Energy Audition based Cyber-Physical Attack Detection System in IoT

摘要：

　　In this paper,we propose an attack detection framework in the In-ternet of Things(IoT)devices.The framework applies a data-centric method to process the energy consumption data and classify the attack status of the monitored device.We implement the framework in real hardware,and emulate common types of attacks to evaluate the performance of the attack detection framework.Due to the characteristic of the energy data,not only cyber attacks but also physical attacks such as heating are also emulated and tested.To shorten the detection time,a two-stage strategy is also proposed to first apply a short time window for a rough detection,then a long time window to the fine detection of anomalies.The accuracy of short-term detection is 90%,while in the long-term detections the accuracy reaches 99.5%.Due to the nature of information from energy consumption data,the framework is more secure in cases the kernel of the device is already compromised.

关键词： Cyber-physical attack detection Internet of Things Energy con-sumption Machine learning

作者: Yang Shi Fangyu Li WenZhan Song Xiang-Yang Li Jin Ye

作者单位: Center for Cyber-Physical Systems,University of Georgia Athens,Georgia University of Science and Technology of China Hefei,China

会议类型: 国际会议

会议名称: 2019国图灵大会(ACM Turing Celebration conference-China 2019 )

会议地点: 成都

会议语种:英文

页码: 481-485

在线出版日期: 2019-05-17（万方平台首次上网日期，不代表论文的发表时间）