Spam detection in IoT based on hybrid deep learning model and multi-objective optimization by NSGA II DOI Creative Commons

Samira Dehghani,

Mohammad Ahmadinia,

Seyed Hamid Ghafoori

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: May 10, 2024

Abstract The Internet of Things (IoT) connects a range things, including sensors, physical devices, controllers, and intelligent computer processors. Physical objects with the ability to organize control independently are referred as smart devices in IoT architecture. interconnected nature within these networks makes them susceptible various cyber threats, spam posing significant risk. Thus, significance effective detection networks, especially context grids, lies safeguarding reliability, security, optimal functionality critical infrastructure systems essential for our modern way life. Existing methods have often overlooked aspects extracting hidden dependencies addressing imbalanced inherent data, limiting their effectiveness ensuring comprehensive security measures. In this study, bidirectional gated recurrent unit (BiGRU) Convolution neural network (CNN) combined Non-dominated Sorting Genetic Algorithm- II (NSGA II) multi-objective optimization method effectively detect IoT. novelty study combines deep learning models through simultaneously capture spatial temporal dependencies, challenge data Our excels over baseline previous approaches detection, leveraging real adeptly address imbalances resulting heightened accuracy reliability system.

Language: Английский

A two-tier multi-objective service placement in container-based fog-cloud computing platforms DOI
Javad Dogani,

Ali Pour Yazdanpanah,

A R Mousavi Zare

et al.

Cluster Computing, Journal Year: 2023, Volume and Issue: 27(4), P. 4491 - 4514

Published: Nov. 28, 2023

Language: Английский

Citations

2
Efficient Auto‐scaling for Host Load Prediction through VM migration in Cloud DOI
Shveta Verma, Anju Bala

Concurrency and Computation Practice and Experience, Journal Year: 2023, Volume and Issue: 36(4)

Published: Oct. 6, 2023

Summary The expeditious deployment of Cloud applications and services on wide‐ranging Data Centres (CDC) gives rise to the utilization many resources. Moreover, by increase in resource utilization, virtualization also greatly impacts achieving desired performance. major challenges are detecting over‐utilized or under‐utilized hosts at right time proper scaling Virtual Machines (VM) accurate host. Auto‐scaling Computing allows service providers scale up down resources automatically provides on‐demand computing power storage capacities. Effective autonomous eventually reduce load, energy consumption, operating costs. In this paper, an efficient auto‐scaling approach for predicting host load through VM migration has been proposed. ensemble method using different time‐series forecasting models proposed forecast approaching workload Based predicted algorithms have devised detect VMs can be migrated. designed validated experimentation a real‐time Google cluster dataset. technique significantly improves average CPU reduces over‐utilization under‐utilization. It minimizes response time, level agreement violations, slighter number migrations overhead.

Language: Английский

Citations

1
A Review on Machine Learning Methods for Workload Prediction in Cloud Computing DOI

Mohammad Yekta,

Hadi Shahriar Shahhoseini

Published: Nov. 1, 2023

Workload prediction is one of the critical parts resource provisioning in cloud computing and its evolved branches such as serverless edge computing. Effective stands a crucial element within realm edge-cloud Accurate workloads essential for effective allocation resources. plays role enhancing efficiency, reducing costs, optimizing performance, maintaining high level quality service, minimizing energy consumption. In this paper, we conduct comprehensive review state-of-the-art Machine Learning (ML) Deep (DL) algorithms employed workload other similar platforms We compared selected papers terms utilized methods techniques, predicted factors, accuracy metrics, dataset. Additionally, to facilitate usability comparison, articles sharing advantages disadvantages are organized into table. Finally, paper concludes by addressing current challenges future research directions.

Language: Английский

Citations

1
Multivariate Workload Aware Correlation Model for Container Workload Prediction DOI
Man Zhang, Chunyan An,

CongHao Yang

et al.

Published: Dec. 17, 2023

Language: Английский

Citations

1
Spam detection in IoT based on hybrid deep learning model and multi-objective optimization by NSGA II DOI Creative Commons

Samira Dehghani,

Mohammad Ahmadinia,

Seyed Hamid Ghafoori

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: May 10, 2024

Abstract The Internet of Things (IoT) connects a range things, including sensors, physical devices, controllers, and intelligent computer processors. Physical objects with the ability to organize control independently are referred as smart devices in IoT architecture. interconnected nature within these networks makes them susceptible various cyber threats, spam posing significant risk. Thus, significance effective detection networks, especially context grids, lies safeguarding reliability, security, optimal functionality critical infrastructure systems essential for our modern way life. Existing methods have often overlooked aspects extracting hidden dependencies addressing imbalanced inherent data, limiting their effectiveness ensuring comprehensive security measures. In this study, bidirectional gated recurrent unit (BiGRU) Convolution neural network (CNN) combined Non-dominated Sorting Genetic Algorithm- II (NSGA II) multi-objective optimization method effectively detect IoT. novelty study combines deep learning models through simultaneously capture spatial temporal dependencies, challenge data Our excels over baseline previous approaches detection, leveraging real adeptly address imbalances resulting heightened accuracy reliability system.

Language: Английский

Citations

0