Applying and Evaluating Supervised Learning Classification Techniques to Detect Attacks on Web Applications

Abstract

Web applications are the source of information such as usernames, passwords, personally identifiable information, etc., they act as platforms of knowledge, resource sharing, digital transactions, digital ledgers, etc., and have been a target for attackers. In recent years reports say that there is a spike in the attacks on web applications, especially attacks like SQL injection and Cross Site Scripting have grown in drastic numbers due to discovery of new vulnerabilities. The attacks on web applications still persist due to the nature of attack payloads, as these payloads are highly heterogeneous and look very similar to regular text even web applications with many security features in place may fail to detect these malicious payload strings. To overcome this problem there are various methods described one such method is utilizing machine learning models to detect malicious strings by classifying the input strings given to the web applications. This paper describes the study of six binary classification methods Logistic regression, Naïve Bayes, SGD, ADABoost, Random Forrest, Decision trees using our own dataset and feature set.