Abstract
Although cloud computing is considered the most widespread technology nowadays, it still suffers from many challenges, especially related to its security. Due to the open and distributed nature of the cloud environment, this makes the cloud itself vulnerable to various attacks. In this paper, the design of a novel integrated Cloud-based Intrusion Detection System (CIDS) is proposed to immunise the cloud against any possible attacks. The proposed CIDS consists of five main modules to do the following actions: monitoring the network, capturing the traffic flows, extracting features, analyzing the flows, detecting intrusions, taking a reaction, and logging all activities. Furthermore an enhanced bagging ensemble system of three deep learning models is utilized to predict intrusions effectively. Moreover, a third-party Cloud-based Intrusion Detection System Service (CIDSS) is also exploited to control the proposed CIDS and provide the reporting service. Finally, it has been shown that the proposed approach overcomes all problems associated with attacks on the cloud raised in the literature.
Original language | English |
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Pages (from-to) | 365-379 |
Number of pages | 15 |
Journal | Open Computer Science |
Volume | 11 |
Issue number | 1 |
DOIs | |
Publication status | Published - 1 Jan 2021 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2021 Wisam Elmasry et al., published by De Gruyter 2021.
Keywords
- Cloud Security
- Deep Learning
- Ensemble System
- Intrusion Detection
- Particle Swarm Optimization