publication . Other literature type . Preprint . Article . 2018

Predicting Cyber-Events by Leveraging Hacker Sentiment

Ashok Deb; Kristina Lerman; Emilio Ferrara;
  • Published: 15 Nov 2018
  • Publisher: MDPI AG
Abstract
Recent high-profile cyber-attacks exemplify why organizations need better cyber-defenses. Cyber-threats are hard to accurately predict because attackers usually try to mask their traces. However, they often discuss exploits and techniques on hacking forums. The community behavior of the hackers may provide insights into the groups&rsquo
Subjects
free text keywords: sentiment analysis, cyber-security, dark web, Computer Science - Computation and Language, Information technology, T58.5-58.64, Leverage (finance), Computer science, Machine learning, computer.software_genre, computer, Exploit, Deep Web, Predictive power, Hacker, Malware, Computer security, Artificial intelligence, business.industry, business
33 references, page 1 of 3

[1] V. Dutt, Y.-S. Ahn, and C. Gonzalez, “Cyber situation awareness: modeling detection of cyber attacks with instance-based learning theory,” Human Factors, vol. 55, no. 3, pp. 605-618, 2013.

[2] S. Jajodia, P. Liu, V. Swarup, and C. Wang, Cyber situational awareness. Springer, 2009.

[3] U. Franke and J. Brynielsson, “Cyber situational awareness-a systematic review of the literature,” Computers & Security, vol. 46, pp. 18-31, 2014.

[4] S. Freud, “1960. the psychopathology of everyday life,” The standard edition of the complete psychological works of Sigmund Freud, vol. 6, 1901.

[5] B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up?: sentiment classification using machine learning techniques,” in Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10, pp. 79-86, Association for Computational Linguistics, 2002.

[6] S. L. Pfleeger and D. D. Caputo, “Leveraging behavioral science to mitigate cyber security risk,” Computers & security, vol. 31, no. 4, pp. 597-611, 2012. [OpenAIRE]

[7] S. Agarwal and A. Sureka, “Applying social media intelligence for predicting and identifying on-line radicalization and civil unrest oriented threats,” arXiv preprint arXiv:1511.06858, 2015. [OpenAIRE]

[8] S. Asur and B. A. Huberman, “Predicting the future with social media,” in Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent TechnologyVolume 01, pp. 492-499, IEEE Computer Society, 2010.

[9] E. Kalampokis, E. Tambouris, and K. Tarabanis, “Understanding the predictive power of social media,” Internet Research, vol. 23, no. 5, pp. 544-559, 2013. [OpenAIRE]

[10] R. Dingledine, N. Mathewson, and P. Syverson, “Tor: The secondgeneration onion router,” tech. rep., Naval Research Lab Washington DC, 2004. [OpenAIRE]

[11] J. Robertson, A. Diab, E. Marin, E. Nunes, V. Paliath, J. Shakarian, and P. Shakarian, Darkweb Cyber Threat Intelligence Mining. Cambridge University Press, 2017.

[12] E. Nunes, A. Diab, A. Gunn, E. Marin, V. Mishra, V. Paliath, J. Robertson, J. Shakarian, A. Thart, and P. Shakarian, “Darknet and deepnet mining for proactive cybersecurity threat intelligence,” in Intelligence and Security Informatics (ISI), 2016 IEEE Conference on, pp. 7-12, IEEE, 2016.

[13] R. et al, “Sentibench - a benchmark comparison of state-of-the-practice sentiment analysis methods,” in EPJ Data Science, pp. 5-23, 2016.

[14] C. Hutto and E. Gilbert, “Vader: a parsimonious rule-based model for sentiment analysis of social media text,” in 8th international AAAI conference on weblogs and social media (ICWSM), AAAI, 2014.

[15] J. W. Pennebaker, M. E. Francis, and R. J. Booth, “Linguistic inquiry and word count: Liwc 2001,” Mahway: Lawrence Erlbaum Associates, vol. 71, no. 2001, p. 2001, 2001.

33 references, page 1 of 3
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