  Indoor intrusion detection technology has been widely utilized in network
security monitoring, smart city, entertainment games, and other fields. Most
existing indoor intrusion detection methods directly exploit the Received
Signal Strength (RSS) data collected by Monitor Points (MPs) and do not
consider the instability of WLAN signals in the complex indoor environments. In
response to this urgent problem, this paper proposes a novel WLAN indoor
intrusion detection method based on deep signal feature fusion and Minimized
Multiple Kernel Maximum Mean Discrepancy (Minimized-MKMMD). Firstly, the
multi-branch deep convolutional neural network is used to conduct the
dimensionality reduction and feature fusion of the RSS data, and the tags are
obtained according to the features of the offline and online RSS fusion
features that are corresponding to the silence and intrusion states, and then
based on this, the source domain and target domain are constructed
respectively. Secondly, the optimal transfer matrix is constructed by
minimizing MKMMD. Thirdly, the transferred RSS data in the source domain is
utilized for training the classifiers that are applying in getting the
classification of the RSS fusion features in the target domain in the same
shared subspace. Finally, the intrusion detection of the target environment is
realized by iteratively updating the process above until the algorithm
converges. The experimental results show that the proposed method can
effectively improve the accuracy and robustness of the intrusion detection
system.
