Two-Phase Deep Learning-Based EDoS Detection System
Cloud computing is currently considered the most cost-effective platform for offering business and consumer IT services over the Internet. However, it is prone to new vulnerabilities. A new type of attack called an economic denial of sustainability (EDoS) attack exploits the pay-per-use model to sca...
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MDPI AG
2021
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oai:doaj.org-article:b89f7abe58384990b144e5e6f41892592021-11-11T15:17:21ZTwo-Phase Deep Learning-Based EDoS Detection System10.3390/app1121102492076-3417https://doaj.org/article/b89f7abe58384990b144e5e6f41892592021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10249https://doaj.org/toc/2076-3417Cloud computing is currently considered the most cost-effective platform for offering business and consumer IT services over the Internet. However, it is prone to new vulnerabilities. A new type of attack called an economic denial of sustainability (EDoS) attack exploits the pay-per-use model to scale up the resource usage over time to the extent that the cloud user has to pay for the unexpected usage charge. To prevent EDoS attacks, a few solutions have been proposed, including hard-threshold and machine learning-based solutions. Among them, long short-term memory (LSTM)-based solutions achieve much higher accuracy and false-alarm rates than hard-threshold and other machine learning-based solutions. However, LSTM requires a long sequence length of the input data, leading to a degraded performance owing to increases in the calculations, the detection time, and consuming a large number of computing resources of the defense system. We, therefore, propose a two-phase deep learning-based EDoS detection scheme that uses an LSTM model to detect each abnormal flow in network traffic; however, the LSTM model requires only a short sequence length of five of the input data. Thus, the proposed scheme can take advantage of the efficiency of the LSTM algorithm in detecting each abnormal flow in network traffic, while reducing the required sequence length of the input data. A comprehensive performance evaluation shows that our proposed scheme outperforms the existing solutions in terms of accuracy and resource consumption.Chien-Nguyen NhuMinho ParkMDPI AGarticleeconomic denial of sustainabilitydeep learningcloud computinglong short-term memoryartificial neural networkTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10249, p 10249 (2021) |
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DOAJ |
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DOAJ |
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EN |
| topic |
economic denial of sustainability deep learning cloud computing long short-term memory artificial neural network Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 |
| spellingShingle |
economic denial of sustainability deep learning cloud computing long short-term memory artificial neural network Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Chien-Nguyen Nhu Minho Park Two-Phase Deep Learning-Based EDoS Detection System |
| description |
Cloud computing is currently considered the most cost-effective platform for offering business and consumer IT services over the Internet. However, it is prone to new vulnerabilities. A new type of attack called an economic denial of sustainability (EDoS) attack exploits the pay-per-use model to scale up the resource usage over time to the extent that the cloud user has to pay for the unexpected usage charge. To prevent EDoS attacks, a few solutions have been proposed, including hard-threshold and machine learning-based solutions. Among them, long short-term memory (LSTM)-based solutions achieve much higher accuracy and false-alarm rates than hard-threshold and other machine learning-based solutions. However, LSTM requires a long sequence length of the input data, leading to a degraded performance owing to increases in the calculations, the detection time, and consuming a large number of computing resources of the defense system. We, therefore, propose a two-phase deep learning-based EDoS detection scheme that uses an LSTM model to detect each abnormal flow in network traffic; however, the LSTM model requires only a short sequence length of five of the input data. Thus, the proposed scheme can take advantage of the efficiency of the LSTM algorithm in detecting each abnormal flow in network traffic, while reducing the required sequence length of the input data. A comprehensive performance evaluation shows that our proposed scheme outperforms the existing solutions in terms of accuracy and resource consumption. |
| format |
article |
| author |
Chien-Nguyen Nhu Minho Park |
| author_facet |
Chien-Nguyen Nhu Minho Park |
| author_sort |
Chien-Nguyen Nhu |
| title |
Two-Phase Deep Learning-Based EDoS Detection System |
| title_short |
Two-Phase Deep Learning-Based EDoS Detection System |
| title_full |
Two-Phase Deep Learning-Based EDoS Detection System |
| title_fullStr |
Two-Phase Deep Learning-Based EDoS Detection System |
| title_full_unstemmed |
Two-Phase Deep Learning-Based EDoS Detection System |
| title_sort |
two-phase deep learning-based edos detection system |
| publisher |
MDPI AG |
| publishDate |
2021 |
| url |
https://doaj.org/article/b89f7abe58384990b144e5e6f4189259 |
| work_keys_str_mv |
AT chiennguyennhu twophasedeeplearningbasededosdetectionsystem AT minhopark twophasedeeplearningbasededosdetectionsystem |
| _version_ |
1718435712560267264 |