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Characterizing the behavior of a program using multiple-length N-grams

Published: 20 February 2001 Publication History
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References

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Arbib, M.A., Theories of Abstract Automata. 1969, Englewood Cliffs, NJ: Prentice-Hall.
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Computer Science Department University of New Mexico, Synthetic UNM lpr data. 1995: http://www.cs.unm.edu/~immsec/data/synth-lpr.html.
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Computer Science Department University of New Mexico, UNM live inetd data. 1996: http://www.cs.unm.edu/-immsec/data/live-inetd.html.
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Debar, H., Dacier, M., Nassehi, M., and Wespi, A., "Fixed vs. variable-length patterns for detecting suspicious process behavior," in ESORICS 98, 5th European Symposium on Research in Computer Security, 1998, Louvain-la-Neuve, Belgium: Springer Vedag.
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Forrest, S., Hofmeyr, S., and Somayaji, A., "Computer immunology," Communications of the ACM, 1997, 40(10), p. 88-96.
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Forrest, S., Hofmeyr, S.A., and Somajayi, A., "A Sense of Self for UNIX Processes," in 1996 IEEE Symposium on Computer Security and Privacy, 1996: IEEE Press.
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Gusfield, D., Algorithms on Strings, Trees, and Sequences. 1997: Cambridge University Press.
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Hofmeyr, S., Forrest, S., and Somayaji, A., "Intrusion detection using sequences of system calls," Journal of Computer Security, 1998, 6, p. 151-180.
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Nerode, A., "Linear automaton transformations," Proceedings of the American Mathematics Society, 1958, 9, p. 541-544.
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Stillerman, M., Marceau, C., and Stillman, M., "Intrusion Detection for Distributed Applications," Communications of the ACM, 1999, 42(7), pp. 62-69.

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cover image ACM Conferences
NSPW '00: Proceedings of the 2000 workshop on New security paradigms
February 2001
168 pages
ISBN:1581132603
DOI:10.1145/366173
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Publication History

Published: 20 February 2001

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Author Tags

  1. computational immunology
  2. finite automata
  3. intrusion detection
  4. string processing

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NSPW00
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NSPW00: New Security Paradigms 2000
September 18 - 21, 2000
Ballycotton, County Cork, Ireland

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NSPW '00 Paper Acceptance Rate 15 of 35 submissions, 43%;
Overall Acceptance Rate 98 of 265 submissions, 37%

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  • (2023)Advanced Persistent Threat Detection Using Data Provenance and Metric LearningIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.322178920:5(3957-3969)Online publication date: 1-Sep-2023
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