Applying and Evaluating Supervised Learning Classification Techniques to Detect Attacks on Web Applications
Madduri Venkata Sai Soma Manish1, Rajesh Kannan Megalingam2
1Madduri Venkata Sai Soma Manish Department of Cybersecurity Systems and Networks, Center for Cybersecurity Systems and Network, Amritapuri, India.
2Dr. Rajesh Kannan Megalingam, Department of Electronics and Communication, Amrita Vishwa Vidyapeetham, Amritapuri, India.
Manuscript received on 02 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 August 2019 | PP: 2222-2225
| Volume-8 Issue-10, August 2019 | Retrieval Number: J94340881019/2019©BEIESP | DOI: 10.35940/ijitee.J9434.0881019
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Binary Classification, Machine Learning, Web
Scope of the Article: Internet and Web Applications