Graduation Term
Spring 2026
Degree Name
Master of Science (MS)
Department
School of Information Technology: Information Systems
Committee Chair
Elahe Javadi
Committee Member
Abdelmounaam Rezgui
Abstract
The field of artificial intelligence is based upon the premise of constructing architectures through which to propagate training data. However, the majority of existing research literature is focused on architecture. While necessary, the attention devoted to the architecture should not so precipitously exceed that of the data. It should be noted that this disparity is not without reasonable cause. Data quality is often exceedingly difficult to verify due to particularities of the field or subfield; LLM repositories of text are distinct from image recognition pictures of dog breeds which are distinct from EEG waveforms of human brains which are distinct from malware binaries, etc.. Further, such verification is also exceedingly time-consuming with a requisite cost in human resources to perform the task. Countless papers are devoted to the documentation of data analysis schema created specific to each field’s own data-types and problem-areas. With expansion being a core tenet of our species’ nature, the creation of new fields of inquiry as well as the exponential generation of data from all fields, new and existing, threaten to swamp human analytical capacity. As such, progress towards automation is required.
Access Type
Thesis-Open Access
Recommended Citation
Ionescu, Matei, "Data Defines Success: Algorithm for Dataset Quality Assessment in Deep Learning for Malware Detection" (2026). Theses and Dissertations. 2288.
https://ir.library.illinoisstate.edu/etd/2288
Included in
Artificial Intelligence and Robotics Commons, Cybersecurity Commons, Data Science Commons, Numerical Analysis and Scientific Computing Commons