A Machine Learning Approach for Web Intrusion Detection: MAMLS Perspective

Smitha, R and Kundapur, Poornima Panduranga (2018) A Machine Learning Approach for Web Intrusion Detection: MAMLS Perspective. In: International Conference on Soft Computing and Signal Processing, Malla Reddy College of Engineering and Technology, 21/06/2018, Bhubaneswar, Khurda, Odisha, India.

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Abstract

OWASP (Open Web Applications Security Project), an open source community committed to serve application developers and security professionals has always accentuated on the dire consequences of web application vulnerabilities like SQLI, XSS, LDAP and Buffer overflow attacks frequently occurring on the web application threat landscape. Since these attacks are difficult to comprehend, machine learning algorithms are often applied to this prob-lem context for decoding anomalous patterns. This work explores the performance of algo-rithms like Decision Forest, Neural Networks, Support Vector Machine and Logistic Regres-sion. Their performance has been evaluated using standard performance metrics. HTTP CSIC 2010, a web intrusion detection dataset is used in this study. Experimental results indicate that SVM and LR have been superior in their performance than their counterparts. Predictive work-flows have been created using MAMLS (Microsoft Azure Machine Learning Studio), a scalable machine learning platform which facilitates an integrated development environment to data scientists.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: HTTP CSIC 2010, Azure Machine Learning, Logistic Regression, Support Vector Machine.
Subjects: Engineering > MIT Manipal > MCA
Depositing User: MIT Library
Date Deposited: 11 Jan 2019 09:28
Last Modified: 11 Jan 2019 09:28
URI: http://eprints.manipal.edu/id/eprint/152890

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