loading
Documents

Research.Publish.Connect.

Paper

Authors: Pamela Carvallo 1 ; Ana R. Cavalli 1 and Natalia Kushik 2

Affiliations: 1 SAMOVAR, Télécom SudParis, CNRS, Université Paris-Saclay and Montimage, France ; 2 SAMOVAR, Télécom SudParis, CNRS and Université Paris-Saclay, France

ISBN: 978-989-758-262-2

Keyword(s): Dataset, Cloud Computing, Intrusion Threat, User Behavior, Synthetic Data Generation, Dataset Validation.

Related Ontology Subjects/Areas/Topics: Cloud Applications ; Distributed and Mobile Software Systems ; Software and Information Security ; Software Engineering ; Software Engineering Methods and Techniques

Abstract: The malicious insider threat is often listed as one of the most dangerous cloud threats. Considering this threat, the main difference between a cloud computing scenario and a traditional IT infrastructure, is that once perpetrated, it could damage other clients due to the multi-tenancy and virtual environment cloud features. One of the related challenges concerns the fact that this threat domain is highly dependent on human behavior characteristics as opposed to the more purely technical domains of network data generation. In this paper, we focus on the derivation and validation of the dataset for cloud-based malicious insider threat. Accordingly, we outline the design of synthetic data, while discussing cloud-based indicators, and socio-technical human factors. As a proof of concept, we test our model on an airline scheduling application provided by a flight operator, together with proposing realistic threat scenarios for its future detection. The work is motivated by the complexity of the problem itself as well as by the absence of the open, realistic cloud-based datasets. (More)

PDF ImageFull Text

Download
Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 54.92.182.0

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Carvallo P., R. Cavalli A. and Kushik N. (2017). Automatic Derivation and Validation of a Cloud Dataset for Insider Threat Detection.In Proceedings of the 12th International Conference on Software Technologies - Volume 1: ICSOFT, ISBN 978-989-758-262-2, pages 480-487. DOI: 10.5220/0006480904800487

@conference{icsoft17,
author={Pamela Carvallo and Ana R. Cavalli and Natalia Kushik},
title={Automatic Derivation and Validation of a Cloud Dataset for Insider Threat Detection},
booktitle={Proceedings of the 12th International Conference on Software Technologies - Volume 1: ICSOFT,},
year={2017},
pages={480-487},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006480904800487},
isbn={978-989-758-262-2},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Software Technologies - Volume 1: ICSOFT,
TI - Automatic Derivation and Validation of a Cloud Dataset for Insider Threat Detection
SN - 978-989-758-262-2
AU - Carvallo P.
AU - R. Cavalli A.
AU - Kushik N.
PY - 2017
SP - 480
EP - 487
DO - 10.5220/0006480904800487

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.