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In this paper, we present an intruder detection system that uses a wireless sensor network and mobile robots. The sensor network uses an unsupervised fuzzy Adaptive Resonance Theory (ART) neural network to learn and detect intruders in a previously unknown environment. Upon the detection of an intruder, a mobile robot travels to the position where the intruder is detected to investigate. The wireless sensor network uses a hierarchical communication/learning structure, where the mobile robot is the root node of the tree. Our fuzzy ART network is based on Kulakov and Davcev's implementation . We enhanced the fuzzy ART neural network to learn a time-series and detect time-related changes using a Markov model. The proposed architecture is tested on physical hardware. Our results show that our enhanced detection system has a higher accuracy than the basic, original, fuzzy ART system.