A Hybrid Intelligence/Multi-agent System Approach for Mining Information Assurance Data

Organizations of all sizes wrestle with the problem of "coping with information overload." They ingest more and more data, in new and varied formats every day, and struggle more and more vigorously to find the nuggets of knowledge hidden away within the vast amounts of information. Furthermore, due to the various and pervasive types of noise in the haystack of data, it is increasingly and exceedingly difficult to discern between the shining false shards and the true needles of knowledge. In the grander scheme of our work we intend to demonstrate that a hybrid intelligence/multi-agent systems-based overarching layer, which collates, compares and contrasts input from several traditional data mining applications below it, will yield far more accurate results than any one application acting on its own.