Dynamic intrusion detection system based on feature extraction and multidimensional hidden Markov model analysis

In this paper, a novel intrusion detection system based on diversity timing factor, combining the characteristic of dynamic and static adaption, sniffing from multi-stage and analyzing with multi-dimensional hidden Markov model has been proposed. In the proposed mechanism, detection, expert, and console modules are developed. In which, the detection module is deployed with numbers of independent sensors on each node/device of the network. This module not only takes the responsibility to detect and collect all of the desired information on each different timing period and stage, but also denotes specific weighting function to indicate the significance of possible influence and tune the value according to the frequency and times of the occurrence of security events on each collected data. All of the collected audit data and detected normal/abnormal signals will be transferred to the database of the expert module for further integrated evaluation on those multiple observing factors and processed with synthetic information and associative events analysis based on hidden Markov model algorithm on multidimensional. After then, the fuzzy inferring rule is applied for intrusion recognition and identification. The console module is assigned to manage the performance of the system, control all of the sensors for monitoring security events and generate alerts and offer periodically reports and present proposals for taking suitable response and making optimal decision. Experimental results demonstrate that the proposed IDS mechanism possesses good efficiency and performance.