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

Biswas, Baidyanath
Mukhopadhyay, Arunabha
Gupta, Gaurav
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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.
Data Analytics, Data Mining and Machine Learning for Social Media, hacker forums, community of practice, multinomial logistic regression, sentiment analysis, text-mining
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