Design And Implementation Of Intelligent Web Threat Intelligence Detection System

In the era of big data,massive amounts of information on the network provide convenience for people.At the same time,the sharing and openness of the network also makes it possible for attackers to take advantage of it,and the security problems facing the Internet are becoming increasingly serious.Network attack behavior is mainly reflected in Web applications.Among the many web attacks,botnets,SQL injection attacks,and phishing websites are the most common and cause the strongest harm.Therefore,this paper focuses on detection algorithms for these three threats.This paper optimizes the MLP-based botnet detection algorithm,further analyzes the HTTP traffic of the host and extracts features,which improves the model’s performance by 10%.A CNN-based SQL injection detection algorithm is proposed,and the test finds that the model is accurate.A phishing website detection algorithm combining URL blacklist and MLP is proposed,which can better detect phishing website attacks.This article mines botnet,SQL injection,and phishing website behaviors from massive HTTP traffic,and then designs and implements an intelligent Web threat intelligence detection system.This system integrates functions such as data acquisition and processing,deep learning model training,and threat intelligence detection and display.It is simple to implement and easy to expand,and is an intelligent platform.