A lightweight intrusion detection framework for wireless sensor networks

Hai, Tran Hoang; Huh, Eui‐Nam; Jo, Minho

doi:10.1002/wcm.785

Cited by 48 publications

(48 citation statements)

References 28 publications

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“…Yu et al [7] tried to secure clustering mechanism against wormhole attack in ad-hoc networks (communication between CHs) but after forming clusters, not during the election procedure of CHs. Hai et al [21] propose a lightweight intrusion detection framework integrated for clustered sensor networks by using an over-hearing mechanism to reduce the sending alert packets. Elhdhili et al [6] propose a reputation based clustering algorithm (RECA) that aims to elect trustworthy, stable and high energy cluster heads but during the election procedure, not after forming clusters.…”

Section: Related Workmentioning

confidence: 99%

Balanced and Safe Weighted Clustering Algorithm for Mobile Wireless Sensor Networks

Dahane

Berrached

Loukil

2015

IFIP Advances in Information and Communication Technology

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Section: Related Workmentioning

confidence: 99%

Balanced and Safe Weighted Clustering Algorithm for Mobile Wireless Sensor Networks

Dahane

Berrached

Loukil

2015

IFIP Advances in Information and Communication Technology

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“…They studied multiple types of attacks on routing protocols in detail and the effects on common routing protocols in WSNs. The assumption is that there are two types of attacks, outer attacks and inner attacks [1,2]. Other researchers have used a decentralized approach to monitor network nodes with fault detections through the coordination of neighboring nodes [44] or the use of watchdogs to detect misbehavior in neighbors [30,31,45,46].…”

Section: Security In Wsnsmentioning

confidence: 99%

“…In the following section, we review the most common network layer attacks on WSNs we selected and we highlight the characteristics of these attacks [1,2,43,48].…”

Section: Security In Wsnsmentioning

confidence: 99%

“…The laptop-class adversary may use higher computational resources and communication power than a legitimate node to advertise itself as the shortest path to the base-station or, in our case, to the CH. A CH aggregates the data of member nodes in a cluster and relays them to another CH or the sink node [1,2].…”

Section: Sinkholementioning

confidence: 99%

“…Practical work in laboratory to study WSNs is a very challenging task for students in computer science and electronics. A sensor is an electronic device with limited resources (processing, storage, battery power and bandwidth).Sensor nodes are randomly and densely deployed in a sensed environment [2,3]. If we exclude systems which are dedicated to wire line networks such as those presented in [1], no proposal of a virtual platform devoted to study the practical aspects of WSNs has been made up to now.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Virtual Laboratory to Practice Mobile Wireless Sensor Networks: A Case Study on Energy Efficient and Safe Weighted Clustering Algorithm

2015

J Inf Process Syst

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In this paper, we present a virtual laboratory platform (VLP) baptized Mercury allowing students to make practical work (PW) on different aspects of mobile wireless sensor networks (WSNs). Our choice of WSNs is motivated mainly by the use of real experiments needed in most courses about WSNs. These experiments require an expensive investment and a lot of nodes in the classroom. To illustrate our study, we propose a course related to energy efficient and safe weighted clustering algorithm. This algorithm which is coupled with suitable routing protocols, aims to maintain stable clustering structure, to prevent most routing attacks on sensor networks, to guaranty energy saving in order to extend the lifespan of the network. It also offers a better performance in terms of the number of re-affiliations. The platform presented here aims at showing the feasibility, the flexibility and the reduced cost of such a realization. We demonstrate the performance of the proposed algorithms that contribute to the familiarization of the learners in the field of WSNs.

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Developing an attack detection framework for wireless sensor network‐based healthcare applications using hybrid convolutional neural network

Subasini

Karuppiah

Sheeba

et al. 2021

Trans Emerging Tel Tech

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Attack detection is the major issue in healthcare‐based wireless sensor networks (H‐WSNs). Due to their low processing speed, very low storage space, poor attack detection rate, longer deployment time, poor communication range, and reduced energy, H‐WSNs are subjected to difficult implementation and have their own limitations. To tackle these issues, we have presented a hybrid deep learning model using convolutional neural network and long short term memory (HDMCL) for attack detection in H‐WSN. This research is divided into three steps: preprocessing, dimensionality reduction, and classification (attack detection). At first, the raw input data (patient's health data) is preprocessed using the one‐hot encoding method. Next, the modified Huber independent component analysis based squirrel search algorithm (MHICA‐SSA) effectively reduces the data dimensionality in which the MHICA‐SSA is the amalgamation of both modified Huber independent component analysis and squirrel search algorithm. The novel algorithm designed overcomes the complexities associated with existing techniques such as the curse of dimensionality problem, improves the parameter interpretation, minimizes the time and storage space, minimizes space complexity, and enhances the attack detection accuracy of the convolutional neural network‐based long short‐term memory (CNN‐LSTM). After that, the deep learning CNN‐LSTM model is utilized for normal, black hole, and gray hole attack detection. The NS2.34 network simulator implements the proposed work thereby the proposed work efficiency is validated using different performance measures such as packet delivery ratio, false alarm rate, network lifetime, energy consumption, throughput, attack detection time, and rate. The proposed work performance is evaluated using different existing methods such as fish swarm optimization based particle swarm optimization, intelligent opportunistic routing algorithm, Jensen‐Shannon divergence‐based independent component analysis, and radio frequency identification based WSN. The throughput of the proposed work is increased up to 3.2 times and the network lifetime is increased up to 4 times when compared to the existing techniques.

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A lightweight intrusion detection framework for wireless sensor networks

Cited by 48 publications

References 28 publications

Balanced and Safe Weighted Clustering Algorithm for Mobile Wireless Sensor Networks

Balanced and Safe Weighted Clustering Algorithm for Mobile Wireless Sensor Networks

A Virtual Laboratory to Practice Mobile Wireless Sensor Networks: A Case Study on Energy Efficient and Safe Weighted Clustering Algorithm

Developing an attack detection framework for wireless sensor network‐based healthcare applications using hybrid convolutional neural network

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