Counter-masquerading : a logicist-AI approach to interventionist strategies

Authors
Ghosh, Rikhiya
ORCID
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Other Contributors
Bringsjord, Selmer
Nirenburg, Sergei
Yener, Bülent, 1959-
McShane, Marjorie Joan, 1967-
Cassimatis, Nicholas L. (Nicholas Louis), 1971-
Issue Date
2020-08
Keywords
Computer science
Degree
PhD
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This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
Full Citation
Abstract
From times immemorial, masquerading has been a problem that has taken many shapes and forms. From physically impersonating a person to writing on behalf of someone, the degree and sophistication of such impersonation has varied considerably. Detection of masquerading has of course been with us as long as masquerading itself; and such detection is often a very difficult challenge. With the advent of social media, many new and often deleterious ways of masquerading have arrived on the scene. The influence of social media can, we all know, sometime shape opinions; the shift of modern life toward (at least in part) a virtual one has given rise to a plethora of new problems about online authorship, which is commonly termed the cybersecurity problem of masquerading. Simple stylometry, which long included finding penman strokes, grammatical patterns, and finding types of words used has now been "upgraded'' to authorship attribution, using 21st-century natural-language processing techniques (NLP) on large datasets. Counter-masquerading is still an embryonic, fast-developing field with new techniques coming out, it seems, every week, and because counter-masquerading is difficult, there is plenty of room for improvement.
Description
August 2020
School of Science
Department
Dept. of Computer Science
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
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