3D-IDS: Doubly Disentangled Dynamic Intrusion Detection-Reference-Cited by-同舟云学术

3D-IDS: Doubly Disentangled Dynamic Intrusion Detection

Published:2023-08-04 Issue: Volume: Page:
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Container-title:Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Qiu Chenyang¹^ORCID,Geng Yingsheng¹^ORCID,Lu Junrui¹^ORCID,Chen Kaida¹^ORCID,Zhu Shitong¹^ORCID,Su Ya²^ORCID,Nan Guoshun¹^ORCID,Zhang Can¹^ORCID,Fu Junsong¹^ORCID,Cui Qimei¹^ORCID,Tao Xiaofeng¹^ORCID

Affiliation:

1. Beijing University of Posts and Telecommunications, Beijing, China

2. HUAWEI Technologies Co., Ltd., Beijing, China

Funder

National Key R&D Program of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3580305.3599238

Reference80 articles.

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3. TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems

4. Yoshua Bengio , Aaron Courville , and Pascal Vincent . 2013. Representation learning: A review and new perspectives . IEEE transactions on pattern analysis and machine intelligence 35, 8 ( 2013 ), 1798--1828. Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence 35, 8 (2013), 1798--1828.

5. MStream: Fast Anomaly Detection in Multi-Aspect Streams

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