A Study on the Adaptability of Immune models for Wireless Sensor Network Security

Alaparthy, Vishwa Teja; Amouri, Amar; Morgera, S.D.

doi:10.1016/j.procs.2018.11.003

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…As summarized in [ 147 ], the original CLONALG has a relatively high FAR and is not able to cope with dynamic environments. It is impracticable for dense environments making it not suitable for WSN applications.…”

Section: Classical Ais Theories and Their Applicationsmentioning

confidence: 99%

“…The reason is mainly scaling problems of the NSA that are even worse when being applied to real network traffic [ 126 ]. Secondly, the danger theory’s distributed and simple concept is suitable for most WSN applications [ 147 ]. Therefore, we will give a brief overview of the application of immune-inspired techniques for computational problems in general and specific to their use in WSNs.…”

Section: Literature Review On Immune-inspired Approachesmentioning

confidence: 99%

“…The reason for this is the usually high memory consumption and comparably high false positives rate of most negative selection approaches. Both disqualify negative selection approaches, especially from a meaningful use in resource-constrained systems such as WSNs [ 147 ].…”

Section: Open Problems and Research Directionsmentioning

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%

Section: Literature Review On Immune-inspired Approachesmentioning

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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“…A considerable number of hybrid schemes [11] are also employed, which proved more effective than the conventional models. In addition, there has been an alternate field of study [12], which take the human immune system (HIS) as an inspiration and derives an IDS for IoT networks. IDSs engineered [13,14] from HIS are commonly based on three different immune theories namely danger theory, negative selection, and clonal selection.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Based Intrusion Detection System for Mobile Internet of Things

Amouri

Alaparthy

Morgera

2020

Sensors

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Intrusion detection systems plays a pivotal role in detecting malicious activities that denigrate the performance of the network. Mobile adhoc networks (MANETs) and wireless sensor networks (WSNs) are a form of wireless network that can transfer data without any need of infrastructure for their operation. A more novel paradigm of networking, namely Internet of Things (IoT) has emerged recently which can be considered as a superset to the afore mentioned paradigms. Their distributed nature and the limited resources available, present a considerable challenge for providing security to these networks. The need for an intrusion detection system (IDS) that can acclimate with such challenges is of extreme significance. Previously, we proposed a cross layer-based IDS with two layers of detection. It uses a heuristic approach which is based on the variability of the correctly classified instances (CCIs), which we refer to as the accumulated measure of fluctuation (AMoF). The current, proposed IDS is composed of two stages; stage one collects data through dedicated sniffers (DSs) and generates the CCI which is sent in a periodic fashion to the super node (SN), and in stage two the SN performs the linear regression process for the collected CCIs from different DSs in order to differentiate the benign from the malicious nodes. In this work, the detection characterization is presented for different extreme scenarios in the network, pertaining to the power level and node velocity for two different mobility models: Random way point (RWP), and Gauss Markov (GM). Malicious activity used in the work are the blackhole and the distributed denial of service (DDoS) attacks. Detection rates are in excess of 98% for high power/node velocity scenarios while they drop to around 90% for low power/node velocity scenarios.

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Multicriteria Approach to Control a Population of Robots to Find the Best Solutions

Misyurin

Nelyubin

Потапов

2019

Advances in Intelligent Systems and Computing

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A Study on the Adaptability of Immune models for Wireless Sensor Network Security

Cited by 8 publications

References 12 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

A Machine Learning Based Intrusion Detection System for Mobile Internet of Things

Multicriteria Approach to Control a Population of Robots to Find the Best Solutions

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