- All Journals
- Al-Qadisiyah University
- Basrah University
- Kirkuk University
- Mosul University
- The Iraqi Borad for Medical Specialization
- University of Anbar
- University of Technology
- Al Mustafa University
- Al-Zahraa University for Women
- Alnoor University
- Kunooz University
- Misan University
- Ninevah University
- Tikrit University
- University of Al-Hamdaniya
- University of Kerbala
Minimising Security Deviations in Software-Defined Networks Using Deep Learning | ||
| AL-Rafidain Journal of Computer Sciences and Mathematics | ||
| Article 11, Volume 19, Issue 1, June 2025, Pages 96-104 PDF (594.52 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.33899/csmj.2025.156885.1166 | ||
| Author | ||
| mohammed sami alghaloom* | ||
| وزارة التربية | ||
| Abstract | ||
| This study aims to enhance the security of Software-Defined Networks (SDN) by employing deep learning techniques to detect cyber threats and mitigate attacks. A comprehensive data analysis was conducted, beginning with feature identification and dimensionality reduction using the Gain Information method to filter out redundant features, thereby improving model performance. Additionally, Min-Max normalization was applied to standardize feature ranges, and the SMOTE technique was utilized to balance the dataset and reduce the impact of underrepresented classes. The research compares the performance of three primary deep learning models—CNN, LSTM, and ANN—with a newly proposed method designed to better differentiate between similar attack categories. The results demonstrate that deep learning models can effectively uncover hidden patterns in network traffic and accurately classify security threats, with the LSTM model particularly excelling in capturing temporal dependencies. While CNN and ANN models showed high accuracy in certain scenarios, they struggled to identify classes with fewer samples, necessitating the use of additional balancing techniques. Conversely, the proposed method showed promise in achieving a balance between accuracy and efficiency, suggesting that further refinement in feature engineering and advanced balancing strategies could enhance its performance. Overall, this study underscores the critical role of integrating deep learning with advanced preprocessing techniques in developing more reliable and effective intrusion detection systems for SDN environments. | ||
| Keywords | ||
| Convolutional Neural Networks; Long Short-Term Memory; Artificial Neural Networks; Software Defined Networks; Cybersecurity | ||
|
Statistics Article View: 122 PDF Download: 49 |
||