An optimal cluster &amp; trusted path for routing formation and classification of intrusion using the machine learning classification approach in WSN

Srividya, P.; Devi, L. Nirmala

doi:10.1016/j.gltp.2022.03.018

Cited by 12 publications

(16 citation statements)

References 21 publications

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“…Here, the DA and FAR metrics focus on attack detection whereas the routing can be analyzed by AEU and PLR metrics. The compared models are TALAD, 21 RBP‐DT, 20 and SVM 22 . The performance of these models is assessed by varying the number of nodes from 50 to 300.…”

Section: Resultsmentioning

confidence: 99%

“…The following section enumerates the numerous existing intrusion detection system and routing protocols for IWSN. In Reference 20, a new recursive binary partitioning decision tree (RBP‐DT) model has been presented to identify and classify the DoS attack in the IWSN environment. Initially, the cluster establishment is invoked by applying particle swarm optimization.…”

Section: Literature Surveymentioning

confidence: 99%

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An artificial intelligence approach for energy‐aware intrusion detection and secure routing in internet of things‐enabled wireless sensor networks

Aruchamy,

Gnanaselvi,

Sowndarya

et al. 2023

Concurrency and Computation

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SummaryIn today's world, the Internet of things (IoT) and wireless sensor networks (WSNs) have been integrated into various applications to support reliable communication systems. The IoT nodes collect the sensed data with the aid of WSN and transfer them to the cloud environment. Nevertheless, these nodes become vulnerable when Denial of Service attacks occurred in the IoT‐enabled WSN (IWSN). The insecure route also affects the energy efficiency of IWSN. Therefore, robust intrusion detection along with secure routing is necessitated to protect the IWSN from various security threats. In this paper, a novel Artificial Intelligence‐based Energy‐aware Intrusion Detection and Secure Routing model has been proposed to develop a secured IWSN. Initially, the proposed model executes the intrusion detection system to identify the various attacks. Afterward, a game strategy‐based decision mechanism is integrated with the proposed intrusion detection model to decide whether protection is needed or not. In the last phase, an energy‐aware ad‐hoc on‐demand distance vector algorithm has been established to offer secured routing among multiple nodes. The performance results show that the proposed model achieves superior detection accuracy of 95% and reduced energy utilization of 38% as compared with the existing intrusion detection models.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

An artificial intelligence approach for energy‐aware intrusion detection and secure routing in internet of things‐enabled wireless sensor networks

Aruchamy,

Gnanaselvi,

Sowndarya

et al. 2023

Concurrency and Computation

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“…Srividya and Devi [13] the author introduced a strategy named ECPSO-PCM which concentrates on PSO optimization, clustering and trust management. The residual energy and spatial correlation-based clustering is used then trust based CH selection is introduced.…”

Section: Comprehensive Survey 21 Trust Oriented Wireless Sensor Networkmentioning

confidence: 99%

Multi level trust calculation with improved ant colony optimization for improving quality of service in wireless sensor network

Ahmed

Abbas

Rashid

2023

IJ-AI

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Wireless sensor network (WSN) is the most integral parts of current technology which are used for the real time applications. The major drawbacks in currect technologies are threads due to the creation of false trust values and data congestion. Maximum of the concept of WSNs primarily needs security and optimization. So, we are in the desire to develop a new model which is highly secured and localized. In this paper, we introduced a novel approach namely multi level trust calculation with improved ant colony optimization (MLT-IACO). This approach mainly sub-divided into two sections they are multi level trust calculation which is the combination three levels of trust such as direct trust, indirect trust and random repeat trust. Secondly, improved ant colony optimization technique is used to find the optimal path in the network. By transmitting the data in the optimal path, the congestion and delay of the network is reduced which leads to increase the efficiency. The outcome values are comparatively analyzed based the parameters such as packet delivery ratio, network throughput and average latency. While compared with the earlier research our MLT-IACO approach produce high packet delivery ratio and throughput as well as lower latency and routing overhead.

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“…Step 7: Confined exploitation In this step, confined exploitation is enunciated with the subsequent Equation (33),…”

Section: Ideal Path Assortmentmentioning

confidence: 99%

“…35 The hierarchical protocols present routing creation by multi-hop; the n number has cluster for its own cluster head to the network. 33 Long-distance data transmission requires considerable energy consumption due to performance deterioration, whereas tiny-distance data transmission uses less power. The objectives for optimization clustering, power consumption, routing, lifespan, calibration, throughput, quality of services, connectivity, and coverage.…”

mentioning

confidence: 99%

Self‐attention based generative adversarial network with Aquila optimization algorithm espoused energy aware cluster head selection in WSN

Soundararajan,

Tapas Bapu,

Sargunavathi

et al. 2023

Int J Communication

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SummaryWSNs have a wide range of applications, and the effective Wireless Sensor Network (WSN) design includes the best energy optimization techniques. The nodes in wireless sensor networks run on batteries. The existing cluster head selection methods do not take into account the latency and rate of wireless network traffic when optimizing the node's energy constraints. To overcome these issues, a self‐attention based generative adversarial network (SabGAN) with Aquila Optimization Algorithm (AqOA) is proposed for Multi‐Objective Cluster Head Selection and Energy Aware Routing (SabGAN‐AqOA‐EgAwR‐WSN) for secured data transmission in wireless sensor network. The proposed method implements the routing process through cluster head. SabGAN classifiers are utilized to select the CH based on firm fitness functions, including delay, detachment, energy, cluster density, and traffic rate. After the selection of the cluster head, the malicious node gains access to the cluster. Therefore, the ideal path selection is carried out by three parameters: trust, connectivity, and degree of amenity. These three parameters are optimized under proposed AqOA. The data are transferred to the base station with the support of optimum trust path. The proposed SabGAN‐AqOA‐EgAwR‐WSN method is activated in NS2 simulator. Finally, the proposed SabGAN‐AqOA‐EgAwR‐WSN method attains 12.5%, 32.5%, 59.5%, and 32.65% higher alive nodes; 85.71%, 81.25%, 82.63%, and 71.96% lower delay; and 52.25%, 61.65%, 37.83%, and 20.63% higher normalized network energy compared with the existing methods.

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An optimal cluster & trusted path for routing formation and classification of intrusion using the machine learning classification approach in WSN

Cited by 12 publications

References 21 publications

An artificial intelligence approach for energy‐aware intrusion detection and secure routing in internet of things‐enabled wireless sensor networks

An artificial intelligence approach for energy‐aware intrusion detection and secure routing in internet of things‐enabled wireless sensor networks

Multi level trust calculation with improved ant colony optimization for improving quality of service in wireless sensor network

Self‐attention based generative adversarial network with Aquila optimization algorithm espoused energy aware cluster head selection in WSN

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