Two-Phase Deep Learning-Based EDoS Detection System
| Autor: | Minho Park, Chien-Nguyen Nhu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Scheme (programming language)
Technology Exploit Computer science QH301-705.5 Distributed computing QC1-999 Cloud computing Resource (project management) General Materials Science Biology (General) Instrumentation QD1-999 computer.programming_language Fluid Flow and Transfer Processes Sequence economic denial of sustainability Artificial neural network business.industry Process Chemistry and Technology Deep learning Physics cloud computing General Engineering deep learning Engineering (General). Civil engineering (General) Computer Science Applications Chemistry The Internet Artificial intelligence TA1-2040 business long short-term memory computer artificial neural network |
| Zdroj: | Applied Sciences, Vol 11, Iss 10249, p 10249 (2021) Applied Sciences Volume 11 Issue 21 |
| ISSN: | 2076-3417 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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