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This paper describes the development of an intelligent intrusion detection system for use within an embedded device network consisting of interconnected agents. Integral behavior types are categorized by focusing primarily on inter-device requests and actions rather than at a packet or link level. Machine learning techniques use these observed behavioral actions to track devices which deviate from normal protocol. Deviant behavior can be analyzed and flagged, enabling interconnected agents to identify an intruder based upon the historical distribution of behavioral data that is accumulated about the possible deviant agent. Simulation results from the prototype system correlate detection accuracy with a tunable input tolerance factor.