Semantic-based lightweight ontology learning framework
Zhang Yu, Morteza Saberi, Elizabeth Chang · Proceedings of the International Conference on Web Intelligence · 2017
Building ontology for wireless network intrusion detection is an emerging method for the purpose of achieving high accuracy, comprehensive coverage, self-organization and flexibility for network security. In this paper, we leverage the power of Natural Language Processing (NLP) and Crowdsourcing for this purpose by constructing lightweight semi-automatic ontology learning framework which aims at developing a semantic-based solution-oriented intrusion detection knowledge map using documents from Scopus. Our proposed framework uses NLP as its automatic component and Crowdsourcing is applied for the semi part. The main intention of applying both NLP and Crowdsourcing is to develop a semi-automatic ontology learning method in which NLP is used to extract and connect useful concepts while in uncertain cases human power is leveraged for verification. This heuristic method shows a theoretical contribution in terms of lightweight and timesaving ontology learning model as well as practical value by providing solutions for detecting different types of intrusions.