@inproceedings{39a14c3f66564c5a8c89f29e98f0f719,
title = "Exploring Self-attention Mechanism of Deep Learning in Cloud Intrusion Detection",
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.",
keywords = "Convolution neural network, Deep learning, Long short-term memory, Network intrusion detection, Self-attention",
author = "Chenmao Lu and Dai, {Hong Ning} and Junhao Zhou and Hao Wang",
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: {\textcopyright} 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.; 10th EAI International Conference on Cloud Computing, CloudComp 2020 ; Conference date: 11-12-2020 Through 12-12-2020",
year = "2021",
month = feb,
day = "13",
doi = "10.1007/978-3-030-69992-5_5",
language = "English",
isbn = "9783030699918",
series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",
publisher = "Springer Cham",
pages = "57--73",
editor = "Lianyong Qi and Khosravi, {Mohammad R.} and Xiaolong Xu and Yiwen Zhang and Menon, {Varun G.}",
booktitle = "Cloud Computing",
edition = "1st",
url = "https://link.springer.com/book/10.1007/978-3-030-69992-5",
}