Publication Type : Journal Article
Publisher : International Journal of Innovative Technology and Exploring Engineering (IJITEE)
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.
Campus : Amritapuri
School : School of Engineering
Center : Humanitarian Technology (HuT) Labs
Department : Electronics and Communication
Year : 2019
Abstract : Web applications are the source of information suchas usernames, passwords, personally identifiable information,etc., they act as platforms of knowledge, resource sharing, digitaltransactions, digital ledgers, etc., and have been a target forattackers. In recent years reports say that there is a spike in theattacks on web applications, especially attacks like SQL injectionand Cross Site Scripting have grown in drastic numbers due todiscovery of new vulnerabilities. The attacks on web applicationsstill persist due to the nature of attack payloads, as these payloadsare highly heterogeneous and look very similar to regular texteven web applications with many security features in place mayfail to detect these malicious payload strings. To overcome thisproblem there are various methods described one such method isutilizing machine learning models to detect malicious strings byclassifying the input strings given to the web applications. Thispaper describes the study of six binary classification methodsLogistic regression, Naïve Bayes, SGD, ADABoost, RandomForrest, 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.