A Machine Learning IDS for Known and Unknown Anomalies

被引：0

作者：

Aguilo-Gost, F. ^{[1
]}

Simo-Mezquita, E. ^{[1
]}

Marin-Tordera, E. ^{[1
]}

Hussain, A. ^{[1
]}

机构：

[1] UPC BarcelonaTech, Adv Network Architectures Lab CRAAX, Vilanova I La Geltru 08800, Spain

来源：

2022 18TH INTERNATIONAL CONFERENCE ON THE DESIGN OF RELIABLE COMMUNICATION NETWORKS (DRCN) | 2022年

基金：

欧盟地平线“2020”;

关键词：

Intrusion Detection System; Machine Learning; Cyber Security; One-Class SVM;

D O I：

10.1109/DRCN53993.2022.9758010

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this work an Intrusion Detection System to detect anomalies in networks system entries is presented. It is based on Machine Learning models and is composed of two components. The first component detects known anomalies with an accuracy beyond 95%. This component uses supervised models and several algorithms can be applied. In the use case analysed here, the best algorithm that fits the model is Random Forests. The second component detects unknown anomalies and benign entries and it is based on unsupervised models. In this use case, the unsupervised One-Class Support Vector Machines algorithm has been applied. This component has an accuracy of 80% detecting unknown anomalies.

引用

页数：5

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