A1 Journal article – refereed
Artificial Immune System Based Intrusion Detection: Innate Immunity Using an Unsupervised Learning Approach

List of Authors: Farhoud Hosseinpour, Payam Vahdani Amoli, Fahimeh Farahnakian, Juha Plosila, Timo Hämäläinen
Publisher: AICIT
Publication year: 2014
Journal: International Journal of Digital Content Technology and Its Applications
Journal acronym: JDCTA
Volume number: 8
Issue number: 5
eISSN: 2233-9310


This paper presents an intrusion detection system architecture based on the artificial immune system concept. In this architecture, an innate immune mechanism through unsupervised machine learning methods is proposed to primarily categorize network traffic to “self” and “non-self” as normal and suspicious profiles respectively. Unsupervised machine learning techniques formulate the invisible structure of unlabeled data without any prior knowledge. The novelty of this work is utilization of these methods in order to provide online and real-time training for the adaptive immune system within the artificial immune system. Different methods for unsupervised machine learning are investigated and DBSCAN (density-based spatial clustering of applications with noise) is selected to be utilized in this architecture. The adaptive immune system in our proposed architecture also takes advantage of the distributed structure, which has shown better self-improvement rate compare to centralized mode and provides primary and secondary immune response for unknown anomalies and zero-day attacks. The experimental results of proposed architecture is presented and discussed.

Last updated on 2019-29-01 at 11:34