Date of Award
Fall 1-15-2023
Degree Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science
First Advisor
Margaret McCoey
Language
ENGLISH
Abstract
Artificial Intelligence (AI) adoption is rapidly being deployed in a number of fields, from banking and finance to healthcare, robotics, transportation, military, e-commerce and social networks. Grand View Research estimates that the global AI market was worth 93.5 billion in 2021 and that it will increase at a compound annual growth rate (CAGR) of 38.1% from 2022 to 2030. According to a 2020 MIT Sloan Management survey, 87% of multinational corporations believe that AI technology will provide a competitive edge. Artificial Intelligence relies heavily on datasets to train its models. The more data, the better it learns and predicts. However, the downside to AI is data, data that can be manipulated or poisoned. A new type of threat is emerging, and that threat is data poisoning. Data Poisoning is challenging and time consuming to spot and when it is discovered, the damage is already extensive. Unlike traditional attack that is caused by errors found in code, this new threat is attacking the AI training data used in its algorithm. Data is now being weaponized. It requires minimal effort but can cause substantial damages. It only takes 1-3% of data to be poisoned to severely diminish an AI’s ability to produce accurate predictions.
Recommended Citation
Simms, Nary, "Data Poisoning: A New Threat to Artificial Intelligence" (2023). Mathematics and Computer Science Capstones. 47.
https://digitalcommons.lasalle.edu/mathcompcapstones/47
Included in
Databases and Information Systems Commons, Data Science Commons, Information Security Commons