Genetic & Evolutionary Feature Selection for Author Identification of HTML Associated with Malware

Abstract

Abstract-Malicious software, also known as malware, is a huge problem that costs consumers billions of dollars each year. To solve this problem, a significant amount of research has been dedicated towards detecting malware. In this paper, we introduce a genetic and evolutionary feature selection technique for the identification of HTML code associated with malware. We believe that there may be an association between malware and the HTML code that it is embedded in. Our results show that this technique outperforms previous techniques in terms of recognition accuracy as well as the total number of features needed for recognition.

GA-based selection of vaginal microbiome features associated with bacterial vaginosis

“…GEFeS is an instance of a steady-state GA found in the X-TOOLSS suite of Evolutionary Computations [25]. GEFeS evolved a population of 20 candidate FMs, used uniform crossover, a mutation usage rate of 1.0, and Gaussian mutation of the form 0.2N(0,1).…”

“…GEFeS [25] has been successfully used for feature selection on a variety of recognition and classification problems. GEFeS evolves a population of candidate feature masks (FMs).…”