Injection attack detection method based on gating cycle unit and attention mechanism

Abstract

The invention discloses an injection attack detection method based on a gating loop unit and an attention mechanism, and the method is characterized in that the method comprises the following steps: step 1, extracting a statement related to a database in the Internet as a sample; preprocessing each sample, and obtaining a category label of each sample; step 2, inputting a result after sample preprocessing into an injection attack detection model, and predicting a category label corresponding to the result; wherein the injection attack detection model comprises an activation layer, a gating cycle unit layer, an attention mechanism layer and an output layer which are connected in sequence; step 3, training an injection attack detection model based on the sample data; and step 4, for a to-be-detected statement, preprocessing the to-be-detected statement, and inputting a preprocessing result into the trained injection attack detection model to obtain a category label of the to-be-detectedstatement, thereby judging whether the to-be-detected statement is an injection attack statement or not. The method is high in accuracy.

Description

Background technique [0001] An injection attack is when an attacker injects code into a program or query or injects malware into a computer in order to execute remote commands that can read or modify a database, or change data on a website. Common injection attacks in cyberspace include SQL injection attacks, XSS attacks, etc. According to the 2016 cyberspace security data report, two common injection attacks, SQL injection attack and XSS attack, have become common methods of network attack incidents, causing serious social and economic impacts. [0002] Common defenses against injection attacks include precompiled instructions and dynamic analysis. The detection method of injection attack has changed greatly from the rule-based method in the past to the machine learning-based method today. Representative rule-based methods mainly include methods based on regular expression matching, methods using pre-compiled methods, and methods using proxy server buffering. Although these...

AI Technical Summary

Problems solved by technology

Both the tree-kernel and word-graph methods combine machine learning, and to a certain extent, consider the contextual relationship between words, but the relationship between words that are far apart cannot be measured, so there is a problem for shorter attack sentences The recognition is accurate, but the recognition effect is not good for long attack sentences

Images