Mehmood Sajid, Amin Rashid, Mustafa Jamal, Hussain Mudassar, Alsubaei Faisal S, Zakaria Muhammad D
Department of Computer Science and IT, University of Chakwal, Chakwal, Pakistan.
Department of Computer Science, University of Chakwal, Chakwal, Pakistan.
PLoS One. 2025 Jan 27;20(1):e0312425. doi: 10.1371/journal.pone.0312425. eCollection 2025.
Software-Defined Networks (SDN) provides more control and network operation over a network infrastructure as an emerging and revolutionary paradigm in networking. Operating the many network applications and preserving the network services and functions, the SDN controller is regarded as the operating system of the SDN-based network architecture. The SDN has several security problems because of its intricate design, even with all its amazing features. Denial-of-service (DoS) attacks continuously impact users and Internet service providers (ISPs). Because of its centralized design, distributed denial of service (DDoS) attacks on SDN are frequent and may have a widespread effect on the network, particularly at the control layer. We propose to implement both MLP (Multilayer Perceptron) and CNN (Convolutional Neural Networks) based on conventional methods to detect the Denial of Services (DDoS) attack. These models have got a complex optimizer installed on them to decrease the false positive or DDoS case detection efficiency. We use the SHAP feature selection technique to improve the detection procedure. By assisting in the identification of which features are most essential to spot the incidents, the approach aids in the process of enhancing precision and flammability. Fine-tuning the hyperparameters with the help of Bayesian optimization to obtain the best model performance is another important thing that we do in our model. Two datasets, InSDN and CICDDoS-2019, are utilized to assess the effectiveness of the proposed method, 99.95% for the true positive (TP) of the CICDDoS-2019 dataset and 99.98% for the InSDN dataset, the results show that the model is highly accurate.
软件定义网络（SDN）作为网络领域一种新兴的革命性范式，能对网络基础设施提供更多的控制和网络操作。SDN控制器负责运行众多网络应用并维护网络服务及功能，被视为基于SDN的网络架构的操作系统。尽管SDN具有诸多惊人特性，但其设计复杂，存在若干安全问题。拒绝服务（DoS）攻击持续影响用户和互联网服务提供商（ISP）。由于其集中式设计，针对SDN的分布式拒绝服务（DDoS）攻击频发，可能对网络产生广泛影响，尤其是在控制层。我们提议在传统方法的基础上实现基于多层感知器（MLP）和卷积神经网络（CNN）的模型来检测拒绝服务（DDoS）攻击。这些模型安装了复杂的优化器以降低误报或DDoS案例检测效率。我们使用SHAP特征选择技术来改进检测过程。通过协助识别哪些特征对于发现事件最为关键，该方法有助于提高精度和检测效率。在贝叶斯优化的帮助下微调超参数以获得最佳模型性能是我们在模型中所做的另一项重要工作。使用两个数据集InSDN和CICDDoS - 2019来评估所提方法的有效性，CICDDoS - 2019数据集的真阳性（TP）率为99.95%，InSDN数据集的真阳性率为99.98%，结果表明该模型具有很高的准确性。
