Predicting Advanced Persistent Threats for IoT Systems Based on Federated Learning-Reference-Cited by-同舟云学术

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Predicting Advanced Persistent Threats for IoT Systems Based on Federated Learning

Published:2021 Issue: Volume: Page:76-89
ISSN:0302-9743
Container-title:Security, Privacy, and Anonymity in Computation, Communication, and Storage
language:
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Author:

Li Zitong,Cheng Xiang,Zhang Jiale,Chen Bing

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-68851-6_5

Reference17 articles.

1. Palani, K., Holt, E., Smith, S.: Invisible and forgotten: zero-day blooms in the IoT. In: IEEE International Conference on Pervasive Computing and Communication Workshops, pp. 1–6 (2016)

2. Husák, M., Komárková, J., Bou-Harb, E., Čeleda, P.: Survey of attack projection, prediction, and forecasting in cyber security. IEEE Commun. Surv. Tutor. 21, 640–660 (2019)

3. Communications in Computer and Information Science;N Polatidis,2017

4. Okutan, A., Yang S.J., McConky, K.: Predicting cyber attacks with Bayesian networks using unconventional signals. In: Conference on Cyber & Information Security Research, pp. 1–13 (2017)

5. Huang, K., Zhou, C., Tian, Y.C., Qin, Y.: Assessing the physical impact of cyberattacks on industrial cyber-physical systems. IEEE Trans. Industr. Electron. 65(10), 8153–8162 (2018)

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1. DEML: Data-Enhanced Meta-Learning Method for IoT APT Traffic Detection;Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering;2024

2. Survey on APT Attack Detection in Industrial Cyber-Physical System;2021 International Conference on Electronic Information Technology and Smart Agriculture (ICEITSA);2021-12

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