7 papers
Concept Drift Detection and Adaptive Retraining of Malware Classification Models
Christofer Washington Berruz Chungata, Martin Jurecek, Katerina Potika +2
Concept drift refers to changes over time in the statistical properties of data, as compared to the data that was used to train a learning model. Machine learning models for malwar…
A Comparison of Malware Image Transformations Using Grad-CAM and Hybrid Learning Models
Vibha Bhavikatti, Mark Stamp
Recent studies have shown that binary-to-image representations can enable effective machine learning-based results for malware detection and classification. However, performance ca…
Robustness of AI-Art Detectors under Generator Shift
Shivank Singh Thakur, Meien Li, Mark Stamp
Text-to-image generative models have advanced rapidly, with modern Diffusion Transformer architectures producing images that are increasingly difficult to distinguish from human-cr…
Detecting AI-generated Artwork
Meien Li, Mark Stamp
The high efficiency and quality of artwork generated by Artificial Intelligence (AI) has created new concerns and challenges for human artists. In particular, recent improvements i…
Cluster Analysis and Concept Drift Detection in Malware
Aniket Mishra, Mark Stamp
Concept drift refers to gradual or sudden changes in the properties of data that affect the accuracy of machine learning models. In this paper, we address the problem of concept dr…
Image-Based Malware Classification Using QR and Aztec Codes
Atharva Khadilkar, Mark Stamp
In recent years, the use of image-based techniques for malware detection has gained prominence, with numerous studies demonstrating the efficacy of deep learning approaches such as…