In the contemporary landscape, Distributed Denial of Service (DDoS) attacks have emerged as an exceedingly pernicious threat, particularly in the context of network management centered around technologies like Software-Defined Networking (SDN). With the increasing intricacy and sophistication of DDoS attacks, the need for effective countermeasures has led to the adoption of Machine Learning (ML) techniques. Nevertheless, despite substantial advancements in this field, challenges persist, adversely affecting the accuracy of ML-based DDoS-detection systems. This article introduces a model designed to detect DDoS attacks. This model leverages a combination of Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN) to enhance the performance of ML-based DDoS-detection systems within SDN environments. We propose utilizing the SHapley Additive exPlanations (SHAP) feature-selection technique and employing a Bayesian optimizer for hyperparameter tuning to optimize our model. To further solidify the relevance of our approach within SDN environments, we evaluate our model by using an open-source SDN dataset known as InSDN. Furthermore, we apply our model to the CICDDoS-2019 dataset. Our experimental results highlight a remarkable overall accuracy of 99.95% with CICDDoS-2019 and an impressive 99.98% accuracy with the InSDN dataset. These outcomes underscore the effectiveness of our proposed DDoS-detection model within SDN environments compared to existing techniques.

Path planning is crucial for safely and efficiently navigating unmanned aerial vehicles (UAVs) toward operational goals. Often, this is a complex, multi-constraint, and non-linear optimization problem, and metaheuristic algorithms are frequently used to solve it. Grey Wolf Optimization (GWO) is one of the most popular algorithms for solving such problems. However, standard GWO has several limitations, such as premature convergence, susceptibility to local minima, and unsuitability for dynamic environments due to its lack of adaptive learning. We propose a Q-learning-based GWO algorithm to address these issues in this study. QGWO introduces four key features: a Q-learning-based adaptive convergence factor, a segmented and parameterized position update strategy, a long-jump mechanism for population diversity preservation, and the replacement of non-dominant wolves for improved exploration. In addition, the Bayesian optimization algorithm is used to set parameters in QGWO for better performance. To evaluate the quality and robustness of QGWO, extensive numerical and simulation experiments were conducted on IEEE CEC 2022 benchmark functions, comparing it with standard GWO and some of its recent variants. In path planning simulation, QGWO lowers the path cost by 27.4%, improves the convergence speed by 19.06%, and reduces the area under the curve (AUC) by 23.8% over standard GWO, achieving optimal trajectory. Results show that QGWO is an efficient, reliable algorithm for UAV path planning in dynamic environments.

How to ensure network security for modern virtual machine based cloud computing platforms is still an open question. This question becomes more important and urgent to solve, as the fast development of cloud computing in recent years. Though there are many existing solutions, they either provide incomplete protection or neglect important intrinsic characteristics in cloud computing. In this paper, we introduce a novel network security architecture for cloud computing (NetSecCC) considering characteristics of cloud computing. Specifically, it 1) provides protection to both external and interne traffics in cloud computing, 2) attains flexible scalability with respect to virtual middlebox load, and 3) achieves fault-tolerant among virtual middlebox failure. Experiments and simulations on our proof-of-concept prototype of NetSecCC validate that NetSecCC is an effective architecture with minimal performance overhead, and that it can be applied to extensive practical promotion in cloud computing.