인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
The increasing proliferation of 6G-enabled Internet of Things (IoT) in the Cyber-Physical Systems (CPS) domain has engendered requirements for distributed, intelligent, and energy-efficient Intrusion Detection Systems (IDS) operating to the edge. Thus, conventional IDS approaches are largely centralized and ignore some vital constraints of edge-centric CPS, such as limited energy, privacy preservation, and real-time responses to threats. Currently existing federated learning (FL)-based IDS solutions cannot optimize data relevance, model sparsity, or trade-offs for privacy efficiency, resulting in communications overhead and impaired performance under resource constraints. To this end, a Lightweight Federated Intrusion Detection Framework for Edge-Centric 6G IoT CPS is proposed in this paper, incorporating five novel analytical modules to achieve decentralized, adaptive, and resource-aware IDS operations. Foremost, Energy-Adaptive Federated Reinforcement Aggregation (EAFRA) will adjust model updates reasonably depending on local energy so that energy and accuracy can be optimized using reinforcement learning methods. Secondly, Spatio-Temporal Uncertainty-aware Federated Attention Filtering (STUFAF) applies Bayesian uncertainty with contextual metadata in giving priority for the informative updates while reducing false positives. Third, Lightweight Self-Evolving Edge Autoencoder Forest (LSE-EAF) assures low latency and high accuracy detection with minimal resource consumption using a hybrid of anomaly detectors. Fourth, Differentially Private Sparse Cluster Aggregation (DPSCA) does adaptive privacy-preserving sparse updates to contextually clustered nodes to balance privacy and communication costs. Finally, Federated Task-Aware Compression with Cyclical Consistency (FTAC<sup>3</sup>) compresses models through task-relevant pruning while maintaining functional consistency on the sets across nodes. The empirical evaluations on standard benchmarks for CPS showed energy savings close to 60%, with a 30% drop in false-positive rates and 70% savings in communication overhead, all while maintaining a detection accuracy of over 93% Sets. This framework marks a huge leap forward in secure, intelligent, and autonomous intrusion detection across infrastructures and scenarios pertaining to next-generation 6G IoT CPS.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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