Anomaly detection based on hybrid artificial immune principles

Abstract

Purpose – Anomaly detection of network attacks has become a high priority because of the need to guarantee security, privacy and reliability. This work aims to describe both intelligent immunological approaches and traditional monitoring systems for anomaly detection. Design/methodology/approach – Author investigated different artificial immune system (AIS) theories and proposes how to combine different ideas to solve problems of network security domain. An anomaly detection system that applies those ideas was built and tested in a real time environment, to test the pros and cons of AIS and clarify its applicability. Rather than building a detailed signature based model of intrusion detection system, the scope of this study tries to explore the principle in an immune network focusing on its self-organization, adaptive learning capability, and immune feedback. Findings – The natural immune system has its own intelligent mechanisms to detect the foreign bodies and fight them and without it, an individual cannot live, even just for several days. Network attackers evolved new types of attacks. Attacks became more complex, severe and hard to detect. This results in increasing needs for network defense systems, especially those with ability to extraordinary approaches or to face the dynamic nature of continuously changing network threats. KDD CUP'99 dataset are used as a training data to evaluate the proposed hybrid artificial immune principles anomaly detection. The average cost of the proposed model was 0.1195 where that the wining of KDD99 dataset computation had 0.233. Originality/value – It is original to introduce investigation on the vaccination biological process. A special module was built to perform this process and check its usage and how it could be formulated in artificial life.

Examining the impact of deep learning technology capability on manufacturing firms: moderating roles of technology turbulence and top management support

“…Anomaly detection capability (ADC) helps to identify the data points, observations, or events that deviate from the dataset’s usual behavior (Jianhong & Yanxiang, 2021 ; Lu et al, 2019 ). ADC helps in detecting vital incidents, such as include small technological glitches in the equipment used in the manufacturing unit or abnormal behavior of an in-service equipment, that might affect the output quality of the manufacturing unit (Ko et al, 2017 ; Salah Sobh, 2013 ).…”