IPv6 over Low-powered Wireless Personal Area Networks (6LoWPAN) have grown in importance in recent years, with the Routing Protocol for Low Power and Lossy Networks (RPL) emerging as a major enabler. However, RPL can be subject to attack, with severe consequences. Most proposed IDSs have been limited to specific RPL attacks and typically assume a stationary environment. In this article, we propose the first adaptive hybrid IDS to efficiently detect and identify a wide range of RPL attacks (including DIO Suppression, Increase Rank, and Worst Parent attacks, which have been overlooked in the literature) in evolving data environments. We apply our framework to networks under various levels of node mobility and maliciousness. We experiment with several incremental machine learning (ML) approaches and various 'concept-drift detection' mechanisms (e.g. ADWIN, DDM, and EDDM) to determine the best underlying settings for the proposed scheme.
Meta-Quantum Ensemble Framework for Robust Network Intrusion Detection
Birla Institute of Technology and Science, Pilani, Dubai Campus（比拉理工理工学院迪拜校区）；New York University Abu Dhabi (NYUAD)（纽约大学阿布扎比分校）；NYUAD Research Institute（纽约大学阿布扎比分校研究院）
Leveraging the Power of Ensemble Learning for Secure Low Altitude Economy
National Key Laboratory on Near-Surface Detection（近地表探测国家重点实验室）；Chengdu Fluid Dynamics Innovation Center（成都流体力学创新中心）；College of Computing and Data Science, NTU（南洋理工大学计算与数据科学学院）；College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；Nanyang Technological University（南洋理工大学）
SecureDyn-FL: A Robust Privacy-Preserving Federated Learning Framework for Intrusion Detection in IoT Networks
Sir Syed CASE Institute of Technology（Sir Syed CASE 理工学院）；Habib University（哈比布大学）；Yeunganam University（延世南大学）；University of Nottingham Ningbo China（诺丁汉大学宁波分校）；Korea University（高丽大学）
XAI-SOH-FL: Enhancing SOH-FL with Adaptive Aggregation and Explainable AI for Intrusion Detection in Heterogeneous IoT
School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST)（电气工程与计算机科学学院（SEECS），国家 sciences and Technology（NUST））
Hybrid LLM-Enhanced Intrusion Detection for Zero-Day Threats in IoT Networks
Dept. of Computer Engineering, The Hashemite University（计算机工程系，哈希米大学）；School of Computer Science and Technology, Algoma University（计算机科学与技术学院，阿尔戈马大学）；College of Computer Science and Engineering, University of Jeddah（计算机科学与工程学院，朱德赫大学）；School of Electrical Engineering and Computer Science, University of Ottawa（电气工程与计算机科学学院，渥太华大学）
WeiDetect: Weibull Distribution-Based Defense against Poisoning Attacks in Federated Learning for Network Intrusion Detection Systems
Department of Computer Applications, Cochin University of Science and Technology（科钦大学科学与技术学院计算机应用系）；Department of Mathematics, University of Padua（帕多瓦大学数学系）；University of Campinas (Unicamp)（坎皮纳斯大学）
