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Creators/Authors contains: "Lazarine, Ben"

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  1. ; ; ; ; (, IEEE)
    Free, publicly-accessible full text available May 15, 2026
  2. ; ; ; (, HICSS)
    Free, publicly-accessible full text available January 1, 2026
  3. ; ; ; ; ; (, Journal of Management Information Systems)
    Full Text Available 
  4. ; ; ; (, HICSS)
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  5. ; ; ; ; (, ” IEEE International Conference on Data Mining Workshop on Machine Learning for Cybersecurity)
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  6. ; ; ; ; ; ; (, IEEE Intelligence and Security Informatics)
    null (Ed.)
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  7. ; ; ; ; ; ; (, IEEE Intelligence and Security Informatics)
    null (Ed.)
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  8. ; ; ; ; ; ; (, 2019 IEEE Conference on Intelligence and Security Informatics)
    The frequency and costs of cyber-attacks are increasing each year. By the end of 2019, the total cost of data breaches is expected to reach $2.1 trillion through the evergrowing online presence of enterprises and their consumers. The tools to perform these attacks and the breached data can often be purchased within the Dark-net. Many of the threat actors within this realm use its various platforms to broker, discuss, and strategize these cyber-threat assets. To combat these attacks, researchers are developing Cyber-Threat Intelligence (CTI) tools to proactively monitor the ever-growing online hacker community. This paper will detail the creation and use of a CTI tool that leverages a social network to identify cyber-threats across major Dark-net data sources. Through this network, emerging threats can be quickly identified so proactive or reactive security measures can be implemented. 
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