Combine a Population-based Incremental Learning with Artificial Immune System for Intrusion Detection System
This research focus on the intrusion detection system (IDS) development which using artificial immune system (AIS) with population based incremental learning (PBIL). AIS have powerful distinguished capability to extirpate antigen when the antigen intrude into human body. The PBIL is based on past learning experience to adjust new learning. Therefore we propose an intrusion detection system call PBIL-AIS which combine two approaches of PBIL and AIS to evolution computing. In AIS part we design three mechanisms such as clonal selection, negative selection and antibody level to intensify AIS performance. In experimental result, our PBIL-AIS IDS can capture high accuracy when an intrusion connection attacks.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1072796Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1539
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