Classification of malicious web code by machine learning

Abstract

Web applications make life more convenient through on the activities. Many web applications have several kind of user input (e.g. personal information, a user's comment of commercial goods, etc.) for the activities. However, there are various vulnerabilities in input functions of web applications. It is possible to try malicious actions using free accessibility of the web applications. The attacks by exploitation of these input vulnerabilities enable to be performed by injecting malicious web code; it enables one to perform various illegal actions, such as SQL Injection Attacks (SQLIAs) and Cross Site Scripting (XSS). These actions come down to theft, replacing personal information, or phishing. Many solutions have devised for the malicious web code, such as AMNESIA [1] and SQL Check [2], etc. The methods use parser for the code, and limited to fixed and very small patterns, and are difficult to adapt to variations. Machine learning method can give leverage to cover far broader range of malicious web code and is easy to adapt to variations and changes. Therefore, we suggests adaptable classification of malicious web code by machine learning approach such as Support Vector Machine (SVM)[3], Naive-Bayes[4], and k-Nearest Neighbor Algorithm[5] for detecting the exploitation user inputs.