Evaluating Unsupervised Deep Learning Models for Network Intrusion Detection Using Real Security Event Data
Evaluating Unsupervised Deep Learning Models for Network Intrusion Detection Using Real Security Event Data

한국인터넷방송통신학회
International journal of advanced smart convergence
Vol.11No.4
2022.01

10 - 19 (10 pages)

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외부링크

AI-based Network Intrusion Detection Systems (AI-NIDS) detect network attacks using machine learning and deep learning models. Recently, unsupervised AI-NIDS methods are getting more attention since there is no need for labeling, which is crucial for building practical NIDS systems. This paper aims to test the impact of designing autoencoder models that can be applied to unsupervised an AI-NIDS in real network systems. We collected security events of legacy network security system and carried out an experiment. We report the results and discuss the findings.

Evaluating Unsupervised Deep Learning Models for Network Intrusion Detection Using Real Security Event Data
Evaluating Unsupervised Deep Learning Models for Network Intrusion Detection Using Real Security Event Data

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Evaluating Unsupervised Deep Learning Models for Network Intrusion Detection Using Real Security Event Data Evaluating Unsupervised Deep Learning Models for Network Intrusion Detection Using Real Security Event Data

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Evaluating Unsupervised Deep Learning Models for Network Intrusion Detection Using Real Security Event Data
Evaluating Unsupervised Deep Learning Models for Network Intrusion Detection Using Real Security Event Data