Publication Type:

Journal Article

Source:

International Journal of Innovative Technology and Exploring Engineering (IJITEE), Volume 8, Issue 10 (2019)

URL:

https://www.ijitee.org/wp-content/uploads/papers/v8i10/J94340881019.pdf

Keywords:

Binary classification, Machine learning, Web Application.

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.

Cite this Research Publication

M. Venkata Sa Manish and Rajesh Kannan Megalingam, “Applying and Evaluating Supervised Learning Classification Techniques to Detect Attacks on Web Applications”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol. 8, no. 10, 2019.