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research-article

Behavior analysis in the medical sector: theory and practice

Authors:

Mahdi Alizadeh,

Nicola ZannoneAuthors Info & Claims

SAC '18: Proceedings of the 33rd Annual ACM Symposium on Applied Computing

Pages 1637 - 1646

https://doi.org/10.1145/3167132.3167307

Published: 09 April 2018 Publication History

Abstract

Behavior analysis has received considerable attention over recent years. In this paper, we apply behavior analysis to study the use of the Break-The-Glass (BTG) procedure at the Academic Medical Center (AMC), a large Dutch hospital. Similar to most hospitals, AMC employs the BTG procedure to deal with emergencies, which allows users to access patient data that they would not be normally allowed to access. This flexibility can be misused by users, leading to legal and financial consequences for the hospital. To assist AMC in the detection of possible misuses of the BTG procedure, in this work, we present an approach to analyze user behavior and apply it to a log collected from AMC. We partition users into different subgroups and build self-explanatory histogram-based profiles for users and subgroups. By comparing profiles, we measure to what extent users behave differently from their peers. The discussion of our findings with experts at AMC has shown that our approach can provide meaningful insights on user behavior and histograms are easy to understand and facilitate the investigation of suspicious behaviors.

References

[1]

Arya Adriansyah, Boudewijn F van Dongen, and Nicola Zannone. 2013. Controlling Break-the-Glass through Alignment. In Proceedings of International Conference on Social Computing. IEEE, 606--611.

Digital Library

[2]

Charu Aggarwal and Chandan Reddy. 2013. Data clustering: algorithms and applications. CRC press.

Digital Library

[3]

Ja'far Alqatawna, Erik Rissanen, and Babak Sadighi. 2007. Overriding of Access Control in XACML. In Proceedings of International Workshop on Policies for Distributed Systems and Networks. IEEE, 87--95.

Digital Library

[4]

Ajit Appari and M Eric Johnson. 2010. Information security and privacy in healthcare: current state of research. IJIEM 6, 4 (2010), 279--314.

[5]

Hanieh Azkia, Nora Cuppens-Boulahia, Frédéric Cuppens, Gouenou Coatrieux, and Said Oulmakhzoune. 2015. Deployment of a posteriori access control using IHE ATNA. International Journal of Information Security 14, 5 (2015), 471--483.

Digital Library

[6]

Achim D Brucker and Helmut Petritsch. 2009. Extending access control models with break-glass. In Proc. of SACMAT. ACM, 197--206.

Digital Library

[7]

Varun Chandola, Arindam Banerjee, and Vipin Kumar. 2009. Anomaly detection: A survey. Comput. Surveys 41, 3 (2009), 15.

Digital Library

[8]

Elisa Costante, Jerry den Hartog, Milan Petkovic, Sandro Etalle, and Mykola Pechenizkiy. 2017. A white-box anomaly-based framework for database leakage detection. J. Inf. Sec. Appl. 32 (2017), 27--46.

Digital Library

[9]

Wayne W Daniel and Chad Lee Cross. 1995. Biostatistics: a foundation for analysis in the health sciences. Wiley New York.

[10]

European Union. 2016. General Data Protection Regulation. http://data.europa.eu/eli/reg/2016/679/oj. (2016). Accessed: 2017-10-15.

[11]

Anna Ferreira, Ricardo Cruz-Correia, Luis Antunes, Pedro Farinha, E Oliveira-Palhares, David Chadwick, and Altamiro Costa-Pereira. 2006. How to break access control in a controlled manner. In Proc. of Computer-Based Medical Systems. IEEE, 847--854.

Digital Library

[12]

Zengyou He, Xiaofei Xu, and Shengchun Deng. 2003. Discovering cluster-based local outliers. Pattern Recognition Letters 24, 9 (2003), 1641--1650.

Digital Library

[13]

Mon-Fong Jiang, Shian-Shyong Tseng, and Chih-Ming Su. 2001. Two-phase clustering process for outliers detection. Pattern Recognition Letters 22, 6 (2001), 691--700.

Digital Library

[14]

Hung-Jen Liao, Chun-Hung Lin, Ying Lin, and Kuang Tung. 2013. Intrusion detection system: A comprehensive review. J. of Network and Computer Applications 36, 1 (2013), 16--24.

Digital Library

[15]

James MacQueen. 1967. Some methods for classification and analysis of multivariate observations. In Proceedings of Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1. 281--297.

[16]

Srdjan Marinovic, Naranker Dulay, and Morris Sloman. 2014. Rumpole: An introspective break-glass access control language. TISSEC 17, 1 (2014), 2.

Digital Library

[17]

Dan Pelleg and Andrew W Moore. 2000. X-means: Extending K-means with Efficient Estimation of the Number of Clusters. In Proceedings of International Conference on Machine Learning. Morgan Kaufmann Publishers Inc., 727--734.

Digital Library

[18]

Ana M Pires and Carla Santos-Pereira. 2005. Using Clustering and Robust Estimators to Detect Outliers in Multivariate Data. In Proc. of Int. Conf. on Robust Statistics.

[19]

Ponemon Institute 2015. Cost of data breach study: global analysis. (2015). https://www.ponemon.org/library/sixth-annual-benchmark-study-on-privacy-security-of-healthcare-data-1.

[20]

Sigrid Schefer-Wenzl and Mark Strembeck. 2014. Model-driven specification and enforcement of RBAC break-glass policies for process-aware information systems. Information and Software Technology 56, 10 (2014), 1289--1308.

Digital Library

[21]

Karlton Sequeira and Mohammed Zaki. 2002. ADMIT: anomaly-based data mining for intrusions. In Proc. of KDD. ACM, 386--395.

Digital Library

Cited By

Zhong HWang JWang XWang XXiao WTang XZang L(2024)DBPrompt: A Database Anomaly Operation Detection and Analysis via Prompt LearningAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5603-2_29(357-368)Online publication date: 1-Aug-2024
https://doi.org/10.1007/978-981-97-5603-2_29
Zhong HZhang FZhao YZhang WXiao WTang XZang L(2023)UDAD: An Accurate Unsupervised Database Anomaly Detection Method2023 IEEE International Performance, Computing, and Communications Conference (IPCCC)10.1109/IPCCC59175.2023.10253824(109-115)Online publication date: 17-Nov-2023
https://doi.org/10.1109/IPCCC59175.2023.10253824
Li SYin QLi GLi QLiu ZZhu JIves ZBonifati AEl Abbadi A(2022)Unsupervised Contextual Anomaly Detection for Database SystemsProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3517861(788-802)Online publication date: 10-Jun-2022
https://dl.acm.org/doi/10.1145/3514221.3517861
Show More Cited By

Index Terms

Behavior analysis in the medical sector: theory and practice
1. Applied computing
  1. Computer forensics
    1. Investigation techniques
2. Security and privacy
  1. Database and storage security
    1. Database activity monitoring

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Information & Contributors

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Published In

cover image ACM Conferences

SAC '18: Proceedings of the 33rd Annual ACM Symposium on Applied Computing

April 2018

2327 pages

ISBN:9781450351911

DOI:10.1145/3167132

Conference Chairs:
Hisham M. Haddad
Kennesaw State University
,
Roger L. Wainwright
University of Tulsa
,
Richard Chbeir
University of Pau & Pays Adour, France

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 April 2018

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NWO CyberSecurity programme

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SAC 2018

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SIGAPP

SAC 2018: Symposium on Applied Computing

April 9 - 13, 2018

Pau, France

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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SAC '25

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The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

Catania , Italy

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Cited By

Zhong HWang JWang XWang XXiao WTang XZang L(2024)DBPrompt: A Database Anomaly Operation Detection and Analysis via Prompt LearningAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5603-2_29(357-368)Online publication date: 1-Aug-2024
https://doi.org/10.1007/978-981-97-5603-2_29
Zhong HZhang FZhao YZhang WXiao WTang XZang L(2023)UDAD: An Accurate Unsupervised Database Anomaly Detection Method2023 IEEE International Performance, Computing, and Communications Conference (IPCCC)10.1109/IPCCC59175.2023.10253824(109-115)Online publication date: 17-Nov-2023
https://doi.org/10.1109/IPCCC59175.2023.10253824
Li SYin QLi GLi QLiu ZZhu JIves ZBonifati AEl Abbadi A(2022)Unsupervised Contextual Anomaly Detection for Database SystemsProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3517861(788-802)Online publication date: 10-Jun-2022
https://dl.acm.org/doi/10.1145/3514221.3517861
Genemo M(2022)Suspicious activity recognition for monitoring cheating in examsProceedings of the Indian National Science Academy10.1007/s43538-022-00069-2Online publication date: 24-Feb-2022
https://doi.org/10.1007/s43538-022-00069-2
Khan MFoley SO’Sullivan B(2022)Database Intrusion Detection Systems (DIDs): Insider Threat Detection via Behaviour-Based Anomaly Detection Systems - A Brief Survey of Concepts and ApproachesEmerging Information Security and Applications10.1007/978-3-030-93956-4_11(178-197)Online publication date: 12-Jan-2022
https://doi.org/10.1007/978-3-030-93956-4_11
Saba TRehman ALatif RFati SRaza MSharif M(2021)Suspicious Activity Recognition Using Proposed Deep L4-Branched-Actionnet With Entropy Coded Ant Colony System OptimizationIEEE Access10.1109/ACCESS.2021.30910819(89181-89197)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3091081
Khan MFoley SO'Sullivan B(2020)Quantitatively Measuring Privacy in Interactive Query Settings Within RDBMS FrameworkFrontiers in Big Data10.3389/fdata.2020.000113Online publication date: 5-May-2020
https://doi.org/10.3389/fdata.2020.00011
Wickramage CFidge COuyang CSahama T(2019)Generating Log Requirements for Checking Conformance against Healthcare Standards using Workflow ModellingProceedings of the Australasian Computer Science Week Multiconference10.1145/3290688.3290739(1-10)Online publication date: 29-Jan-2019
https://dl.acm.org/doi/10.1145/3290688.3290739
Argento LMargheri APaci FSassone VZannone N(2018)Towards Adaptive Access ControlData and Applications Security and Privacy XXXII10.1007/978-3-319-95729-6_7(99-109)Online publication date: 10-Jul-2018
https://doi.org/10.1007/978-3-319-95729-6_7

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