Preventing Poisoning Attacks On AI Based Threat Intelligence Systems

dc.contributor.authorKhurana, Nitika
dc.contributor.authorMittal, Sudip
dc.contributor.authorPiplai, Aritran
dc.contributor.authorJoshi, Anupam
dc.date.accessioned2020-07-22T17:30:44Z
dc.date.available2020-07-22T17:30:44Z
dc.date.issued2019-12-05
dc.description2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP) 13-16 Oct. 2019, Pittsburgh, PA, USA, USAen_US
dc.description.abstractAs AI systems become more ubiquitous, securing them becomes an emerging challenge. Over the years, with the surge in online social media use and the data available for analysis, AI systems have been built to extract, represent and use this information. The credibility of this information extracted from open sources, however, can often be questionable. Malicious or incorrect information can cause a loss of money, reputation, and resources; and in certain situations, pose a threat to human life. In this paper, we use an ensembled semi-supervised approach to determine the credibility of Reddit posts by estimating their reputation score to ensure the validity of information ingested by AI systems. We demonstrate our approach in the cybersecurity domain, where security analysts utilize these systems to determine possible threats by analyzing the data scattered on social media websites, forums, blogs, etc.en_US
dc.description.sponsorshipThe work was partially supported by a gift from IBM Research, USA.en_US
dc.description.urihttps://ieeexplore.ieee.org/document/8918803en_US
dc.format.extent6 pagesen_US
dc.genreconference papers and proceedings preprintsen_US
dc.identifierdoi:10.13016/m2sn1g-sqoh
dc.identifier.citationN. Khurana, S. Mittal, A. Piplai and A. Joshi, "Preventing Poisoning Attacks On AI Based Threat Intelligence Systems," 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP), Pittsburgh, PA, USA, 2019, pp. 1-6, doi: 10.1109/MLSP.2019.8918803.en_US
dc.identifier.uri10.1109/MLSP.2019.8918803
dc.identifier.urihttp://hdl.handle.net/11603/19221
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectUMBC Ebiquity Research Group
dc.titlePreventing Poisoning Attacks On AI Based Threat Intelligence Systemsen_US
dc.typeTexten_US

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