Intrusion detection based on the artificial immune system

Introduction/purpose: The artificial immune system is a computational model inspired by the biological or human immune system. Of particular interest in artificial immune systems is the way the human body reacts to new pathogens and adapts to remain immune for a long period after a disease has been...

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Autor principal:	Danijela D. Protić
Formato:	article
Lenguaje:	EN
Publicado:	University of Defence in Belgrade 2020
Materias:	artificial immune system intrusion detection Military Science U Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/7662b59101bc4da7895b80e36d809db4
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spelling	oai:doaj.org-article:7662b59101bc4da7895b80e36d809db42021-12-02T12:18:38ZIntrusion detection based on the artificial immune system10.5937/vojtehg68-279540042-84692217-4753https://doaj.org/article/7662b59101bc4da7895b80e36d809db42020-10-01T00:00:00Zhttp://scindeks-clanci.ceon.rs/data/pdf/0042-8469/2020/0042-84692004790P.pdfhttps://doaj.org/toc/0042-8469https://doaj.org/toc/2217-4753Introduction/purpose: The artificial immune system is a computational model inspired by the biological or human immune system. Of particular interest in artificial immune systems is the way the human body reacts to new pathogens and adapts to remain immune for a long period after a disease has been combated, which refers to the recognition of known malicious attacks and the way the immune system identifies self-cells not to be reacted to, which refers to the anomaly detection. Methods: Negative selection, positive selection, clonal selection, immune networks, danger theory, and dendritic cell algorithm are presented. Results: A variety of algorithms and models related to artificial immune systems and two classification principles are presented; one based on the detection of a particular attack and the other based on anomaly detection. Conclusion: Artificial immune systems are often used in intrusion detection since they are accurate and fast. Experiments show that the models can be used in both known attack and anomaly detection. Eager machine learning classifiers show better results in the decision, which is an advantage if runtime is not a significant parameter. Dendritic cell and negative selection algorithms show better results for real-time detection. Danijela D. ProtićUniversity of Defence in Belgradearticleartificial immune systemintrusion detectionMilitary ScienceUEngineering (General). Civil engineering (General)TA1-2040ENVojnotehnički Glasnik, Vol 68, Iss 4, Pp 790-803 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial immune system intrusion detection Military Science U Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	artificial immune system intrusion detection Military Science U Engineering (General). Civil engineering (General) TA1-2040 Danijela D. Protić Intrusion detection based on the artificial immune system
description	Introduction/purpose: The artificial immune system is a computational model inspired by the biological or human immune system. Of particular interest in artificial immune systems is the way the human body reacts to new pathogens and adapts to remain immune for a long period after a disease has been combated, which refers to the recognition of known malicious attacks and the way the immune system identifies self-cells not to be reacted to, which refers to the anomaly detection. Methods: Negative selection, positive selection, clonal selection, immune networks, danger theory, and dendritic cell algorithm are presented. Results: A variety of algorithms and models related to artificial immune systems and two classification principles are presented; one based on the detection of a particular attack and the other based on anomaly detection. Conclusion: Artificial immune systems are often used in intrusion detection since they are accurate and fast. Experiments show that the models can be used in both known attack and anomaly detection. Eager machine learning classifiers show better results in the decision, which is an advantage if runtime is not a significant parameter. Dendritic cell and negative selection algorithms show better results for real-time detection.
format	article
author	Danijela D. Protić
author_facet	Danijela D. Protić
author_sort	Danijela D. Protić
title	Intrusion detection based on the artificial immune system
title_short	Intrusion detection based on the artificial immune system
title_full	Intrusion detection based on the artificial immune system
title_fullStr	Intrusion detection based on the artificial immune system
title_full_unstemmed	Intrusion detection based on the artificial immune system
title_sort	intrusion detection based on the artificial immune system
publisher	University of Defence in Belgrade
publishDate	2020
url	https://doaj.org/article/7662b59101bc4da7895b80e36d809db4
work_keys_str_mv	AT danijeladprotic intrusiondetectionbasedontheartificialimmunesystem
_version_	1718394518556901376

Intrusion detection based on the artificial immune system

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