Volume 3 - Issue 1 – 2
Guest Editorial: Frontiers in Insider Threats and Data Leakage Prevention
- Yoshiaki Hori
Kyushu University 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
hori@inf.kyushu-u.ac.jp
- William Claycomb
CERT Programy Software Engineering Institute Carnegie Mellon University Pittsburgh, PA, USA
claycomb@cert.org
- Kangbin Yim
Soonchunhyang University, Dept. of Information Security Engineering 646 Eupnae, Shinchang, Asan, Korea
yim@sch.ac.kr
Keywords: Journal of Wireless Mobile Networks, Ubiquitous Computing, Dependable Applications
Abstract
Organizations continue to be plagued by information leaks caused by insiders with legitimate access to
critical or proprietary information. Such unauthorized leaks may result in significant damage to competitiveness,
reputation and finances, and organizations should consider proactive approaches to preventing,
detecting, and responding to this threat. In this special issue, we have selected eight papers describing
recent work on insider threat and data leakage prevention. These include four papers [1][2][3][4] derived
from the third International Workshop on Managing Insider Security Threats (MIST 2011)1 in conjunction
with the third IEEE International Conference on Intelligent Networking and Collaborative Systems
(IEEE INCoS 2011).
In the first paper, titled “From Insider Threats to Business Processes that are Secure-by-Design” [1],
the author suggests that insider threat is a placeholder term indicating the transition from securing IT
infrastructures to securing the socio-technical systems. While observing that the concept of an insider
is not helpful in today’s dynamic heterogeneous organizations, he adopts “business processes that are
secure-by-design (sustainable business processes)” as a new paradigm where those processes remain
viable even when attacks are launched with insider knowledge. Finally, the author presents two research
challenges for the sustainable business processes, modelling socio-technical systems and exploring the
foundations of judgement-based risk analysis methods.