In distributed agent architecture, tasks are performed on multiple computers which are sometimes spread across different locations. While it is important to collect security critical sensory information from the agent society, it is equally important to analyze and report such security events in a precise and useful manner. Data mining techniques are found to be very efficient in the generation of security event profiles. This paper describes the implementation of such a security alert mining tool which generates profiles of security events collected from a large agent society. In particular, our previous work addressed the development of a security console to collect and display alert message (IDMEF) from a Cougaar (agent) society. These messages are then logged in an XML database for further off-line analysis. In our current work, stream mining algorithms are applied for sequencing and generating frequently occurring episodes, and then finding association rules among frequent candidate episodes. This alert miner could profile most prevalent patterns as indications of frequent attacks in a large agent society.
The paper describes the design and development of an efficient visualization tool called security console for monitoring security related events in a large agent society (Cougaar). This administrative tool is primarily used to collect and process alert messages generated by various sensors across the distributed agent society. This tool exploits the agents’ hierarchical structural for aggregating security events in order to discover correlation among them. In particular, it logically groups related alerts from raw messages (by removing duplicates, if any) and applies data mining techniques (like association rules and frequency episode learning), to discover situations that have certain characteristics in common. We performed extensive experimentation with the security console in various attack scenarios that generate large number of alert messages. Reported results exhibit that this alert monitoring and correlation tool can provide a profile of attack patterns which occur more frequently in the monitored agent society.
This paper describes a new genetic approach called the structured genetic algorithm (sGA) for automatic registration of digital images. The specialty of this genetic model lies primarily in its redundant genetic material and a gene activation mechanism which utilizes a multi-layered structure for the chromosome. The additional genetic material serves to retain multiple optional solution spaces in parameter optimization. The structured genetic model is applied here to minimize the registration measures in image transformations, as investigated by Fitzpatrick and Grefenstatte with the simple GA. The results demonstrate that sGA is a much faster and robust search method that is guaranteed to reach a global optimum by adaptively estimating the subspace from the maximum space during the evolutionary process. Preliminary experimental results are reported.