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فهرست نشریات دارای اعتبار وزارت علوم، تحقیقات و فناوری
پایگاه استنادی علوم جهان اسلام (ISC)

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Delavari, Khashayar, Shetabi, Mehran, Sadrossadat, Sayed Alireza. (1404). Enhancing DDoS Attack Detection in Software Defined Networks with Deep Reinforcement Learning. سامانه مدیریت نشریات علمی, (), -. doi: 10.22034/cmde.2025.65880.3056
Khashayar Delavari; Mehran Shetabi; Sayed Alireza Sadrossadat. "Enhancing DDoS Attack Detection in Software Defined Networks with Deep Reinforcement Learning". سامانه مدیریت نشریات علمی, , , 1404, -. doi: 10.22034/cmde.2025.65880.3056
Delavari, Khashayar, Shetabi, Mehran, Sadrossadat, Sayed Alireza. (1404). 'Enhancing DDoS Attack Detection in Software Defined Networks with Deep Reinforcement Learning', سامانه مدیریت نشریات علمی, (), pp. -. doi: 10.22034/cmde.2025.65880.3056
Delavari, Khashayar, Shetabi, Mehran, Sadrossadat, Sayed Alireza. Enhancing DDoS Attack Detection in Software Defined Networks with Deep Reinforcement Learning. سامانه مدیریت نشریات علمی, 1404; (): -. doi: 10.22034/cmde.2025.65880.3056

Enhancing DDoS Attack Detection in Software Defined Networks with Deep Reinforcement Learning

Computational Methods for Differential Equations
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 10 دی 1404    اصل مقاله (998.34 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22034/cmde.2025.65880.3056
نویسندگان
Khashayar Delavari؛ Mehran Shetabi؛ Sayed Alireza Sadrossadat*
Department of Computer Engineering, Yazd University, Yazd 8915818411, Iran.
چکیده
The rapid growth of Software Defined Networking (SDN) offers significant benefits in network flexibility, management,
and scalability. However, the centralization of control in SDN poses substantial security risks, especially from
Distributed Denial of Service (DDoS) attacks. Traditional detection mechanisms often fall short due to the evolving
nature of these threats. This paper introduces a novel Deep Reinforcement Learning (DRL) technique to enhance
DDoS attack detection and mitigation in SDN environments. By leveraging DRL’s adaptive learning capabilities,
the proposed model continuously learns and adapts to new attack patterns, providing robust defense. The model
employs a combination of Autoencoder (AE) and Bidirectional Gated Recurrent Unit (BGRU) to analyze traffic
patterns and detect anomalies effectively. Experimental results, using a comprehensive dataset from real network
traffic, demonstrate the model’s superior accuracy, higher detection rate, and reduced false-positive rates compared
to existing methods. Additionally, the proposed technique incorporates a trust value mechanism to mitigate detected
attacks, ensuring enhanced security and reliability for SDN networks.
کلیدواژه‌ها
DDOS attacks؛ Deep learning؛ Deep reinforcement learning؛ machine learning؛ Software-Defined Networks
آمار
تعداد مشاهده مقاله: 157
تعداد دریافت فایل اصل مقاله: 131


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