Detecting Potential Insider Threat: Analyzing Insiders’ Sentiment Exposed in Social Media
Joint Authors
Lee, Kyungho
Park, Won
You, Youngin
Source
Security and Communication Networks
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-07-18
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Abstract EN
In the era of Internet of Things (IoT), impact of social media is increasing gradually.
With the huge progress in the IoT device, insider threat is becoming much more dangerous.
Trying to find what kind of people are in high risk for the organization, about one million of tweets were analyzed by sentiment analysis methodology.
Dataset made by the web service “Sentiment140” was used to find possible malicious insider.
Based on the analysis of the sentiment level, users with negative sentiments were classified by the criteria and then selected as possible malicious insiders according to the threat level.
Machine learning algorithms in the open-sourced machine learning software “Weka (Waikato Environment for Knowledge Analysis)” were used to find the possible malicious insider.
Decision Tree had the highest accuracy among supervised learning algorithms and K-Means had the highest accuracy among unsupervised learning.
In addition, we extract the frequently used words from the topic modeling technique and then verified the analysis results by matching them to the information security compliance elements.
These findings can contribute to achieve higher detection accuracy by combining individual’s characteristics to the previous studies such as analyzing system behavior.
American Psychological Association (APA)
Park, Won& You, Youngin& Lee, Kyungho. 2018. Detecting Potential Insider Threat: Analyzing Insiders’ Sentiment Exposed in Social Media. Security and Communication Networks،Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1214330
Modern Language Association (MLA)
Park, Won…[et al.]. Detecting Potential Insider Threat: Analyzing Insiders’ Sentiment Exposed in Social Media. Security and Communication Networks No. 2018 (2018), pp.1-8.
https://search.emarefa.net/detail/BIM-1214330
American Medical Association (AMA)
Park, Won& You, Youngin& Lee, Kyungho. Detecting Potential Insider Threat: Analyzing Insiders’ Sentiment Exposed in Social Media. Security and Communication Networks. 2018. Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1214330
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1214330