A human-centred model for network flow analysis

Thibaud Merien; David Brosset; Xavier Bellekens; Christophe Claramunt

doi:10.1109/CSNET.2018.8602913

A human-centred model for network flow analysis

Thibaud Merien, David Brosset, Xavier Bellekens, Christophe Claramunt

Electronic And Electrical Engineering

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

1 Citation (Scopus)

19 Downloads (Pure)

Abstract

Computer networks are ubiquitous and growing exponentially, with a predicted 50 billion devices connected by 2050. This tremendous growth dramatically increases the attack surface of both private and public networks. These attacks often influence the behaviour of the system, leading to the detection of the attack. In this manuscript we model the path of an attack through the network by graphs. The model developed aims to better integer attackers intentions. Using the data produced by 5 honeypots, we apply our model. The preliminary results show that the approach is useful to rapidly detect anomalies in the experiment dataset.

Original language	English
Title of host publication	2018 2nd Cyber Security in Networking Conference (CSNet)
Place of Publication	Piscataway, NJ.
Publisher	IEEE
Number of pages	6
ISBN (Print)	9781538670453
DOIs	https://doi.org/10.1109/CSNET.2018.8602913
Publication status	Published - 7 Jan 2019
Event	2nd Cyber Security in Networking Conference, CSNet 2018 - Paris, France Duration: 24 Oct 2018 → 26 Oct 2018

Conference

Conference	2nd Cyber Security in Networking Conference, CSNet 2018
Country/Territory	France
City	Paris
Period	24/10/18 → 26/10/18

Keywords

computer networks
attack detection
system behaviour

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10.1109/CSNET.2018.8602913

Merien-etal-CSNC-2018-A-human-centred-model-for-network-flow-analysisAccepted author manuscript, 257 KB

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title = "A human-centred model for network flow analysis",

abstract = "Computer networks are ubiquitous and growing exponentially, with a predicted 50 billion devices connected by 2050. This tremendous growth dramatically increases the attack surface of both private and public networks. These attacks often influence the behaviour of the system, leading to the detection of the attack. In this manuscript we model the path of an attack through the network by graphs. The model developed aims to better integer attackers intentions. Using the data produced by 5 honeypots, we apply our model. The preliminary results show that the approach is useful to rapidly detect anomalies in the experiment dataset.",

keywords = "computer networks, attack detection, system behaviour",

author = "Thibaud Merien and David Brosset and Xavier Bellekens and Christophe Claramunt",

note = "{\textcopyright} 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.; 2nd Cyber Security in Networking Conference, CSNet 2018 ; Conference date: 24-10-2018 Through 26-10-2018",

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doi = "10.1109/CSNET.2018.8602913",

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AU - Merien, Thibaud

AU - Brosset, David

AU - Bellekens, Xavier

AU - Claramunt, Christophe

N1 - © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2019/1/7

Y1 - 2019/1/7

N2 - Computer networks are ubiquitous and growing exponentially, with a predicted 50 billion devices connected by 2050. This tremendous growth dramatically increases the attack surface of both private and public networks. These attacks often influence the behaviour of the system, leading to the detection of the attack. In this manuscript we model the path of an attack through the network by graphs. The model developed aims to better integer attackers intentions. Using the data produced by 5 honeypots, we apply our model. The preliminary results show that the approach is useful to rapidly detect anomalies in the experiment dataset.

AB - Computer networks are ubiquitous and growing exponentially, with a predicted 50 billion devices connected by 2050. This tremendous growth dramatically increases the attack surface of both private and public networks. These attacks often influence the behaviour of the system, leading to the detection of the attack. In this manuscript we model the path of an attack through the network by graphs. The model developed aims to better integer attackers intentions. Using the data produced by 5 honeypots, we apply our model. The preliminary results show that the approach is useful to rapidly detect anomalies in the experiment dataset.

KW - computer networks

KW - attack detection

KW - system behaviour

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DO - 10.1109/CSNET.2018.8602913

M3 - Conference contribution book

AN - SCOPUS:85061828885

SN - 9781538670453

BT - 2018 2nd Cyber Security in Networking Conference (CSNet)

PB - IEEE

CY - Piscataway, NJ.

T2 - 2nd Cyber Security in Networking Conference, CSNet 2018

Y2 - 24 October 2018 through 26 October 2018

ER -

A human-centred model for network flow analysis

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