Anjum Mohd, Dutta Ashit Kumar, Elrashidi Ali, Shahab Sana, Aldrees Asma, Shaikh Zaffar Ahmed, Aljohani Abeer
Department of Computer Engineering, Aligarh Muslim University, 202002, Aligarh, India.
Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Ad Diriyah, 13713, Riyadh, Saudi Arabia.
Sci Rep. 2025 Aug 1;15(1):28050. doi: 10.1038/s41598-025-10826-0.
The Internet of Things (IoT) consists of physical objects and devices embedded with network connectivity, software, and sensors to collect and transmit data. The development of the Internet of Things (IoT) has led to various security and privacy issues, including distributed denial-of-service (DDoS) attacks. Conventional attack detection methods face significant challenges related to privacy, scalability, and adaptability due to the dynamic nature of IoT environments. To address these limitations, this research proposes GraphFedAI, a novel framework that integrates adaptive session-based graph modeling, Pearson correlation-guided feature selection, interpolation-aware graph neural network (GNN) training, and federated learning to enable robust, scalable, and privacy-preserving DDoS detection in heterogeneous Internet of Things (IoT) networks.The framework represents the IoT network as dynamic graphs where communication patterns among devices are modeled as edges that evolve over time. Graph neural networks are utilized to extract both temporal and structural features from these graphs, thereby enhancing the accuracy of DDoS detection. Federated learning is incorporated to maintain data privacy by training models locally on each device without sharing raw data. This integration also ensures system scalability, as FL adapts training based on localized network topology.The system is evaluated using the CIC-IoT-2023 dataset, demonstrating its effectiveness in achieving high detection accuracy, low false positive rates, and strong resilience under dynamic IoT conditions.
物联网（IoT）由嵌入网络连接、软件和传感器以收集和传输数据的物理对象和设备组成。物联网（IoT）的发展引发了各种安全和隐私问题，包括分布式拒绝服务（DDoS）攻击。由于物联网环境的动态特性，传统的攻击检测方法在隐私、可扩展性和适应性方面面临重大挑战。为了解决这些限制，本研究提出了GraphFedAI，这是一个新颖的框架，它集成了基于自适应会话的图建模、皮尔逊相关引导的特征选择、插值感知图神经网络（GNN）训练和联邦学习，以在异构物联网（IoT）网络中实现强大、可扩展和隐私保护的DDoS检测。该框架将物联网网络表示为动态图，其中设备之间的通信模式被建模为随时间演变的边。利用图神经网络从这些图中提取时间和结构特征，从而提高DDoS检测的准确性。通过在每个设备上本地训练模型而不共享原始数据，纳入联邦学习以维护数据隐私。这种集成还确保了系统的可扩展性，因为联邦学习根据局部网络拓扑调整训练。使用CIC-IoT-2023数据集对该系统进行了评估，证明了其在动态物联网条件下实现高检测准确率、低误报率和强弹性方面的有效性。
