提出一种利用机器学习技术检测入侵网络攻击的模型

Teba Ali Jasem Ali, M. Jawhar
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引用次数: 0

摘要

当前,人们对计算机的依赖在生活的各个方面都在增加,因此有必要保护计算机网络和计算资源免受复杂的网络攻击。这是通过构建工具、应用程序和系统来实现的,这些工具、应用程序和系统可以检测攻击或异常,以适应不断变化的体系结构和动态变化的威胁。本文的目标是建立一个基于卷积神经网络(CNN)等深度学习技术的网络入侵检测系统(NIDS),并证明了其在预测、分类和提取网络流量高级特征方面的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Proposing a Model for Detecting Intrusion Network Attacks Using Machine Learning Techniques
: At the present time, the reliance on computers is increasing in all aspects of life, so it is necessary to protect computer networks and computing resources from complex attacks against the network. This is performed by building tools, applications, and systems that detect attacks or anomalies adapting to ever-changing architectures and dynamically changing threats. The goal of this paper is to build a Network Intrusion Detection System (NIDS) based on deep learning techniques such as Convolutional Neural Network (CNN), which demonstrated its efficiency in predicting, classifying, and extracting high-level features in network traffic.
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