Distributed denial-of-service (DDoS) attacks pose a significant cybersecurity threat to software-defined networks (SDNs). This paper proposes a feature-engineering- and machine-learning-based approach to detect DDoS attacks in SDNs. First, the CSE-CIC-IDS2018 dataset was cleaned and normalized, and the optimal feature subset was found using an improved binary grey wolf optimization algorithm. Next, the optimal feature subset was trained and tested in Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (k-NN), Decision Tree, and XGBoost machine learning algorithms, from which the best classifier was selected for DDoS attack detection and deployed in the SDN controller. The results show that RF performs best when compared across several performance metrics (e.g., accuracy, precision, recall, F1 and AUC values). We also explore the comparison between different models and algorithms. The results show that our proposed method performed the best and can effectively detect and identify DDoS attacks in SDNs, providing a new idea and solution for the security of SDNs.

Data aggregation plays a critical role in sensor networks for efficient data collection. However, the assumption of uniform initial energy levels among sensors in existing algorithms is unrealistic in practical production applications. This discrepancy in initial energy levels significantly impacts data aggregation in sensor networks. To address this issue, we propose Data Aggregation with Different Initial Energy (DADIE), a novel algorithm that aims to enhance energy-saving, privacy-preserving efficiency, and reduce node death rates in sensor networks with varying initial energy nodes. DADIE considers the transmission distance between nodes and their initial energy levels when forming the network topology, while also limiting the number of child nodes. Furthermore, DADIE reconstructs the aggregation tree before each round of data transmission. This allows nodes closer to the receiving end with higher initial energy to undertake more data aggregation and transmission tasks while limiting energy consumption. As a result, DADIE effectively reduces the node death rate and improves the efficiency of data transmission throughout the network. To enhance network security, DADIE establishes secure transmission channels between transmission nodes prior to data transmission, and it employs slice-and-mix technology within the network. Our experimental simulations demonstrate that the proposed DADIE algorithm effectively resolves the data aggregation challenges in sensor networks with varying initial energy nodes. It achieves 5–20% lower communication overhead and energy consumption, 10–20% higher security, and 10–30% lower node mortality than existing algorithms.

The vast number of IoT devices is distributed across multiple trust domains, each with distinct security policies, trust models, and permission management methods. This diversity increases the risk of privacy exposure during cross-domain communications. At the same time, traditional authentication methods have problems such as complex certificate management, high risk of key escrow, and reliance on trusted third parties. To address the above problems, this paper proposes a novel method, enhancing device privacy through certificateless encryption and zero-knowledge proof (DPCZK). DPCZK achieves decentralization by leveraging a consortium blockchain as a trust bridge across different domains. The adoption of certificateless encryption mitigates the incomplete trust issues associated with the key generation center. Furthermore, DPCZK incorporates an identity-hiding mechanism based on zero-knowledge proof, enabling devices to authenticate and interact with resources anonymously during cross-domain operations, thereby safeguarding their privacy. Additionally, through threshold technology, the target domain can reveal the true identities of malicious devices and revoke their access rights, ensuring a balanced approach to security and privacy protection. The proposed scheme has been experimentally validated in a virtual environment and compared with existing solutions. Results demonstrate that DPCZK offers significant improvements in both effectiveness and efficiency.