Implementing Data Exfiltration Defense in Situ: A Survey of Countermeasures and Human Involvement

Author:

Chung Mu-Huan1ORCID,Yang Yuhong1ORCID,Wang Lu1ORCID,Cento Greg2ORCID,Jerath Khilan2ORCID,Raman Abhay2ORCID,Lie David1ORCID,Chignell Mark H.1ORCID

Affiliation:

1. University of Toronto, Canada

2. Sun Life Financial, Canada

Abstract

In this article we consider the problem of defending against increasing data exfiltration threats in the domain of cybersecurity. We review existing work on exfiltration threats and corresponding countermeasures. We consider current problems and challenges that need to be addressed to provide a qualitatively better level of protection against data exfiltration. After considering the magnitude of the data exfiltration threat, we outline the objectives of this article and the scope of the review. We then provide an extensive discussion of present methods of defending against data exfiltration. We note that current methodologies for defending against data exfiltration do not connect well with domain experts, both as sources of knowledge and as partners in decision-making. However, human interventions continue to be required in cybersecurity. Thus, cybersecurity applications are necessarily socio-technical systems that cannot be safely and efficiently operated without considering relevant human factor issues. We conclude with a call for approaches that can more effectively integrate human expertise into defense against data exfiltration.

Funder

Mitacs

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

Reference232 articles.

1. Network and Distributed Systems Security Symposium (NDSS’19) 2019 Nodoze: Combatting threat alert fatigue with automated provenance triage

2. Social Engineering Threat and Defense: A Literature Survey

3. Machine learning based model to identify firewall decisions to improve cyber-defense;Al-Haija Qasem Abu;International Journal on Advanced Science Engineering and Information Technology,2021

4. M. Afshar S. Samet and H. Usefi. 2021. Incorporating behavior in attribute based access control model using machine learning. In 2021 IEEE International Systems Conference (SysCon) . IEEE 1–8.

5. Efficient string matching;Aho Alfred V.;Commun. ACM,1975

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