利用神经网络增强防火墙过滤性能

Heba Saleous, Z. Trabelsi
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引用次数: 4

摘要

互联网已经发展到一个地步,世界各地的人们都越来越依赖于它所提供的方便的交流媒介。然而,由于这种依赖性,恶意流量已成为一个主要问题。正因为如此,防火墙是任何网络的必要组成部分,因为它们能够根据规则过滤流量,这些规则规定哪些数据包应该被接受或拒绝。但是,过滤规则必须由网络管理员手工配置,不符合规则的报文可能会被防火墙错误判断。在较大的网络中,这可能会变得乏味。神经网络可以学习管理员设置的过滤规则,以决定不符合任何特定规则的数据包是应该接受还是拒绝。神经网络将使用现有的数据包数据及其防火墙动作进行训练,然后测试以确定其与防火墙相比的过滤精度。
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Enhancing Firewall Filter Performance Using Neural Networks
The Internet has grown to a point where people all over the world have grown dependent of the convenient communication medium that is being provided. However, with this dependency, malicious traffic has become a major concern. Because of this, firewalls are a mandatory part of any network, due to their ability to filter the traffic based on rules that state which packets should be accepted or denied. However, filter rules must be manually configured by a network administrator, and packets that do not fit any rule may be subject to wrong judgment by the firewall. This can become tedious in larger networks. Neural networks can learn the filter rules that have been set by administrators in order to decide if packets that do not fit any specific rules should be accepted or denied. The neural network will be trained with existing packet data and their firewall actions, and then tested to determine its filtering accuracy compared to the firewall.
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