Evidential reasoning in network intrusion detection systems

Mansour Esmaili; Reihaneh Safavi-Naini; Josef Pieprzyk

Evidential reasoning in network intrusion detection systems

Mansour Esmaili, Reihaneh Safavi-Naini, Josef Pieprzyk

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

Abstract

Intrusion Detection Systems (IDS) have previously been built by hand. These systems have difficulty successfully classifying intruders, and require a significant amount of computational overhead making it difficult to create robust real-time IDS systems. Artificial Intelligence techniques can reduce the human effort required to build these systems and can improve their performance. AI has recently been used in Intrusion Detection (ID) for anomaly detection, data reduction and induction, or discovery, of rules explaining audit data [l]. This paper proposes the application of evidential reasoning for dealing with uncertainty in Intrusion Detection Systems. We show how dealing with uncertainty can allow the system to detect the abnormality in the user behavior more efficiently.

Original language	English
Title of host publication	Information Security and Privacy - 1st Australasian Conference, ACISP 1996, Proceedings
Place of Publication	Berlin; Heidelberg
Publisher	Springer, Springer Nature
Pages	253-265
Number of pages	13
Volume	1172
ISBN (Print)	3540619917, 9783540619918
Publication status	Published - 1996
Externally published	Yes
Event	1st Australasian Conference on Information Security and Privacy, ACISP - 1996 - Wollongong, Australia Duration: 24 Jun 1996 → 26 Jun 1996

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	1172
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Other

Other	1st Australasian Conference on Information Security and Privacy, ACISP - 1996
Country/Territory	Australia
City	Wollongong
Period	24/06/96 → 26/06/96

Cite this

Esmaili, M., Safavi-Naini, R., & Pieprzyk, J. (1996). Evidential reasoning in network intrusion detection systems. In Information Security and Privacy - 1st Australasian Conference, ACISP 1996, Proceedings (Vol. 1172, pp. 253-265). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1172). Springer, Springer Nature.

Esmaili, Mansour ; Safavi-Naini, Reihaneh ; Pieprzyk, Josef. / Evidential reasoning in network intrusion detection systems. Information Security and Privacy - 1st Australasian Conference, ACISP 1996, Proceedings. Vol. 1172 Berlin; Heidelberg : Springer, Springer Nature, 1996. pp. 253-265 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{3020ad9cf0b54cf39b02afa194f75240,

title = "Evidential reasoning in network intrusion detection systems",

abstract = "Intrusion Detection Systems (IDS) have previously been built by hand. These systems have difficulty successfully classifying intruders, and require a significant amount of computational overhead making it difficult to create robust real-time IDS systems. Artificial Intelligence techniques can reduce the human effort required to build these systems and can improve their performance. AI has recently been used in Intrusion Detection (ID) for anomaly detection, data reduction and induction, or discovery, of rules explaining audit data [l]. This paper proposes the application of evidential reasoning for dealing with uncertainty in Intrusion Detection Systems. We show how dealing with uncertainty can allow the system to detect the abnormality in the user behavior more efficiently.",

author = "Mansour Esmaili and Reihaneh Safavi-Naini and Josef Pieprzyk",

year = "1996",

language = "English",

isbn = "3540619917",

volume = "1172",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer, Springer Nature",

pages = "253--265",

booktitle = "Information Security and Privacy - 1st Australasian Conference, ACISP 1996, Proceedings",

address = "United States",

note = "1st Australasian Conference on Information Security and Privacy, ACISP - 1996 ; Conference date: 24-06-1996 Through 26-06-1996",

}

Esmaili, M, Safavi-Naini, R & Pieprzyk, J 1996, Evidential reasoning in network intrusion detection systems. in Information Security and Privacy - 1st Australasian Conference, ACISP 1996, Proceedings. vol. 1172, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1172, Springer, Springer Nature, Berlin; Heidelberg, pp. 253-265, 1st Australasian Conference on Information Security and Privacy, ACISP - 1996, Wollongong, New South Wales, Australia, 24/06/96.

Evidential reasoning in network intrusion detection systems. / Esmaili, Mansour; Safavi-Naini, Reihaneh; Pieprzyk, Josef.
Information Security and Privacy - 1st Australasian Conference, ACISP 1996, Proceedings. Vol. 1172 Berlin; Heidelberg: Springer, Springer Nature, 1996. p. 253-265 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1172).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - Evidential reasoning in network intrusion detection systems

AU - Esmaili, Mansour

AU - Safavi-Naini, Reihaneh

AU - Pieprzyk, Josef

PY - 1996

Y1 - 1996

N2 - Intrusion Detection Systems (IDS) have previously been built by hand. These systems have difficulty successfully classifying intruders, and require a significant amount of computational overhead making it difficult to create robust real-time IDS systems. Artificial Intelligence techniques can reduce the human effort required to build these systems and can improve their performance. AI has recently been used in Intrusion Detection (ID) for anomaly detection, data reduction and induction, or discovery, of rules explaining audit data [l]. This paper proposes the application of evidential reasoning for dealing with uncertainty in Intrusion Detection Systems. We show how dealing with uncertainty can allow the system to detect the abnormality in the user behavior more efficiently.

AB - Intrusion Detection Systems (IDS) have previously been built by hand. These systems have difficulty successfully classifying intruders, and require a significant amount of computational overhead making it difficult to create robust real-time IDS systems. Artificial Intelligence techniques can reduce the human effort required to build these systems and can improve their performance. AI has recently been used in Intrusion Detection (ID) for anomaly detection, data reduction and induction, or discovery, of rules explaining audit data [l]. This paper proposes the application of evidential reasoning for dealing with uncertainty in Intrusion Detection Systems. We show how dealing with uncertainty can allow the system to detect the abnormality in the user behavior more efficiently.

UR - http://www.scopus.com/inward/record.url?scp=84947997895&partnerID=8YFLogxK

M3 - Conference proceeding contribution

AN - SCOPUS:84947997895

SN - 3540619917

SN - 9783540619918

VL - 1172

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 253

EP - 265

BT - Information Security and Privacy - 1st Australasian Conference, ACISP 1996, Proceedings

PB - Springer, Springer Nature

CY - Berlin; Heidelberg

T2 - 1st Australasian Conference on Information Security and Privacy, ACISP - 1996

Y2 - 24 June 1996 through 26 June 1996

ER -

Esmaili M, Safavi-Naini R, Pieprzyk J. Evidential reasoning in network intrusion detection systems. In Information Security and Privacy - 1st Australasian Conference, ACISP 1996, Proceedings. Vol. 1172. Berlin; Heidelberg: Springer, Springer Nature. 1996. p. 253-265. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Evidential reasoning in network intrusion detection systems

Abstract

Publication series

Other

Other files and links

Fingerprint

Cite this