基于复杂深度神经网络的网络入侵检测系统设计

M. Al-Shabi
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引用次数: 8

摘要

近年来,各个科学和工业领域都有了巨大的发展。因此,各种类型的易受入侵的网络被广泛引入。鉴于此,许多研究都致力于检测各种类型的入侵并保护网络免受这些渗透。本文设计了一种新的网络入侵检测系统,利用复杂的深度神经网络检测网络攻击。开发的系统通过pycharm程序在标准数据集KDDCUP99上进行了训练和测试。与现有的类似深度神经网络和传统机器学习算法的入侵检测方法相比,本文提出的检测系统在检测精度上取得了更好的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design of a Network Intrusion Detection System Using Complex Deep Neuronal Networks
Recent years have witnessed a tremendous development in various scientific and industrial fields. As a result, different types of networks are widely introduced which are vulnerable to intrusion. In view of the same, numerous studies have been devoted to detecting all types of intrusion and protect the networks from these penetrations. In this paper, a novel network intrusion detection system has been designed to detect cyber-attacks using complex deep neuronal networks. The developed system is trained and tested on the standard dataset KDDCUP99 via pycharm program. Relevant to existing intrusion detection methods with similar deep neuronal networks and traditional machine learning algorithms, the proposed detection system achieves better results in terms of detection accuracy.
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