Accession Number:



Context-Aware Malware Detection Using Topic Modeling (Preprint)

Descriptive Note:

[Technical Report, Master's Thesis]

Corporate Author:

University of Cincinnati

Personal Author(s):

Report Date:


Pagination or Media Count:



Whether or not a piece of software is malicious is entirely dependent upon the context in which the software is run. Current malware detection strategies have shown high classification accuracy, but they lack contextual considerations. The objective of this thesis is to address the development of a context-aware malware detection system. A definition of context and how it pertains to malware detection is discussed. Based on this definition, two proof-of-concept context-aware models utilizing Latent Dirichlet Allocation are developed to address different aspects of context. These models provide insight into the challenges of including context in malware detection models, and future work to improve the contextual aspects of the models is discussed.


Subject Categories:

  • Computer Systems Management and Standards

Distribution Statement:

[A, Approved For Public Release]