A fuzzy multicriteria aggregation method for data analytics: application to insider threat monitoring

Ivan Palomares Carrascosa; Harsha Kalutarage; Yan Huang; Robert McCausland; Paul Miller; Gavin  McWilliams

doi:10.1109/IFSA-SCIS.2017.8023360

A fuzzy multicriteria aggregation method for data analytics: application to insider threat monitoring

Ivan Palomares Carrascosa, Harsha Kalutarage, Yan Huang, Robert McCausland, Paul Miller, Gavin McWilliams

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

5 Citations (Scopus)

366 Downloads (Pure)

Abstract

With the increase in volume, heterogeneity and uncertainty in data, conventional analytics approaches for monitoring users behavior in organisations are no longer sufficient for the effective and reliable detection of malicious activities. This motivates the need for introducing additional analysis techniques. This paper introduces an intelligent fusion method based on fuzzy aggregation functions typically utilized in multi-criteria decision making. The proposed method, which can be integrated with analytics systems, undertakes temporal and multi-criteria fusion processes on pre-analyzed data, to enhance effective monitoring and decision-making. An application to a prominent area of research in the cyber-security domain, the insider threat problem, is shown to validate the usefulness of our method

Original language	English
Title of host publication	2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS 2017)
Subtitle of host publication	Proceedings of a meeting held 27-30 June 2017, Otsu, Japan
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	329-334
Number of pages	6
ISBN (Electronic)	9781509049172
ISBN (Print)	9781509049189
DOIs	https://doi.org/10.1109/IFSA-SCIS.2017.8023360
Publication status	Published - Sep 2017
Event	Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems - Otsu, Japan, Otsu, Japan Duration: 26 Jun 2017 → 30 Jun 2017

Conference

Conference	Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems
Abbreviated title	IFSA-SCIS 2017
Country	Japan
City	Otsu
Period	26/06/17 → 30/06/17

Access to Document

10.1109/IFSA-SCIS.2017.8023360

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Cite this

Palomares Carrascosa, I., Kalutarage, H., Huang, Y., McCausland, R., Miller, P., & McWilliams, G. (2017). A fuzzy multicriteria aggregation method for data analytics: application to insider threat monitoring. In 2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS 2017): Proceedings of a meeting held 27-30 June 2017, Otsu, Japan (pp. 329-334). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/IFSA-SCIS.2017.8023360

Palomares Carrascosa, Ivan ; Kalutarage, Harsha ; Huang, Yan ; McCausland, Robert ; Miller, Paul ; McWilliams, Gavin . / A fuzzy multicriteria aggregation method for data analytics : application to insider threat monitoring. 2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS 2017): Proceedings of a meeting held 27-30 June 2017, Otsu, Japan. Institute of Electrical and Electronics Engineers (IEEE), 2017. pp. 329-334

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abstract = "With the increase in volume, heterogeneity and uncertainty in data, conventional analytics approaches for monitoring users behavior in organisations are no longer sufficient for the effective and reliable detection of malicious activities. This motivates the need for introducing additional analysis techniques. This paper introduces an intelligent fusion method based on fuzzy aggregation functions typically utilized in multi-criteria decision making. The proposed method, which can be integrated with analytics systems, undertakes temporal and multi-criteria fusion processes on pre-analyzed data, to enhance effective monitoring and decision-making. An application to a prominent area of research in the cyber-security domain, the insider threat problem, is shown to validate the usefulness of our method",

author = "{Palomares Carrascosa}, Ivan and Harsha Kalutarage and Yan Huang and Robert McCausland and Paul Miller and Gavin McWilliams",

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Palomares Carrascosa, I, Kalutarage, H, Huang, Y, McCausland, R, Miller, P & McWilliams, G 2017, A fuzzy multicriteria aggregation method for data analytics: application to insider threat monitoring. in 2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS 2017): Proceedings of a meeting held 27-30 June 2017, Otsu, Japan. Institute of Electrical and Electronics Engineers (IEEE), pp. 329-334, Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems, Otsu, Japan, 26/06/17. https://doi.org/10.1109/IFSA-SCIS.2017.8023360

A fuzzy multicriteria aggregation method for data analytics : application to insider threat monitoring. / Palomares Carrascosa, Ivan; Kalutarage, Harsha; Huang, Yan; McCausland, Robert; Miller, Paul; McWilliams, Gavin .

2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS 2017): Proceedings of a meeting held 27-30 June 2017, Otsu, Japan. Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 329-334.

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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AU - McCausland, Robert

AU - Miller, Paul

AU - McWilliams, Gavin

PY - 2017/9

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N2 - With the increase in volume, heterogeneity and uncertainty in data, conventional analytics approaches for monitoring users behavior in organisations are no longer sufficient for the effective and reliable detection of malicious activities. This motivates the need for introducing additional analysis techniques. This paper introduces an intelligent fusion method based on fuzzy aggregation functions typically utilized in multi-criteria decision making. The proposed method, which can be integrated with analytics systems, undertakes temporal and multi-criteria fusion processes on pre-analyzed data, to enhance effective monitoring and decision-making. An application to a prominent area of research in the cyber-security domain, the insider threat problem, is shown to validate the usefulness of our method

AB - With the increase in volume, heterogeneity and uncertainty in data, conventional analytics approaches for monitoring users behavior in organisations are no longer sufficient for the effective and reliable detection of malicious activities. This motivates the need for introducing additional analysis techniques. This paper introduces an intelligent fusion method based on fuzzy aggregation functions typically utilized in multi-criteria decision making. The proposed method, which can be integrated with analytics systems, undertakes temporal and multi-criteria fusion processes on pre-analyzed data, to enhance effective monitoring and decision-making. An application to a prominent area of research in the cyber-security domain, the insider threat problem, is shown to validate the usefulness of our method

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M3 - Conference Contribution (Conference Proceeding)

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PB - Institute of Electrical and Electronics Engineers (IEEE)

Y2 - 26 June 2017 through 30 June 2017

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Palomares Carrascosa I, Kalutarage H, Huang Y, McCausland R, Miller P, McWilliams G. A fuzzy multicriteria aggregation method for data analytics: application to insider threat monitoring. In 2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS 2017): Proceedings of a meeting held 27-30 June 2017, Otsu, Japan. Institute of Electrical and Electronics Engineers (IEEE). 2017. p. 329-334 https://doi.org/10.1109/IFSA-SCIS.2017.8023360