@inproceedings{0ec73d76927247378d453181da393871,
title = "Constructing and Generating a Time-varying State- Space Vector against Cloud Service Event Interactions",
abstract = "the abnormal interaction between cloud service events is an essential factor leading to failure. Using neural networks explores the interaction between cloud service events, which needs a method to transform the cloud service events into a vector. However, cloud service events exist as a log with time- varying characteristics. The dataset of cloud service is vast, including numerous strings that cannot be directly involved in calculations. There is a lack of pathway transform data semantics to interaction semantics. Thus, this paper provides a method to construct cloud service logs into vector. Regarding cloud service events as natural language, the method characterizes cloud service events as vector without building a corpus. In addition, we use the semi-supervised neural networks to predict the interaction between cloud service events. The results show that the effectiveness of our method, indicating that it meets the needs of project. This work also provides a foundation for security traceability.",
keywords = "Backus-Naur Form, cloud service event, semi-supervised neural networks, space vector, Time-varying state",
author = "Yiyi Jiang and Xiaojian Li and Chunhe Xia and Hangping Hu and Zhen Zhang and Hailan Wang",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE 4th International Conference on Big Data and Artificial Intelligence, BDAI 2021 ; Conference date: 02-07-2021 Through 04-07-2021",
year = "2021",
month = jul,
day = "2",
doi = "10.1109/BDAI52447.2021.9515231",
language = "英语",
series = "2021 IEEE 4th International Conference on Big Data and Artificial Intelligence, BDAI 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "134--139",
booktitle = "2021 IEEE 4th International Conference on Big Data and Artificial Intelligence, BDAI 2021",
address = "美国",
}