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iGen: Toward Automatic Generation and Analysis of Indicators of Compromise (IOCs) using Convolutional Neural Network

Abstract Field of cyber threats is evolving rapidly and every day multitude of new information about malware and Advanced Persistent Threats (APTs) is generated in the form of malware reports, blog articles, forum posts, etc. However, current Threat Intelligence (TI) systems have several limitations. First, most of the TI systems examine and interpret data manually with the help of analysts. Second, some of them generate Indicators of Compromise (IOCs) directly using regular expressions without understanding the contextual meaning of those IOCs from the data sources which allows the tools to include lot of false positives. Third, lot of TI systems consider either one or two data sources for the generation of IOCs, and misses some of the most valuabl... (more)
Created Date 2017
Contributor Panwar, Anupam (Author) / Ahn, Gail-Joon (Advisor) / Doupé, Adam (Committee member) / Zhao, Ziming (Committee member) / Arizona State University (Publisher)
Subject Computer science / CNN / Indicators of Compromise / Intrusion Detection / Machine Learning / NLP / Security
Type Masters Thesis
Extent 54 pages
Language English
Reuse Permissions All Rights Reserved
Note Masters Thesis Computer Science 2017
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis