An Efficient Intrusion Detection System Based on Pattern Matching and State Transition Analysis
Emerging technologies have metamorphosed the nature of surveillance and monitoring applications, but the sensory data collected using various gadgets is poorly synchronized and the analysis of such data remain changeable. Over the years, the need for security and surveillance systems has changed significantly due to the influence of various events and attacks. Anomaly detection systems based on various soft computing techniques like genetic algorithms, neural networks and fuzzy logic exist in literature. Recently data mining and state transition analysis are becoming important components in identifying intrusions. From a data mining perspective, sensor network problems are characterized by a large number of variables (sensors), producing a continuous stream of data, in a dynamic environment.