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EXPLOIT KIT DETECTION SYSTEM BASED ON THE NEURAL NETWORK USING IMAGE

机译:基于图像的神经网络开发套件检测系统

摘要

The present invention relates to an exploit kit detection system based on a neural network using an image and provides a configuration including: a file collection module for collecting a web file created in a web document code and a script code; a distribution module for distributing and storing the collected web file; a management module for assigning, when the web file is received, a job ID to the web file and registering the web file in an inspection target list; an image conversion module for converting a corresponding web file into grayscale, targeting the web file registered in the inspection target list; a classification model, as a classification model based on the neural network, for receiving an image of grayscale and classifying existence and a type of the exploit kit (EK); and a result processing module for receiving a classification result, creating a result data, and transmitting the result data to the distribution module.;According to the system as described above, as maliciousness of an image is determined by analyzing the image, an environment of detecting an exploit kit through only one conversion process is provided, and thus a fast performance can be demonstrated, and the system may be used for easy filtering of a malicious file from large-scale web page files.
机译:本发明涉及基于使用图像的神经网络的攻击工具包检测系统,并提供一种配置,包括:文件收集模块,用于收集以网络文档代码和脚本代码创建的网络文件。分发模块,用于分发和存储收集到的网络文件;管理模块,用于在接收到网络文件时,将作业ID分配给该网络文件,并将该网络文件注册到检查目标列表中;图像转换模块,用于将相应的Web文件转换为灰度图像,以检查目标列表中注册的Web文件为目标;分类模型,作为基于神经网络的分类模型,用于接收灰度图像并对存在的类别和漏洞利用工具包(EK)的类型进行分类;根据上述系统,当通过分析图像来确定图像的恶意性时,根据本发明的系统,提供了仅通过一个转换过程就可以检测漏洞利用工具包的方法,因此可以证明具有快速的性能,并且该系统可以用于轻松地从大规模网页文件中过滤恶意文件。

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