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Exploring Open Source Intelligence for cyber threat Prediction

Adewopo, Victor A
http://orcid.org/0000-0002-1700-5241
http://rave.ohiolink.edu/etdc/view?acc_num=ucin162491804723753

Abstract Details

2021, MS, University of Cincinnati, Education, Criminal Justice, and Human Services: Information Technology.
The cyberspace is one of the most complex systems ever built by humans, the utilization of cybertechnology resources are used ubiquitously by many, but sparsely understood by the majority of the users. In the past, cyberattacks are usually orchestrated in a random pattern of attack to lure unsuspecting targets. More evidence has demonstrated that cyberattack knowledge is shared among individuals using social media and hacker forums in the virtual ecosystem. Previous research work focused on using machine learning algorithms (SVM) to identify threats [1]. Rodriguez et al. utilized sentiments and data mining techniques in classifying threats [2]. This research developed a novel framework for identifying threats and predicting vulnerability exposure. The methodology used in this research combined information extracted from the deep web and surface web containing technical indicators of threats. This thesis showcased that potential cyberthreat can be predicted from open-source data using a deep learning algorithm (LSTM). The developed model utilized open-source intelligence to identify existing threat in an input search and identify the severity level of the threat by crawling the National vulnerability Database(NVD) and Common Vulnerabilities and Exposures (CVE) Database for a list of published threats related to the search term with an accuracy of 91%, precision of 90% and recall of 91% on test data
Bilal Gonen, Ph.D. (Committee Chair)
Nelly Elsayed, Ph.D. (Committee Member)
M. Murat Ozer, Ph.D. (Committee Member)
69 p.
Information Technology
LSTM; Machine Learning; Deep Learning; Threat Detection; Online Social Media

Recommended Citations

Citations

  • Adewopo, V. A. (2021). Exploring Open Source Intelligence for cyber threat Prediction [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin162491804723753

    APA Style (7th edition)

  • Adewopo, Victor. Exploring Open Source Intelligence for cyber threat Prediction. 2021. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin162491804723753.

    MLA Style (8th edition)

  • Adewopo, Victor. "Exploring Open Source Intelligence for cyber threat Prediction." Master's thesis, University of Cincinnati, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin162491804723753

    Chicago Manual of Style (17th edition)

ucin162491804723753
590
© 2021, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.