Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/50108

"Leadership in Action: How Top Hackers Behave" A Big-Data Approach with Text-Mining and Sentiment Analysis

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Title:"Leadership in Action: How Top Hackers Behave" A Big-Data Approach with Text-Mining and Sentiment Analysis
Authors:Biswas, Baidyanath
Mukhopadhyay, Arunabha
Gupta, Gaurav
Keywords:Data Analytics, Data Mining and Machine Learning for Social Media
hacker forums, community of practice, multinomial logistic regression, sentiment analysis, text-mining
Date Issued:03 Jan 2018
Abstract:This paper examines hacker behavior in dark forums and identifies its significant predictors in the light of "leadership theory" for "communities of practice." We combine techniques from online forum features as well as text-mining and sentiment-analysis of messages. We create a multinomial logistic regression model to achieve role-based hacker classification and validate our model with actual hacker forum data. We identify "total number of messages," "number of threads," "hacker keyword frequency," and "sentiments" as the most significant predictors of expert hacker behavior. We also demonstrate that while disseminating technical knowledge, the hacker community follows Pareto principle. As a recommendation for future research, we build a unique keyword lexicon of the most significant terms derived by tf-idf measure. Such investigation of hacker behavior is particularly relevant for organizations in proactive prevention of cyber-attacks. Foresight on online hacker behavior can help businesses save losses from breaches and additional costs of attack-preventive measures.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/50108
ISBN:978-0-9981331-1-9
DOI:10.24251/HICSS.2018.221
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Data Analytics, Data Mining and Machine Learning for Social Media


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