TY - GEN
T1 - A machine learning approach for web intrusion detection
T2 - International Conference on Soft Computing and Signal Processing, ICSCSP 2018
AU - Smitha, Rajagopal
AU - Hareesha, K. S.
AU - Kundapur, Poornima Panduranga
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Open Web Applications Security Project (OWASP), 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 problem context for decoding anomalous patterns. This work explores the performance of algorithms like decision forest, neural networks, support vector machine, and logistic regression. 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 workflows have been created using Microsoft Azure Machine Learning Studio (MAMLS), a scalable machine learning platform which facilitates an integrated development environment to data scientists.
AB - Open Web Applications Security Project (OWASP), 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 problem context for decoding anomalous patterns. This work explores the performance of algorithms like decision forest, neural networks, support vector machine, and logistic regression. 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 workflows have been created using Microsoft Azure Machine Learning Studio (MAMLS), a scalable machine learning platform which facilitates an integrated development environment to data scientists.
UR - http://www.scopus.com/inward/record.url?scp=85061078024&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85061078024&partnerID=8YFLogxK
U2 - 10.1007/978-981-13-3600-3_12
DO - 10.1007/978-981-13-3600-3_12
M3 - Conference contribution
AN - SCOPUS:85061078024
SN - 9789811335990
T3 - Advances in Intelligent Systems and Computing
SP - 119
EP - 133
BT - Soft Computing and Signal Processing - Proceedings of ICSCSP 2018
A2 - Prasad, V. Kamakshi
A2 - Reddy, G. Ram Mohana
A2 - Wang, Jiacun
A2 - Reddy, V. Sivakumar
PB - Springer Verlag
Y2 - 22 June 2018 through 23 June 2018
ER -