Exploring Self-attention Mechanism of Deep Learning in Cloud Intrusion Detection

Chenmao LU, Hong Ning DAI*, Junhao ZHOU, Hao WANG

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review


Cloud computing offers elastic and ubiquitous computing services, thereby receiving extensive attention recently. However, cloud servers have also become the targets of malicious attacks or hackers due to the centralization of data storage and computing facilities. Most intrusion attacks to cloud servers are often originated from inner or external networks. Intrusion detection is a prerequisite to designing anti-intrusion countermeasures of cloud systems. In this paper, we explore deep learning algorithms to design intrusion detection methods. In particular, we present a deep learning-based method with the integration of conventional neural networks, self-attention mechanism, and Long short-term memory (LSTM), namely CNN-A-LSTM to detect intrusion. CNN-A-LSTM leverages the merits of CNN in processing local correlation data and extracting features, the time feature extracting capability of LSTM, and the self-attention mechanism to better exact features. We conduct extensive experiments on the KDDcup99 dataset to evaluate the performance of our CNN-A-LSTM model. Compared with other machine learning and deep learning models, our CNN-A-LSTM has superior performance.

Original languageEnglish
Title of host publicationCloud Computing - 10th EAI International Conference, CloudComp 2020, Proceedings
EditorsLianyong Qi, Mohammad R. Khosravi, Xiaolong Xu, Yiwen Zhang, Varun G. Menon
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages17
ISBN (Print)9783030699918
Publication statusPublished - Feb 2021
Externally publishedYes
Event10th EAI International Conference on Cloud Computing, CloudComp 2020 - Virtual, Online
Duration: 11 Dec 202012 Dec 2020

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
ISSN (Print)1867-8211
ISSN (Electronic)1867-822X


Conference10th EAI International Conference on Cloud Computing, CloudComp 2020
CityVirtual, Online

Bibliographical note

Funding Information:
Acknowledgement. The work described in this paper was partially supported by Macao Science and Technology Development Fund under Grant No. 0026/2018/A1.

Publisher Copyright:
© 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.


  • Convolution neural network
  • Deep learning
  • Long short-term memory
  • Network intrusion detection
  • Self-attention


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