Intrusion detection in network flows based on an optimized clustering   criterion

JABER KARIMPOUR; SHAHRIAR LOTFI; ALIAKBAR TAJARI SIAHMARZKOOH

Article Details

Intrusion detection in network flows based on an optimized clustering criterion

Authors:

JABER KARIMPOUR | SHAHRIAR LOTFI | ALIAKBAR TAJARI SIAHMARZKOOH

Publisher:

TUBITAK

Journal:

Turkish Journal of Electrical Engineering and Computer Science 2017 Vol.25 Issue 3, pp.1963-1975

Document Type:

Article

Publication Date:

5/2017

Document Language:

Turkish

Document ID (Org No.):

2000740813

Frequency:

Bimonthly

eISSN:

1303-6203

pISSN:

1300-0632

Abstract:

 

Graph-based intrusion detection approaches consider the   network as a graph and detect anomalies based on graph metrics. However,   most of these approaches succumb to the cluster-based behavior of the   anomalies. To resolve this problem in our study, we use flow and   graph-clustering concepts to create a data set first. A new criterion   related to the average weight of clusters is then defined and a model is   proposed to detect attacks based on the above-mentioned criterion. Finally,   the model is evaluated using a DARPA data set. Results show that the   proposed approach detects the attacks with high accuracy relative to methods   described in previous studies.

Article Details

Intrusion detection in network flows based on an optimized clustering criterion

Authors: JABER KARIMPOUR | SHAHRIAR LOTFI | ALIAKBAR TAJARI SIAHMARZKOOH

Publisher: TUBITAK

Journal: Turkish Journal of Electrical Engineering and Computer Science 2017 Vol.25 Issue 3, pp.1963-1975

Document Type: Article

Publication Date: 5/2017

Document Language: Turkish

Document ID (Org No.): 2000740813

Frequency: Bimonthly

eISSN: 1303-6203

pISSN: 1300-0632

Issues

Authors:

JABER KARIMPOUR | SHAHRIAR LOTFI | ALIAKBAR TAJARI SIAHMARZKOOH

Publisher:

TUBITAK

Journal:

Turkish Journal of Electrical Engineering and Computer Science 2017 Vol.25 Issue 3, pp.1963-1975

Document Type:

Article

Publication Date:

5/2017

Document Language:

Turkish

Document ID (Org No.):

2000740813

Frequency:

Bimonthly

eISSN:

1303-6203

pISSN:

1300-0632