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Forecasting number of vulnerabilities using long short-term neural memory network

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dc.contributor.authorHoque M.S.en_US
dc.contributor.authorJamil N.en_US
dc.contributor.authorAmin N.en_US
dc.contributor.authorRahim A.A.A.en_US
dc.contributor.authorJidin R.B.en_US
dc.contributor.authorid57220806665en_US
dc.contributor.authorid36682671900en_US
dc.contributor.authorid7102424614en_US
dc.contributor.authorid57224225526en_US
dc.contributor.authorid6508169028en_US
dc.date.accessioned2023-05-29T09:05:54Z
dc.date.available2023-05-29T09:05:54Z
dc.date.issued2021
dc.description.abstractCyber-attacks are launched through the exploitation of some existing vulnerabilities in the software, hardware, system and/or network. Machine learning algorithms can be used to forecast the number of post release vulnerabilities. Traditional neural networks work like a black box approach; hence it is unclear how reasoning is used in utilizing past data points in inferring the subsequent data points. However, the long short-term memory network (LSTM), a variant of the recurrent neural network, is able to address this limitation by introducing a lot of loops in its network to retain and utilize past data points for future calculations. Moving on from the previous finding, we further enhance the results to predict the number of vulnerabilities by developing a time series-based sequential model using a long short-term memory neural network. Specifically, this study developed a supervised machine learning based on the non-linear sequential time series forecasting model with a long short-term memory neural network to predict the number of vulnerabilities for three vendors having the highest number of vulnerabilities published in the national vulnerability database (NVD), namely microsoft, IBM and oracle. Our proposed model outperforms the existing models with a prediction result root mean squared error (RMSE) of as low as 0.072. � 2021 Institute of Advanced Engineering and Science. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.11591/ijece.v11i5.pp4381-4391
dc.identifier.epage4391
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85107269642
dc.identifier.spage4381
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85107269642&doi=10.11591%2fijece.v11i5.pp4381-4391&partnerID=40&md5=723ac43cd37c4b8c277a0e118de6c6a1
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25985
dc.identifier.volume11
dc.publisherInstitute of Advanced Engineering and Scienceen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleInternational Journal of Electrical and Computer Engineering
dc.titleForecasting number of vulnerabilities using long short-term neural memory networken_US
dc.typeArticleen_US
dspace.entity.typePublication
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