested on a particular data set more than doubles the detection rate of signature based methods. Secondly, we presented a ....
Steve R. White, Morton Swimmer, Edward J. Pring, William C. Arnold, David M. Chess, and John F. Morar. Anatomy of a Commercial-Grade Immune System. IBM Research White Paper, 1999. http://www.av.ibm.com/ScientificPapers/White/ Anatomy/anatomy.html.
Data Mining Methods for Detection of New Malicious.. - Schultz, Eskin, Zadok, al. (5 citations) (Correct)
....data. One of the primary problems faced by the virus community is to devise methods for detecting new malicious programs that have not yet been analyzed [26] Eight to ten malicious programs are created every day and most cannot be accurately detected until signatures have been generated for them [27]. During this time period, systems protected by signature based algorithms are vulnerable to attacks. Malicious executables are also used as attacks for many types of intrusions. In the DARPA 1999 intrusion detection evaluation, many of the attacks on the Windows platform were caused by malicious ....
....Naive Bayes, and a Multi Classifier system. We describe the signature based method first. 5.1 Signature Methods We examine signature based methods to compare our results to traditional anti virus methods. Signature based detection methods are the most commonly used algorithms in industry [27]. These signatures are picked to differentiate one malicious executable from another, and from benign programs. These signatures are generated by an expert in the field or an automatic method. Typically, a signature is picked to illustrate the distinct properties of a specific malicious ....
[Article contains additional citation context not shown here]
Steve R. White, Morton Swimmer, Edward J. Pring, William C. Arnold, David M. Chess, and John F. Morar. Anatomy of a Commercial-Grade Immune System. IBM Research White Paper, 1999. http://www.av.ibm.com/ScientificPapers/White/ Anatomy/anatomy.html. 12
MEF: Malicious Email Filter - A UNIX Mail Filter that.. - Schultz, Eskin, Stolfo (1 citation) (Correct)
....With a low false positive rate the inconvenience to the end user would be minimal while providing ample defense during the time before an update of models is available. Virus Scanners are updated about every month, and 240 300 new malicious executables are created in that time (8 10 a day [8]) Our method would catch roughly 216 270 of those new malicious executables without the need for an update whereas traditional methods would catch only 87 109. Our method more than doubles the detection rate of signature based methods. ....
Steve R. White, Morton Swimmer, Edward J. Pring, William C. Arnold, David M. Chess, and John F. Morar. Anatomy of a Commercial-Grade Immune System, IBM Research White Paper, 1999. http://www.av.ibm.com/ScientificPapers/White/ Anatomy/anatomy.html
Data Mining Methods for Detection of New Malicious.. - Schultz, Eskin, Zadok.. (5 citations) (Correct)
....training data. One of the primary problems faced by the virus community is to devise methods for detecting new malicious programs that have not yet been analyzed [24] Eight to ten malicious programs are created every day and most cannot be detected until signatures have been generated for them [25]. During this time period, systems protected by signature based algorithms are vulnerable to attacks. Malicious executables are also used as attacks for many types of intrusions. In the DARPA 1999 intrusion detection evaluation, many of the attacks on the Windows platform were caused by malicious ....
....Naive Bayes, and a Multi Classifier system. We detail the signature based method first. 5 4.1 Signature Methods We examine signature based methods to compare our results to traditional anti virus methods. Signature based detection methods are the most commonly used algorithms in the industry [25]. These signatures are picked to differentiate one malicious executable from another, and from benign programs. These signatures can generated by an expert in the field or an automatic method. Typically a signature is picked to illustrate the unusual properties of a specific malicious executables. ....
[Article contains additional citation context not shown here]
Steve R. White, Morton Swimmer, Edward J. Pring, William C. Arnold, David M. Chess, and John F. Morar. Anatomy of a Commercial-Grade Immune System. IBM Research White Paper, 1999. http://www.av.ibm.com/ScientificPapers/White/ Anatomy/anatomy.html. 14
An Environment for Controlled Worm Replication and Analysis - Or Internet-Inna-Box Ian (2000) Self-citation (Swimmer Arnold Chess Morar) (Correct)
No context found.
Steve R White, Morton Swimmer, Edward Pring, William Arnold, David Chess, John F Morar, Anatomy of a Commercial-Grade Immune System, Proceedings of the Ninth International Virus Bulletin Conference, September/October 1999, pp 203-228.
Developing an Immunity to Spam - Terri Oda And (2003) Self-citation (White) (Correct)
No context found.
White, S.R., Swimmer, M., Pring, E.J., Arnold, W.C., Chess, D.M., Morar, J.F.: Anatomy of a commercial-grade immune system. Technical report, IBM Thomas J. Watson Research Center (2002)
Developing an Immunity to Spam - Oda, White (2003) Self-citation (White) (Correct)
No context found.
White, S.R., Swimmer, M., Pring, E.J., Arnold, W.C., Chess, D.M., Morar, J.F.: Anatomy of a commercial-grade immune system. Technical report, IBM Thomas J. Watson Research Center (2002)
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