Learning with Querying and its Application in Network Security

Abstract

Understanding the ability of learning in both humans and non-humans is an important research crossing the boundaries between several scientific disciplines from computer science to brain science and psychology. In this chapter, the authors first introduce a query based learning concept (learning with query) in which all the minds’ beliefs and actions will be revised by observing the outcomes of past mutual interactions (selective-attention and self-regulation) over time. That is, moving into an active learning and aggressive querying method will be able to focus on effectiveness to achieve learning goals and desired outcomes. Secondly, they show that the proposed method has better effectiveness for several learning algorithms, such as decision tree, particle swarm optimization, and self-organizing maps. Finally, a query based learning method is proposed to solve network security problems as a sample filter at intrusion detection. Experimental results show that the proposed method can not only increase the accuracy detection rate for suspicious activity and recognize rare attack types but also significantly improve the efficiency of intrusion detection. Therefore, it is good to design and to implement an effective learning algorithm for information security.