A Particle Swarm Optimized Feature selection and Learning for Fault Classification in Web-Apps
Web attacks are considered to be serious attack as the users of website may not get proper services if web application got an attack. The proposed work aim to classify web attacks as malicious and normal. There are 2 datasets used in proposed work are
- HTTP dataset CSIS 2010
- FWAF dataset created by FSECURITY
Deep neural network DNN algorithm is used for this study.
The efficient feature selection algorithm applied in this proposed work is PSO (Particle Swarm Optimization). The hybrid model of web attack detection is implemented.
The model outperformed with highest accuracy of 99.5% using FWAF dataset
The dataset is classified as binary classification model using DNN algorithm and source code is available in Python. Keras and Tensorflow are used for deploying and building a deep learning model for web attack detection. The following output image shows the accuracy of the DNN algorithm.
Further, the working model of the proposed work is available in following demo
Such kind of intrusion detection and attack detection and classification is available in the following link and provided more information
Intrusion detection and classification