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Full-Text Articles in Physical Sciences and Mathematics
On The Effectiveness Of Generic Malware Models, Naman Bagga, Fabio Di Troia, Mark Stamp
On The Effectiveness Of Generic Malware Models, Naman Bagga, Fabio Di Troia, Mark Stamp
Faculty Publications, Computer Science
Malware detection based on machine learning typically involves training and testing models for each malware family under consideration. While such an approach can generally achieve good accuracy, it requires many classification steps, resulting in a slow, inefficient, and potentially impractical process. In contrast, classifying samples as malware or benign based on more generic “families” would be far more efficient. However, extracting common features from extremely general malware families will likely result in a model that is too generic to be useful. In this research, we perform controlled experiments to determine the tradeoff between generality and accuracy—over a variety of machine …
Support Vector Machines For Image Spam Analysis, Aneri Chavda, Katerina Potika, Fabio Di Troia, Mark Stamp
Support Vector Machines For Image Spam Analysis, Aneri Chavda, Katerina Potika, Fabio Di Troia, Mark Stamp
Faculty Publications, Computer Science
Email is one of the most common forms of digital communication. Spam is unsolicited bulk email, while image spam consists of spam text embedded inside an image. Image spam is used as a means to evade text-based spam filters, and hence image spam poses a threat to email-based communication. In this research, we analyze image spam detection using support vector machines (SVMs), which we train on a wide variety of image features. We use a linear SVM to quantify the relative importance of the features under consideration. We also develop and analyze a realistic “challenge” dataset that illustrates the limitations …