Physical intrusion monitoring via local-global network and deep isolation forest based on heterogeneous signals

This paper proposes a local-global semi-sharing network (LGSSN) for heterogeneous signals in physical intrusion monitoring. The signals are collected by heterogeneous distributed optical fiber sensor (DOFS). The local classifier of LGSSN is constructed via a hybrid model deep isolation forest (DIF). It can extract the dominant representations of original high-dimensional signals through deep autoencoders (DAE). Then, an isolation forest (IF) is added to the last layer to obtain local classification for extremely imbal-anced cases. In addition, the network is simplified by semi-sharing strategy, and a parallel computing framework is presented for accelerating the process. Further, the final decision on monitoring state is acquired by a Bayesian inference-based global integrated monitor (GIM) with enhanced classification accuracy. The proposed strategy is tested on a monitoring application along the Nanjing Metro Line S7, Jiangsu Province, China. Comparative experimental results illustrate the feasibility and effectiveness of proposed strategy. (c) 2021 Elsevier B.V. All rights reserved.