loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Ahmed Saaudi ; Yan Tong and Csilla Farkas

Affiliation: Department of Computer Science & Engineering, University of South Carolina, 550 Assembly St., Columbia and U.S.A.

Keyword(s): Insider Threat, Anomaly Detection System, Machine Learning, HMM, Big Data.

Related Ontology Subjects/Areas/Topics: Internet Technology ; Intrusion Detection and Response ; Web Information Systems and Technologies

Abstract: This paper presents a novel approach to detect malicious behaviors in computer systems. We propose the use of varying granularity levels to represent users’ log data: Session-based, Day-based, and Week-based. A user’s normal behavior is modeled using a Hidden Markov Model. The model is used to detect any deviation from the normal behavior. We also propose a Sliding Window Technique to identify malicious activity effectively by considering the near history of user activity. We evaluated our results using Receiver Operating Characteristic curves (or ROC curves). Our evaluation shows that the results are superior to existing research by improving the detection ability and reducing the false positive rate. Combining sliding window technique with session-based system gives a fast detection performance.

CC BY-NC-ND 4.0

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 3.149.27.33

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:
Saaudi, A.; Tong, Y. and Farkas, C. (2019). Probabilistic Graphical Model on Detecting Insiders: Modeling with SGD-HMM. In Proceedings of the 5th International Conference on Information Systems Security and Privacy - ICISSP; ISBN 978-989-758-359-9; ISSN 2184-4356, SciTePress, pages 461-470. DOI: 10.5220/0007404004610470

@conference{icissp19,
author={Ahmed Saaudi. and Yan Tong. and Csilla Farkas.},
title={Probabilistic Graphical Model on Detecting Insiders: Modeling with SGD-HMM},
booktitle={Proceedings of the 5th International Conference on Information Systems Security and Privacy - ICISSP},
year={2019},
pages={461-470},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007404004610470},
isbn={978-989-758-359-9},
issn={2184-4356},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Information Systems Security and Privacy - ICISSP
TI - Probabilistic Graphical Model on Detecting Insiders: Modeling with SGD-HMM
SN - 978-989-758-359-9
IS - 2184-4356
AU - Saaudi, A.
AU - Tong, Y.
AU - Farkas, C.
PY - 2019
SP - 461
EP - 470
DO - 10.5220/0007404004610470
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic