A novel sophisticated hybrid method for intrusion detection using the artificial immune system

Abstract

Artificial immune system (AIS) as a good prototype for developing Machine Learning (ML) is a promising candidate to design Intrusion Detection System (IDS). Two of its prevalent paradigms, the negative selection, and the danger theory, inspired by immunity responses of the powerful human immune system (HIS) are being widely used in this case. In this paper, we proposed a novel sophisticated hybrid method including two defensive lines by using two aforesaid mechanisms. In the proposed method, decentralized cooperation of dendritic cells together with mature detectors acts as a stimulator to generate efficient and accurate detectors and retain memory in the long-term that meant security control via ensuring immunity. Simulating such a system was applicable only by establishing artificial life cycles in the MATLAB environment. Experimental consequences demonstrate that the present schema gradually ameliorates its overall performance during the incipient stages of life cycles and also it is more capable than the previous ones found in the literature.