A Review of Artificial Immune System Based Security Frameworks for MANET

Jim, Lincy Elizebeth; Gregory, Mark A

doi:10.4236/ijcns.2016.91001

Cited by 6 publications

(4 citation statements)

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“…For anomaly detection in HIDS, identification of unknown attacks by an AIS can be combined with either EA [32], which evolves memory cells for secondary responses, or with fuzzy rules [111]. A review of AIS applications in MANET security can be found in [59].…”

Section: Artificial Immune Systemsmentioning

confidence: 99%

Survey on evolutionary computation methods for cybersecurity of mobile ad hoc networks

2018

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In this paper, a comprehensive survey of evolutionary computation (EC) methods for cybersecurity of mobile ad hoc networks (MANETs) is presented. Typically, EC methods are named based on the natural processes inspiring them, such as swarm intelligence (e.g., ant colony optimization, artificial bee colony, and particle swarm optimization), evolutionary algorithms (e.g., genetic algorithms, genetic programming, grammatical evolution, and differential evolution), artificial immune systems, and evolutionary games analyzing strategic interactions among different population types. We introduce these methods with their typical applications, and commonly used algorithms to improve cybersecurity within the scope of MANETs. Ongoing and speedy topology changes, multi-hop communication, nonhierarchical organization, and power and computational limitations are among the intrinsic characteristics of MANETs causing cybersecurity vulnerabilities. We describe basic defense mechanisms in MANETs for vulnerability detection, attack deterrence, prevention and recovery, and risk mitigation. We classify principal applications of EC as intrusion detection, trust management, and cryptography in cybersecurity systems to countermeasure adversarial activities.This research is supported by D01 W911SR-14-2-0001-0014. The contents of this document represent the views of the authors and are not necessarily the official views of, or endorsed by, the US Government, Department of Defense, Department of the Army or US Army Communications-Electronic RD&E Center. Dr. Cem Safak Sahin is currently an MIT Lincoln Laboratory employee; no Laboratory funding or resources were used to produce the result/findings reported in this publication.

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Section: Artificial Immune Systemsmentioning

confidence: 99%

Survey on evolutionary computation methods for cybersecurity of mobile ad hoc networks

2018

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“…AIS approach is applied to several applications [13], it aims at solving complex computational or engineering problems such as pattern recognition [10,15,16,19,24,33,72] computer virus detection and elimination [27,67], and optimization [28].…”

Section: Artificial Immune Systems For Handwritten Recognitionmentioning

confidence: 99%

An artificial immune system for offline isolated handwritten arabic character recognition

Boufenar

Batouche²,

Schoenauer

2016

Evolving Systems

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Character recognition plays an important role in the modern world. In recent years, character recognition systems for different languages has gain importance. The recognition of Arabic writing is still an important challenge due to its cursive nature and great topological variability. The Artificial Immune System is a supervised learning technique that embodies the concepts of natural immunity to cope with complex classification problems. The objective of this research is to investigate the applicability of an Artificial Immune System in Offline Isolated Handwritten Arabic Characters. The developed system is composed of three main modules: preprocessing, feature extraction and recognition. The system was trained and tested with tenfold cross-validation technique on an original realistic database that we built from the well-known IFN/ENIT benchmark. Parameter tuning was performed with a grid-search algorithm with leave-one-out cross-validation. The obtained results of the proposed system are promising with a classification rate of 93.25% and often outperform most well-known classifiers from Scikit Learn Library.

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“…However these researches are still immature, under development, and not yet effectively exploited in the industry. Jim & al have reviewed most of the immune inspired algorithms and their use in mobile ad hoc networks [13]. Ishida [14] as well reviewed diverse immune network models and discussed the benefits of each approach.…”

Section: Danger Theory and Dendritic Cell Algorithmmentioning

confidence: 99%