Increasing the rate of intrusion detection based on a hybrid technique

Khattab M. Ali Alheeti; Laith Al-Jobouri; Klaus McDonald-Maier

doi:10.1109/CEEC.2013.6659468

Increasing the rate of intrusion detection based on a hybrid technique

Khattab M. Ali Alheeti, Laith Al-Jobouri, Klaus McDonald-Maier

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

6 Citations (Scopus)

Abstract

This paper presents techniques to increase intrusion detection rates. Theses techniques are based on specific features that are detected and it's shown that a small number of features (9) can yield improved detection rates compared to higher numbers. These techniques utilize soft computing techniques such a Backpropagation based artificial neural networks and fuzzy sets. These techniques achieve a significant improvement over the state of the art for standard DARPA benchmark data.

Original language	English
Title of host publication	2013 5th Computer Science and Electronic Engineering Conference (CEEC)
Subtitle of host publication	conference proceedings
Place of Publication	Piscataway
Publisher	IEEE
Pages	179-182
Number of pages	4
ISBN (Electronic)	9781479903832, 9781479903818
ISBN (Print)	9781479903825
DOIs	https://doi.org/10.1109/CEEC.2013.6659468
Publication status	Published - 11 Nov 2013
Externally published	Yes
Event	2013 5th Computer Science and Electronic Engineering Conference (CEEC): conference proceedings - University of Essex, Colchester, United Kingdom Duration: 17 Sept 2013 → 18 Sept 2013 Conference number: 5th

Conference

Conference	2013 5th Computer Science and Electronic Engineering Conference (CEEC)
Abbreviated title	CEEC 2013
Country/Territory	United Kingdom
City	Colchester
Period	17/09/13 → 18/09/13

Keywords

Intrusion detection
Soft computing
Fuzzy set
Neural networks

Access to Document

10.1109/CEEC.2013.6659468

Cite this

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title = "Increasing the rate of intrusion detection based on a hybrid technique",

abstract = "This paper presents techniques to increase intrusion detection rates. Theses techniques are based on specific features that are detected and it's shown that a small number of features (9) can yield improved detection rates compared to higher numbers. These techniques utilize soft computing techniques such a Backpropagation based artificial neural networks and fuzzy sets. These techniques achieve a significant improvement over the state of the art for standard DARPA benchmark data.",

author = "\{Ali Alheeti\}, \{Khattab M.\} and Laith Al-Jobouri and Klaus McDonald-Maier",

year = "2013",

month = nov,

day = "11",

doi = "10.1109/CEEC.2013.6659468",

language = "English",

isbn = "9781479903825",

pages = "179--182",

booktitle = "2013 5th Computer Science and Electronic Engineering Conference (CEEC)",

publisher = "IEEE ",

note = "2013 5th Computer Science and Electronic Engineering Conference (CEEC) : conference proceedings, CEEC 2013 ; Conference date: 17-09-2013 Through 18-09-2013",

}

Ali Alheeti, KM, Al-Jobouri, L & McDonald-Maier, K 2013, Increasing the rate of intrusion detection based on a hybrid technique. in 2013 5th Computer Science and Electronic Engineering Conference (CEEC): conference proceedings. IEEE , Piscataway, pp. 179-182, 2013 5th Computer Science and Electronic Engineering Conference (CEEC), Colchester, United Kingdom, 17/09/13. https://doi.org/10.1109/CEEC.2013.6659468

TY - GEN

T1 - Increasing the rate of intrusion detection based on a hybrid technique

AU - Ali Alheeti, Khattab M.

AU - Al-Jobouri, Laith

AU - McDonald-Maier, Klaus

N1 - Conference code: 5th

PY - 2013/11/11

Y1 - 2013/11/11

N2 - This paper presents techniques to increase intrusion detection rates. Theses techniques are based on specific features that are detected and it's shown that a small number of features (9) can yield improved detection rates compared to higher numbers. These techniques utilize soft computing techniques such a Backpropagation based artificial neural networks and fuzzy sets. These techniques achieve a significant improvement over the state of the art for standard DARPA benchmark data.

AB - This paper presents techniques to increase intrusion detection rates. Theses techniques are based on specific features that are detected and it's shown that a small number of features (9) can yield improved detection rates compared to higher numbers. These techniques utilize soft computing techniques such a Backpropagation based artificial neural networks and fuzzy sets. These techniques achieve a significant improvement over the state of the art for standard DARPA benchmark data.

U2 - 10.1109/CEEC.2013.6659468

DO - 10.1109/CEEC.2013.6659468

M3 - Conference contribution

AN - SCOPUS:84892605806

SN - 9781479903825

SP - 179

EP - 182

BT - 2013 5th Computer Science and Electronic Engineering Conference (CEEC)

PB - IEEE

CY - Piscataway

T2 - 2013 5th Computer Science and Electronic Engineering Conference (CEEC)

Y2 - 17 September 2013 through 18 September 2013

ER -

Increasing the rate of intrusion detection based on a hybrid technique

Abstract

Conference

Keywords

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