Scaling of wireless sensor network intrusion detection probability: 3D sensors, 3D intruders, and 3D environments

Said, Omar; Elnashar, Alaa Ismail

doi:10.1186/s13638-015-0240-6

Cited by 10 publications

(3 citation statements)

References 26 publications

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“…The results were obtained using uniform technique. It also has been seen that there is minimal execution in case of chi-square and beta WSNs [13].…”

Section: Literature Reviewmentioning

confidence: 99%

Node Validation Technique to Increase Security of Wireless Sensor Networks

2019

IJITEE

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A distributed sort of network in which sensor nodes can join or leave the network when they want is identified as wireless sensor network. Due to self-arranging of the network, attacker nodes make their entry inside networks and launch different types of active and passive intrusions. The active attacks can be divided into various sub categories and one of them is misdirection attack. This attack increases delay in the network. The available attacker hub will trigger attack. In order to recognize and disengage malicious nodes a novel strategy is proposed in this work. The malicious nodes are recognized from the networks which are in charge of triggering the node. The attacker or malevolent node launches sinkhole intrusion. This intrusion streams fake recognition information within the network. This study proposes a verification approach for detecting attacker nodes present in the network. The performance of introduced approach is tested in NS2. It is scrutinized that performance is improved as per various parameters.

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“…The results were obtained using uniform technique. It also has been seen that there is minimal execution in case of chi-square and beta WSNs [13].…”

Section: Literature Reviewmentioning

confidence: 99%

Node Validation Technique to Increase Security of Wireless Sensor Networks

2019

IJITEE

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“…WSN comprises smaller important components, which have restricted power devices, say sensors, and it can be installed in open surroundings [1]. Due to its wireless environment, it is employed in most unguarded surroundings wherein any sort of adversaries may take note of the traffic and insert their own nodes on the sensor network [2], which is called as attack injection.…”

Section: Introductionmentioning

confidence: 99%

A Quasi-Newton Neural Network Based Efficient Intrusion Detection System for Wireless Sensor Network

Gautami¹,

Shanthini²,

Karthik³

2023

Computer Systems Science and Engineering

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In Wireless Sensor Networks (WSN), attacks mostly aim in limiting or eliminating the capability of the network to do its normal function. Detecting this misbehaviour is a demanding issue. And so far the prevailing research methods show poor performance. AQN3 centred efficient Intrusion Detection Systems (IDS) is proposed in WSN to ameliorate the performance. The proposed system encompasses Data Gathering (DG) in WSN as well as Intrusion Detection (ID) phases. In DG, the Sensor Nodes (SN) is formed as clusters in the WSN and the Distance-based Fruit Fly Fuzzy c-means (DFFF) algorithm chooses the Cluster Head (CH). Then, the data is amassed by the discovered path. Next, it is tested with the trained IDS. The IDS encompasses '3' steps: pre-processing, matrix reduction, and classification. In pre-processing, the data is organized in a clear format. Then, attributes are presented on the matrix format and the ELDA (entropybased linear discriminant analysis) lessens the matrix values. Next, the output as of the matrix reduction is inputted to the QN3 classifier, which classifies the denial-of-services (DoS), Remotes to Local (R2L), Users to Root (U2R), and probes into attacked or Normal data. In an experimental estimation, the proposed algorithm's performance is contrasted with the prevailing algorithms. The proposed work attains an enhanced outcome than the prevailing methods.Keywords: Distance fruit fly fuzzy c-means (DFFF); entropy-based linear discriminant analysis (ELDA); Quasi-Newton neural network (QN3); remote to local (R2L); denial of service (DoS); user to root (U2R)

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“…Energy cost finds the best route to destination as regards energy conservation. Network life time is the total working time of WSN until it becomes unable to satisfy user's needs [7][8][9][10][11][12]. To face these challenges, a powerful management system for WSN should be constructed provided that this system considers the critical parameters such as WSN node power degree, WSN bandwidth, and WSN instrumentations.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of WSN management system for QoS guarantee

Said

2015

J Wireless Com Network

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As the wireless sensor networks (WSNs) continue to evolve, it becomes more and more important. Furthermore, no one can deny that quality of services (QoS) is still one of the most important areas in this domain. In this paper, a system for guaranteeing WSN QoS is proposed. The idea of this system is to use the previous individual solutions such as clustering, data prioritization, and data classification in addition to efficient techniques for network management. Simulation of this system is achieved using the network simulation package (NS2). Simulation results are given to demonstrate the effectiveness of the proposed system in terms of throughput, latency, lost packets, and sensor power consumption.

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Scaling of wireless sensor network intrusion detection probability: 3D sensors, 3D intruders, and 3D environments

Cited by 10 publications

References 26 publications

Node Validation Technique to Increase Security of Wireless Sensor Networks

Node Validation Technique to Increase Security of Wireless Sensor Networks

A Quasi-Newton Neural Network Based Efficient Intrusion Detection System for Wireless Sensor Network

Performance evaluation of WSN management system for QoS guarantee

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