TY - JOUR
T1 - The insider on the outside
T2 - a novel system for the detection of information leakers in social networks
AU - Cascavilla, Giuseppe
AU - Conti, Mauro
AU - Schwartz, David G.
AU - Yahav, Inbal
N1 - Publisher Copyright:
© 2018, © 2018 Operational Research Society.
PY - 2018/7/4
Y1 - 2018/7/4
N2 - Confidential information is all too easily leaked by naive users posting comments. In this paper we introduce DUIL, a system for Detecting Unintentional Information Leakers. The value of DUIL is in its ability to detect those responsible for information leakage that occurs through comments posted on news articles in a public environment, when those articles have withheld material non-public information. DUIL is comprised of several artefacts, each designed to analyse a different aspect of this challenge: the information, the user(s) who posted the information, and the user(s) who may be involved in the dissemination of information. We present a design science analysis of DUIL as an information system artefact comprised of social, information, and technology artefacts. We demonstrate the performance of DUIL on real data crawled from several Facebook news pages spanning two years of news articles.
AB - Confidential information is all too easily leaked by naive users posting comments. In this paper we introduce DUIL, a system for Detecting Unintentional Information Leakers. The value of DUIL is in its ability to detect those responsible for information leakage that occurs through comments posted on news articles in a public environment, when those articles have withheld material non-public information. DUIL is comprised of several artefacts, each designed to analyse a different aspect of this challenge: the information, the user(s) who posted the information, and the user(s) who may be involved in the dissemination of information. We present a design science analysis of DUIL as an information system artefact comprised of social, information, and technology artefacts. We demonstrate the performance of DUIL on real data crawled from several Facebook news pages spanning two years of news articles.
KW - Cybersecurity
KW - design science research
KW - information leakers
KW - online social networks
KW - sensitive information
KW - threat detection
UR - http://www.scopus.com/inward/record.url?scp=85046040862&partnerID=8YFLogxK
U2 - 10.1080/0960085X.2017.1387712
DO - 10.1080/0960085X.2017.1387712
M3 - Article
AN - SCOPUS:85046040862
SN - 0960-085X
VL - 27
SP - 470
EP - 485
JO - European Journal of Information Systems
JF - European Journal of Information Systems
IS - 4
ER -