A Novel Hybrid Deep Learning Approach to Code Generation Aimed at Mitigating the Real-Time Network Attack in the Mobile Experiment Via GRU-LM and Word2vec
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Cheon, Minjong | - |
dc.contributor.author | Ha, Hyodong | - |
dc.contributor.author | Lee, Ook | - |
dc.contributor.author | Mun, Changbae | - |
dc.date.accessioned | 2022-12-20T06:24:34Z | - |
dc.date.available | 2022-12-20T06:24:34Z | - |
dc.date.created | 2022-11-02 | - |
dc.date.issued | 2022-09 | - |
dc.identifier.issn | 1574-017X | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/173082 | - |
dc.description.abstract | As the use of devices in mobile environments increases, network attacks such as DDoS have a malicious attempt to flood the network's regular traffic to overload the target and surrounding infrastructure. This research proposed machine learning and deep learning approaches to dealing with DDoS attacks, and the results are described as follows. First, this research successfully detected DDoS attacks through an LGBM with a 100% accuracy score. Second, the proposed model (GRU-LM), which consists of a trained Word2vec layer with the Python dataset, is far more effective than the standard GRU model. Since Python is quite similar to English, language model-based GRU yields superior results. Various preprocessing steps were performed through the NLTK package, and each number was assigned to the tokenized one for constructing the GRU language model. The result reveals that the proposed model achieved an accuracy score of 87% for predicting the following words in the source code, while the rest achieved below 30% accuracy. This conclusion is significant because its relatively simple and light structure overcomes tradeoff problems between time and accuracy and is adaptable to the mobile setting. Discovering traffic patterns for the underlying data of DDOS assaults and retrieving them using statistical data analysis is the value of this research. Furthermore, since public cloud application vulnerability assaults are rising due to expanding cloud infrastructure, this finding could be used in such attacks. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | HINDAWI LTD | - |
dc.title | A Novel Hybrid Deep Learning Approach to Code Generation Aimed at Mitigating the Real-Time Network Attack in the Mobile Experiment Via GRU-LM and Word2vec | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Ook | - |
dc.identifier.doi | 10.1155/2022/3999868 | - |
dc.identifier.scopusid | 2-s2.0-85139733736 | - |
dc.identifier.wosid | 000868900200004 | - |
dc.identifier.bibliographicCitation | MOBILE INFORMATION SYSTEMS, v.2022, pp.1 - 11 | - |
dc.relation.isPartOf | MOBILE INFORMATION SYSTEMS | - |
dc.citation.title | MOBILE INFORMATION SYSTEMS | - |
dc.citation.volume | 2022 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 11 | - |
dc.type.rims | ART | - |
dc.type.docType | Review | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | Codegeneration | - |
dc.subject.keywordPlus | DDoS Attack | - |
dc.subject.keywordPlus | English languages | - |
dc.subject.keywordPlus | Language model | - |
dc.subject.keywordPlus | Learning approach | - |
dc.subject.keywordPlus | Machine-learning | - |
dc.subject.keywordPlus | Mobile environments | - |
dc.subject.keywordPlus | Model-based OPC | - |
dc.subject.keywordPlus | Network attack | - |
dc.subject.keywordPlus | Real time network | - |
dc.subject.keywordPlus | Codes (symbols) | - |
dc.subject.keywordPlus | Computational linguistics | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordPlus | Denial-of-service attack | - |
dc.subject.keywordPlus | High level languages | - |
dc.subject.keywordPlus | Learning systems | - |
dc.subject.keywordPlus | Malware | - |
dc.subject.keywordPlus | Natural language processing systems | - |
dc.subject.keywordPlus | Network coding | - |
dc.subject.keywordPlus | Network security | - |
dc.subject.keywordPlus | RNA | - |
dc.subject.keywordPlus | Python | - |
dc.identifier.url | https://www.hindawi.com/journals/misy/2022/3999868/ | - |
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