Multi head self-attention gated graph convolutional network based multi‑attack intrusion detection in MANET

Reka, R; Karthick, R; Saravana Ram, R; Singh, Gurkirpal

doi:10.1016/j.cose.2023.103526

Computers & Security

2024

DOI: 10.1016/j.cose.2023.103526

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Multi head self-attention gated graph convolutional network based multi‑attack intrusion detection in MANET

R Reka,

R Karthick,

R Saravana Ram

et al.

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Cited by 29 publications

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An efficient cluster head selection in WSNs using transient search optimization (TSO) algorithm

Subramanian,

Muthusamy,

Kulandaivelu

et al. 2024

Int J Communication

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SummaryIn this manuscript, a nature‐inspired optimization method, named transient search optimization (TSO), is proposed. Energy‐based monetary custom is a serious issue on the wireless sensor network (WSN). Here, the network clustering is an effectual technique to reduce node energy depletion and increased network lifetime. The proposed method aims to improve the efficiency of sensor nodes (SNs) by reducing their detachment, minimizing energy transmission, and protecting excessive energy stored in the nodes. This approach helps decrease delays, reduce traffic flow, and optimize network performance. The execution is implemented on the NS2 software. The experimental outcomes exhibit that the proposed system performs better based on two wireless sensor architectures, such as 50 nodes and 100 nodes. The parameter produces 52.24%, 54.38%, and 56.37% better network lifetime; 44.71%, 46.24%, and 49.45% higher alive node; and 39.26%, 36.26%, and 28.65% lesser dead SNs compared with existing techniques like multi‐objective cluster head (CH)–based energy‐aware optimized routing approach in wireless sensor network (MOCH‐ORR‐WSN), energy effective CH selection with improved sparrow search algorithm in WSN (ECH‐ISS‐WSN), and energy effectual cluster basis routing protocol under butterfly optimization along ant colony optimization algorithms for WSN (EEC‐BOA‐ACO‐WSN).

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An efficient cluster head selection in WSNs using transient search optimization (TSO) algorithm

Subramanian,

Muthusamy,

Kulandaivelu

et al. 2024

Int J Communication

View full text Add to dashboard Cite

show abstract

An advancing method for web service reliability and scalability using ResNet convolution neural network optimized with Zebra Optimization Algorithm

Gokulakrishan,

Ramakrishnan,

Saritha

et al. 2024

Trans Emerging Tel Tech

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Web service reliability and scalability is an important mission that keeps web services running normally. Within web service, the web services invoked by users not only depend on the service itself, but also on web load condition. Due to the features of web dynamics, traditional reliability and scalability methods have become inappropriate; at the same time, the web condition parameter sparsity problem will cause inaccurate reliability prediction. To address these challenges, Web Service Reliability and Scalability Determination Using ResNet Convolutional Neural Network optimized with Zero Optimization Algorithm (WRS‐ResNetCNN‐ZOA) is proposed in this manuscript. Initially, the input data is collected from WSRec dataset. The ResNet convolutional neural network (ResNetCNN) with Business Process Execution Language (BPEL) specification is introduced to forecast the reliability and scalability of web service. The results are categorized as right and wrong based on ResNetCNN. The weight parameters of the ResNetCNN is optimized by Zebra Optimization Algorithm to improve accuracy of the prediction. The performance of the proposed method is examined under some performance metrics, like F‐measure, reliability, scalability, accuracy, sensitivity, specificity, and precision. The proposed technique attains 15.36%, 35.39%, 23.87%, 20.67% better reliability, 42.39%, 11.39%, 34.16%, 25.78% better accuracy when analyzed to the existing methods, like Web Reliability based on K‐clustering, (WRS‐KClustering), Web Reliability prediction based on AdaBoostM1 and J48 (WRS‐AdaM1‐J48), Web Reliability prediction based on Online service Reliability (WRS‐OPUN), and Web Reliability prediction based on Dynamic Bayesian Network (WRS‐DBNS), respectively.

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Machine learning solutions for mobile internet of things security: A literature review and research agenda

Messabih,

Kerrache,

Cheriguene

et al. 2024

Trans Emerging Tel Tech

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In recent years, the advancements in wireless technologies and sensor networks have promoted the Mobile Internet of Things (MIoT) paradigm. However, the unique characteristics of MIoT networks expose them to significant security vulnerabilities and threats, necessitating robust cybersecurity measures, including effective attack detection and mitigation techniques. Among these strategies, Artificial Intelligence (AI), and particularly Machine Learning‐ (ML) based approaches, emerge as a pivotal method for bolstering MIoT security. In this paper, we present a comprehensive literature survey regarding the utilization of ML for enhancing security in MIoT. Through an exhaustive review of existing research articles, we analyze the diverse array of ML‐based approaches employed to safeguard MIoT ecosystems and provide a holistic understanding of the current landscape, elucidating the strengths and limitations of prevailing methodologies. We propose a structured taxonomy to categorize recent works in this domain, by distinguishing approaches based on Shallow Supervised Learning (SSL), Shallow Unsupervised Learning (SUL), Deep Learning (DL), and Reinforcement Learning (RL). By delineating existing challenges and potential future directions for cybersecurity in MIoT, we aim to stimulate discourse and inspire novel approaches towards more resilient and secure MIoT ecosystems.

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Multi head self-attention gated graph convolutional network based multi‑attack intrusion detection in MANET

Cited by 29 publications

References 29 publications

An efficient cluster head selection in WSNs using transient search optimization (TSO) algorithm

An efficient cluster head selection in WSNs using transient search optimization (TSO) algorithm

An advancing method for web service reliability and scalability using ResNet convolution neural network optimized with Zebra Optimization Algorithm

Machine learning solutions for mobile internet of things security: A literature review and research agenda

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