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: Chapter in book/report/conference proceedingConference proceedingpeer-review

2 Citations (Scopus)

Abstract

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
Subtitle of host publication10th EAI International Conference, CloudComp 2020, Qufu, China, December 11-12, 2020, Proceedings
EditorsLianyong Qi, Mohammad R. Khosravi, Xiaolong Xu, Yiwen Zhang, Varun G. Menon
PublisherSpringer Cham
Pages57-73
Number of pages17
Edition1st
ISBN (Electronic)9783030699925
ISBN (Print)9783030699918
DOIs
Publication statusPublished - 13 Feb 2021
Event10th EAI International Conference on Cloud Computing, CloudComp 2020 - Virtual, Online, Qufu, China
Duration: 11 Dec 202012 Dec 2020
https://link.springer.com/book/10.1007/978-3-030-69992-5

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume363
ISSN (Print)1867-8211
ISSN (Electronic)1867-822X
NameCloudComp: International Conference on Cloud Computing

Conference

Conference10th EAI International Conference on Cloud Computing, CloudComp 2020
Country/TerritoryChina
CityQufu
Period11/12/2012/12/20
Internet address

Scopus Subject Areas

  • Computer Networks and Communications

User-Defined Keywords

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

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