Zeekflow+: A Deep LSTM Autoencoder with Integrated Random Forest Classifier for Binary and Multi-class Classification in Network Traffic Data-Reference-Cited by-同舟云学术

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Zeekflow+: A Deep LSTM Autoencoder with Integrated Random Forest Classifier for Binary and Multi-class Classification in Network Traffic Data

Published:2024-06-26 Issue: Volume: Page:613-618
ISSN:
Container-title:Proceedings of the 17th International Conference on PErvasive Technologies Related to Assistive Environments
language:
Short-container-title:

Author:

Arapidis Emmanouil¹^ORCID,Temenos Nikos²^ORCID,Giagkos Dimitris¹^ORCID,Rallis Ioannis²^ORCID,Kalogeras Dimitris²^ORCID,Papadakis Nikolaos¹^ORCID,Litke Antonis¹^ORCID,C. Messinis Sotirios²^ORCID

Affiliation:

1. INFILI Technologies SA, Greece

2. Institute of Communication and Computer Systems, Greece

Funder

HORIZON EUROPE Framework Programme

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3652037.3663908

Reference22 articles.

1. The Internet of Things: A survey

2. Anomaly detection

3. Giagkos Dimitris, Kompougias Orestis, Litke Antonis, and Papadakis Nikolaos. 2023. ZeekFlow: Deep Learning-based network intrusion detection A multimodal approach. In Workshop on Attacks and Software Protection (WASP). 1–16.

4. Unsupervised network traffic anomaly detection with deep autoencoders

5. One-Class LSTM Network for Anomalous Network Traffic Detection

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