A Novel Cyber Security Model Using Deep Transfer Learning

• Published in: Arabian journal for science and engineering (2011) 2024-03, Vol.49 (3), p.3623-3632
• Main Authors: Çavuşoğlu, Ünal, Akgun, Devrim, Hizal, Selman
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Classification; Cloud computing; Communications traffic; Cybersecurity; Datasets; Deep learning; Engineering; Feature extraction; Feature maps; Humanities and Social Sciences; Machine learning; multidisciplinary; Research Article-Computer Engineering and Computer Science; Science
• ISSN: 2193-567X; 1319-8025; 2191-4281
• DOI: 10.1007/s13369-023-08092-1

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.