计算机科学
有效载荷(计算)
Web应用程序
脆弱性(计算)
构造(python库)
Web服务器
深度学习
人工智能
机器学习
数据挖掘
万维网
互联网
计算机安全
计算机网络
网络数据包
作者
J.J. Christy Eunaicy,S. Suguna
标识
DOI:10.1016/j.matpr.2022.03.348
摘要
Due to the network access and security vulnerabilities of web applications, web applications are often targets of cyber-attacks. Attacks against web applications can be extremely dangerous. A lot of damage has been done because of the vulnerability of the application, which lets them access the Web Application database. Monitoring web attacks and generating alarms when a challenge to an attack is detected. This work uses deep learning models (ANN, CNN & RNN) to detect web attacks automatically. To identify the time when the attack on the payload occurred, the work first analyses the web log information provided by the user. To make an attack prediction, the log information is pre-processed. Web-log information is pre-processed to remove duplicate values and missing values and to get the payload information. To encode the fields and normalize (Min-Max) that converts into unique format while predicting and the encoding value also applied. To construct the prediction model for the detection of web attacks, the pre-processed dataset is incorporated into the deep learning classifiers. In the performance evaluation, RNN provided 94% accuracy and 6% error rate, higher than other method.
科研通智能强力驱动
Strongly Powered by AbleSci AI