Semantic Intrusion Detection System using pattern matching and state transition analysis

Emerging technologies have metamorphosed the nature of surveillance and monitoring application, but the sensory data collected using various gadgets still remain changeable and poorly synchronized. Recently data mining and state transition analysis are becoming important components in identifying intrusions. This paper proposes a semantic based intrusion detection scheme where state transition analysis, pattern matching and data mining techniques are integrated to maximize the detection accuracy. Patterns and rules are formulated based on the events detected by WSN. The sink receives information about the several events happening in the coverage area and correlates the streaming data (events) in spatial domain (several sensors) and time domain. The semantic rules are developed using ANTLR tool. The proposed scheme has been validated using a surveillance system for detecting an intruder. Implementations prove that the proposed hybrid intrusion detection scheme can accurately detect an intruder with the help of a wired network and an appropriate database.