An adaptive sensor network for home intrusion detection by human activity profiling
An adaptive sensor network for home intrusion detection by human activity profiling
复制标题
通过人类活动分析进行家庭入侵检测的自适应传感器网络
DOI：
10.1007/s10015-011-0872-5
复制
发表时间：
2011
影响因子：
0.9
通讯作者：
Yoshiteru Ishida
中科院分区：
文献类型：
--
作者：
Masahiro Tokumitsu;Masashi Murakami;Yoshiteru Ishida
提出了一种用于家庭入侵检测的自适应传感器网络。传感器网络结合了基于轮廓的异常检测和基于隐马尔可夫模型 (HMM) 的自适应信息处理，使系统能够自动训练和调整轮廓。已经通过实验研究了漏报和误报之间的权衡。几种类型的假设入侵已经过测试并成功检测到。然而，假设的异常情况，例如假设居民因突发疾病摔倒，很难被发现。
An adaptive sensor network for home intrusion detection is proposed. The sensor network combines profile-based anomaly detection and adaptive information processing based on hidden Markov models (HMM) that allow the system to train and tune the profiles automatically. The trade-off between miss-alarms and false alarms has been studied experimentally. Several types of hypothetical intrusion have been tested and successfully detected. However, hypothetical anomalies such as supposing that a resident has fallen down due to sudden illness have been difficult to detect.