Agent-based artificial immune system approach for adaptive damage detection in monitoring networks

Chen, Bo

doi:10.1016/j.jnca.2010.03.011

Cited by 25 publications

(7 citation statements)

References 25 publications

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“…In the area of AISs, the antibodies are usually called detectors as their job is to detect certain circumstances (i.e., the presence of non-self). The detectors are often represented as feature vectors representing antigenic patterns able to detect changes in behavior [ 111 ]. Often the problem space is represented by an n -dimensional space, and the detectors are hyperspheres that use a matching rule based on an individual membership or distance function (e.g., Euclidean distance).…”

Section: Classical Ais Theories and Their Applicationsmentioning

confidence: 99%

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A Review on Immune-Inspired Node Fault Detection in Wireless Sensor Networks with a Focus on the Danger Theory

Widhalm

Goeschka

Kästner

2023

Sensors

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The use of fault detection and tolerance measures in wireless sensor networks is inevitable to ensure the reliability of the data sources. In this context, immune-inspired concepts offer suitable characteristics for developing lightweight fault detection systems, and previous works have shown promising results. In this article, we provide a literature review of immune-inspired fault detection approaches in sensor networks proposed in the last two decades. We discuss the unique properties of the human immune system and how the found approaches exploit them. With the information from the literature review extended with the findings of our previous works, we discuss the limitations of current approaches and consequent future research directions. We have found that immune-inspired techniques are well suited for lightweight fault detection, but there are still open questions concerning the effective and efficient use of those in sensor networks.

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Section: Classical Ais Theories and Their Applicationsmentioning

confidence: 99%

“…The fault diagnosis algorithm proposed in [ 182 ] uses a clonal selection-inspired approach to identify hard and soft faulty sensor nodes in a WSNs. Additionally, for structural health monitoring (SHM) with WSNs, some immune-inspired approaches have been proposed [ 111 , 199 ].…”

Section: Literature Review On Immune-inspired Approachesmentioning

confidence: 99%

A Review on Immune-Inspired Node Fault Detection in Wireless Sensor Networks with a Focus on the Danger Theory

Widhalm

Goeschka

Kästner

2023

Sensors

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“…Machine learning's adaptability is especially valuable in creating security systems tailored to the specific needs and risks of organizations. Shen et al [6] discussed the concept of adaptive security systems, highlighting how machine learning allows these systems to continuously learn from their environment and adjust their security measures accordingly. This adaptability ensures that security systems evolve alongside the organization's changing threat landscape, reducing the risk of vulnerabilities.…”

Section: Adaptive Security Systemsmentioning

confidence: 99%

Synergizing Machine Learning and Cybersecurity for Robust Digital Protection.

Shukla

2023

Preprint

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In today's dynamic digital landscape, cyber threats are growing in sophistication and frequency, challenging traditional cybersecurity methods. This research paper, titled "Fortifying Digital Defenses: Exploring the Synergy of Machine Learning and Cybersecurity," delves into the pivotal role of machine learning in bolstering our cyber defenses. Machine learning, a subset of artificial intelligence, has emerged as a powerful tool in the fight against cyber adversaries. The paper explores how machine learning techniques can significantly enhance threat detection, incident response, and security system adaptability. It showcases real-world applications, such as anomaly detection, behavioral analysis, and predictive threat intelligence, illustrating the symbiotic relationship between machine learning and cybersecurity. By embracing machine learning, organizations can stay ahead of emerging threats, adapt more effectively, and fortify their digital defenses in this ever-evolving digital age.

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“…Relying on Fuzzy Clustering algorithm, a few years later the same authors provided an unsupervised damage classification algorithm able to distinguish among different damage classes, each represented by a specific memory cell, according to the similarity in the monitored data (Bo and Zang, 2009). The AIPR structural damage classification was then integrated into an automatic immuneinspired monitoring system composed of mobile agents mimicking B-cells, which patrolled over a wireless network of different sensors distributed throughout the monitored structure (Chen, 2010;Liu and Chen, 2011). Finally, the AIPR method was upgraded to allow for anomaly identification even in the absence of specific data sample in the training stage (Chen and Zang, 2011), thereby enabling to solve unsupervised pattern recognition problems.…”

Section: Immune Systemmentioning

confidence: 99%

An Overview on Structural Health Monitoring: From the Current State-of-the-Art to New Bio-inspired Sensing Paradigms

Lourénço¹,

Amado-Mendes²,

Ramos³

et al. 2018

IJBIC

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Maria-Giovanna Masciotta obtained her dual-degree PhD in 2015 within a joint programme between the University of Minho (Portugal) and the University of "G. d'Annunzio" of Chieti-Pescara (Italy), being therefore awarded the mention of "Doctor Europeaus". Since 2012, she has been carrying out her research activity at the Institute for Sustainability and Innovation in Structural Engineering (ISISE), which is the largest research unit in Portugal in the field of Structural Engineering. Her research interests are related to the engaging fields of modal testing, structural health monitoring and damage identification of heritage structures, being author of several publications on these subjects, but also include bio-inspired sensing paradigms and advanced structural analysis of masonry constructions.Alberto Barontini, graduated in Civil Engineering by University of Florence, in 2015, is currently conducting his PhD programme on bio-inspired Structural Health Monitoring systems at University of Minho, in collaboration with University of Coimbra. His research domains include the study of bio-inspired optimization and learning algorithms applied to identification (detection, localization and quantification) of damage and safety assessment. His research interests also include in-situ non-destructive testing, advanced structural analysis, dynamics of structures and earthquake engineering. Luís F. Ramos, graduated in Civil Engineer by University of Minho in 1999, made his doctorate thesis in 2007, also in University of Minho, about Damage Identification on Masonry Structures based on Vibration Signatures. His current research interests include non-destructive tests for damage detection, structural health monitoring of ancient constructions, dynamic tests, finite element analysis, non-linear analysis of masonry constructions, and earth constructions, being author of several publications, in both conferences and in specialized journals, on those topics. He has been involved in several specialized consultancy projects in monuments and in several research projects. Paulo Amado-Mendes, graduated in Civil Engineering at the University of Coimbra (Portugal) in 2001, completed his PhD in Civil Engineering, specialization Construction Sciences, in 2010, also at the University of Coimbra. His scientific areas of research comprise: civil engineering and applied physics and mechanics; wave propagation in elastic and acoustic media; fluvial and maritime hydraulics and sediment transport and *

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Agent-based artificial immune system approach for adaptive damage detection in monitoring networks

Cited by 25 publications

References 25 publications

A Review on Immune-Inspired Node Fault Detection in Wireless Sensor Networks with a Focus on the Danger Theory

A Review on Immune-Inspired Node Fault Detection in Wireless Sensor Networks with a Focus on the Danger Theory

Synergizing Machine Learning and Cybersecurity for Robust Digital Protection.

An Overview on Structural Health Monitoring: From the Current State-of-the-Art to New Bio-inspired Sensing Paradigms

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