Evolutionary Algorithms in Web Security: Exploring Untapped Potential

Abstract

Many aspects of our lives are dependent upon the Web. Research that considers how we can better protect the Web and the products and services delivered using it-that is research into Web Security-is therefore of the upmost importance. Consequently, this paper considers evolutionary algorithms and their potential (which is to tap into and harness the power of natural evolution) within the field of Web Security. The paper provides a concise overview of existing studies that have used evolutionary algorithms as a tool within Web Security. More specifically, the paper considers the manner in which evolutionary algorithms have been applied, the types of problems that they have been applied to, and the way in which they have been assessed or evaluated. Furthermore, an opportunity to better harness the power of natural evolution within the field of Web Security, by applying modern evolutionary algorithms that draw inspiration from the open-ended aspect of natural evolution, is highlighted and discussed.

GeneMiner: A Classification Approach for Detection of XSS Attacks on Web Services

“…Zhou et al [ 21 , 30 ] applied machine learning techniques, [ 33 ] applied a genetic algorithm along with reinforcement learning to detect XSS attacks, and [ 50 ] detected reflected XSS attacks using reinforced learning. It is observed that only 06 out of 16 articles focused on evolutionary algorithms for the detection of XSS attacks, while only 01 focused on XML injection [ 51 , 52 ].…”