机器学习中的高级僵尸网络检测框架

Nisha Rana, Ankur Chaudhary, R. S. Rawat
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引用次数: 1

摘要

在过去的十年里,僵尸网络已经发展成为对数字安全的严重威胁,因为它展示了它能够权衡许多个人电脑并让它们做非法工作的能力。有各种可用的选项可以观察到僵尸网络。由于包含了大量的信息,利用人工智能计算的僵尸网络的地位是巨大的。准备好的分配与收集到的信息相联系,以评估结果。对系统流信息的检查被用作一种识别策略,因为它不依赖于包裹内容,因此可以抵抗最新类型的加密,以及模糊使用攻击者来保护他们的机器人。结果清楚地显示了我们用橙色表示的数据集的目的和方法。数据集显示了机器人的流量,显示了准确性和误报率。此外,几乎所有类型的僵尸网络都可以利用预期模型进行识别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Advance Botnet Detection Framework in Machine Learning
In the last decade, the botnet has developed as an intense danger to digital security by demonstrating its ability to trade-off many PCs and making them do the unlawful work. There are various available option by which botnet can be observed. Because of the inclusion of a gigantic measure of information, the place of botnet utilizing AI calculations is in the enormous pattern. The prepared allotments were connected to the gathered information to assess the outcomes. Examination of system stream information is used as a strategy for identification since it doesn't rely on the parcel content thus giving resistance towards the most recent type of encryption together with obscurity used up assailants to protect their bots. The result is clearly showing the purpose and methods of Datasets which we put in orange. The Datasets showing the traffic of bots showing the accuracy and false-positive rate. Moreover, pretty much every kind of botnet as it may be identified utilizing the expected model.
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