Metaheuristic algorithm for anomaly detection in Internet of Things leveraging on a neural-driven multiagent system
作者：
Highlights：
• Multiagent metaheuristic for anomaly detection in Internet of Things.
• Vectors mapping devices activity footprints are manged by self-organizing agents.
• Agents’ operations leverage on a tailored word embedding based similarity function.
• Adaptivity, self-organization and decentralization features enhance anomaly detection in IoT.
摘要
•Multiagent metaheuristic for anomaly detection in Internet of Things.•Vectors mapping devices activity footprints are manged by self-organizing agents.•Agents’ operations leverage on a tailored word embedding based similarity function.•Adaptivity, self-organization and decentralization features enhance anomaly detection in IoT.
论文关键词：Anomaly detection,Activity footprints,Multiagent systems,Word embedding,Bio-inspired model,Internet of Things
论文评审过程：Received 28 December 2020, Revised 30 April 2021, Accepted 15 June 2021, Available online 17 June 2021, Version of Record 30 June 2021.
论文官网地址：https://doi.org/10.1016/j.knosys.2021.107241
