dc.contributor.author | Çavuşoğlu, Ünal | |
dc.contributor.author | Akgun, Devrim | |
dc.contributor.author | Hizal, Selman | |
dc.date.accessioned | 2023-11-24T06:49:04Z | |
dc.date.available | 2023-11-24T06:49:04Z | |
dc.date.issued | 2024 | en_US |
dc.identifier.citation | Ünal Çavuşoğlu, Devrim Akgün, & Selman Hızal. (2023). A Novel Cyber Security Model Using Deep Transfer Learning. Arabian Journal for Science and Engineering. https://doi.org/10.1007/s13369-023-08092-1
| en_US |
dc.identifier.uri | https://hdl.handle.net/20.500.14002/2157 | |
dc.description.abstract | Preventing attackers from interrupting or totally stopping critical services in cloud systems is a vital and challenging task. Today, machine learning-based algorithms and models are widely used, especially for the intelligent detection of zero-day attacks. Recently, deep learning methods that provide automatic feature extraction are designed to detect attacks automatically. In this study, we constructed a new deep learning model based on transfer learning for detecting and protecting cloud systems from malicious attacks. The developed deep transfer learning-based IDS converts network traffic into 2D preprocessed feature maps.Then the feature maps are processed with the transferred and fine-tuned convolutional layers of the deep learning model before the dense layer for detection and classification of traffic data. The results computed using the NSL-KDD test dataset reveal that the developed models achieve 89.74% multiclass and 92.58% binary classification accuracy. We performed another evaluation using only 20% of the training dataset as test data, and 80% for training. In this case, the model achieved 99.83% and 99.85% multiclass and binary classification accuracy, respectively. © 2023, King Fahd University of Petroleum & Minerals. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Institute for Ionics | en_US |
dc.relation.ispartof | Arabian Journal for Science and Engineering | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Deep learning; Intrusion detection system; Network security; Transfer learning; VGG16 | en_US |
dc.title | A Novel Cyber Security Model Using Deep Transfer Learning | en_US |
dc.type | article | en_US |
dc.authorid | 0000-0001-6345-0066 | en_US |
dc.department | Fakülteler, Teknoloji Fakültesi, Bilgisayar Mühendisliği Bölümü | en_US |
dc.institutionauthor | Hizal, Selman | |
dc.identifier.doi | 10.1007/s13369-023-08092-1 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 57206725968 | en_US |
dc.identifier.wosquality | Q2 | en_US |
dc.identifier.scopus | 2-s2.0-85165564665 | en_US |