Designing intrusion detection systems for cloud environments requires a framework that not only achieves high accuracy but also effectively identifies a wide spectrum of attacks, including DoS/DDoS, Probe, R2L, and U2R, under dynamic and noisy network conditions. This paper presents the integrated AQSE-QDST framework, which combines the Adaptive Quantum Swarm Evolution (AQSE) algorithm for feature space optimization with QDST-Net (Quantum-Inspired Dual Spatial-Temporal Network) as the classification engine. In addition, the Entropy-Guided Adaptive Flow Normalization (EAFN) mechanism is incorporated to accelerate convergence and reduce fitness fluctuations during the early stages of training. This three-layer design enables robust feature extraction, dimensionality reduction, and stable feature selection for diverse and imbalanced datasets. Experiments conducted on three benchmark cybersecurity datasets demonstrate that the proposed framework performs effectively in detecting both frequent and rare attacks. The model achieves accuracy rates of 99.69% on NSL-KDD, 98.86% on CIC-IDS2017, and 98.65% on UNSW-NB15, highlighting its capability to detect both high-volume attacks such as DoS/DDoS and Probe and low-frequency attacks such as R2L and U2R. Furthermore, convergence analysis indicates that AQSE-QDST outperforms baseline methods by maintaining more stable fitness values and more consistent feature selection behavior.
Rasoul Farahi, Nahideh Derakhshanfard, Roya Abdollahzadeh Sarnaghi et al.· Discover Internet of Things· 0 citations
This study focuses on the important task of optimizing device clustering and assigning them to edge servers, while also implementing data redistribution in hierarchical semi-synchronous federated learning within the realm of advancing edge computing. Our research goal is to increase the performance and scalability of federated learning systems by improving resource allocation and data processing efficiency, which will in turn enhance edge computing frameworks. The current literature does not have thorough methods that can effectively combine model accuracy with optimal device clustering algorithms in hierarchical semi-synchronous federated learning, leading to below-par performance and inefficient use of resources. This difference highlights the need for creative measures that enhance not only model training accuracy but also the grouping of devices as opposed to current methods. The study utilizes a Graph Neural Network (GNN) to group IoT devices according to their hardware features and local datasets, then applies the K-means algorithm to create efficient device clusters. After that, Hybrid Data Redistribution is used to equalize local datasets in each cluster, and Proximal Policy resource allocation optimization algorithm is implemented to allocate devices to edge servers according to bandwidth usage, and energy consumption based on real-time updates, ultimately enabling hierarchical semi-synchronous federated learning to improve model training. The results show a 15% increase in clustering metrics compared to current algorithms, showcasing how our method improves device assignment and data redistribution in hierarchical semi-synchronous federated learning, addressing issues in model accuracy and resource optimization.
Hadi Farajvand, Nahideh Derakhshanfard, Abbas Mirzaei et al.· Scientific Reports· 0 citations
A review of federated learning through a structured taxonomy that covers its core architectural paradigms, major learning types, model training approaches, and aggregation mechanisms, and analyzes the principal challenges confronting FL, including privacy and security risks, statistical and system heterogeneity, communication constraints, and global model divergence.
Mahdiyeh Velaei, Hosna Ghahramani, Ali Ghaffari et al.· Cluster Computing· 0 citations