DDoS Attack Detection in Cloud Computing Using Optimized Elman Neural Network Based on Bacterial Colony Optimization and Centroid Opposition-Based Learning DOI Open Access

S. Kalvikkarasi,

A. Saraswathi

International Journal of Computer Networks And Applications, Год журнала: 2024, Номер 11(6), С. 835 - 854

Опубликована: Дек. 30, 2024

Cloud computing infrastructures are particularly vulnerable to Distributed Denial of Service (DDoS) attacks due the large-scale and dynamic nature resources.Large data volumes handled by cloud settings, which raises computational cost detection, filtering malicious traffic from genuine in such large quantities is difficult.The conventional detection techniques insufficient.The optimized Elman Neural Network (ENN) used this study's proposed enhanced DDoS attack framework combines centroid opposition-based learning (COBL) with bacterial colony optimization (BCO) called COBCO.The BCO lacks population diversity can fall into local optima random initialization update.To overcome above issues, COBL for update enhance avoid issues.By imitating foraging behavior, COBCO algorithm improves ENN's capacity explore exploit solution space, increasing network's speed convergence accuracy detection.Meanwhile, enhances process producing a wider range solid candidate solutions, offset drawbacks learning.Extensive simulations show that suggested strategy outperforms traditional identifying different kinds attacks.

Язык: Английский

Trustworthy and efficient project scheduling in IIoT based on smart contracts and edge computing DOI Creative Commons
Peng Liu, Xinglong Wu,

Yanjun Peng

и другие.

Journal of Cloud Computing Advances Systems and Applications, Год журнала: 2025, Номер 14(1)

Опубликована: Янв. 11, 2025

To facilitate flexible manufacturing, modern industries have incorporated numerous modular operations such as multi-robot services which can be expediently arranged or offloaded to other production resources. However, complex manufacturing projects often consist of multiple tasks with fixed sequences, posing a significant challenge for smart factories in efficiently scheduling limited robot resources complete specific tasks. Additionally, when span across factories, ensuring faithful execution contracts becomes another challenge. In this paper, we propose modified combinatorial auction method combined blockchain and edge computing technologies organize project scheduling. Firstly, transform efficient resource into resource-constrained multi-project problem (RCPSP). Subsequently, the solution integrates random sampling (CA-RS) contracts. Alongside security analysis, simulations are conducted using real data sets. The results indicate that suggested CA-RS approach significantly enhances efficiency arrangement within industrial Internet Things compared baseline algorithms.

Язык: Английский

Процитировано

3
CryptoHHO: a bio-inspired cryptosystem for data security in Fog–Cloud architecture DOI
Md Saquib Jawed, Mohammad Sajid

The Journal of Supercomputing, Год журнала: 2024, Номер 80(11), С. 15834 - 15867

Опубликована: Апрель 6, 2024

Язык: Английский

Процитировано

4
Security, Reliability, Cost, and Energy-aware Scheduling of Real-Time Workflows in Compute-Continuum Environments DOI
Ahmad Taghinezhad-Niar, Javid Taheri

IEEE Transactions on Cloud Computing, Год журнала: 2024, Номер 12(3), С. 954 - 965

Опубликована: Июль 1, 2024

Язык: Английский

Процитировано

4
A systematic review of various load balancing approaches in cloud computing utilizing machine learning and deep learning DOI

Sonia Sonia,

Rajender Nath

International Journal of Data Science and Analytics, Год журнала: 2025, Номер unknown

Опубликована: Янв. 27, 2025

Язык: Английский

Процитировано

0
Reinforcement learning based Secure edge enabled multi task scheduling model for internet of everything applications DOI Creative Commons

Thiruppathy Kesavan,

R. Venkatesan,

Wai Kit Wong

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 20, 2025

The fast growth of the Internet Everything (IoE) has resulted in an exponential rise network data, increasing demand for distributed computing. Data collection and management with job scheduling using wireless sensor networks are considered essential requirements IoE environment; however, security issues over data on online platform energy consumption must be addressed. Secure Edge Enabled Multi-Task Scheduling (SEE-MTS) model been suggested to properly allocate jobs across machines while considering availability relevant copies. proposed approach leverages edge computing enhance efficiency applications, addressing growing need manage huge generated by devices. system ensures user protection through dynamic updates, multi-key search generation, encryption, verification result accuracy. A MTS mechanism is employed optimize usage, which allocates slots various processing tasks. Energy assessed tasks queues, preventing node overloading minimizing disruptions. Additionally, reinforcement learning techniques applied reduce overall task completion time minimal data. Efficiency have improved due reduced energy, delay, reaction, times. Results indicate that SEE-MTS achieves utilization 4 J, a delay 2s, reaction 4s, at 89%, level 96%. With computation 6s, offers security, reducing times, although real-world implementation may limited number devices incoming

Язык: Английский

Процитировано

0
Data center multidimensional management strategy based on descending neighborhood DBSCAN algorithm in unsupervised learning DOI
Bin Liang, Junqing Bai

Journal of Industrial Information Integration, Год журнала: 2025, Номер unknown, С. 100830 - 100830

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Adaptive container auto-scaling for fluctuating workloads in cloud DOI
Xiaoyue Feng, Sijia Zhang,

Tianzhe Jiao

и другие.

Future Generation Computer Systems, Год журнала: 2025, Номер unknown, С. 107872 - 107872

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
LSDMA: Levelized Security Driven Deadline Constrained Multiple Workflow Allocation Model in Cloud Computing DOI
Mahfooz Alam, Mohammad Shahid, Suhel Mustajab

и другие.

Future Generation Computer Systems, Год журнала: 2025, Номер unknown, С. 107941 - 107941

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0
Military Computing Security: Insights and Implications DOI

Kavita Sahu,

Rajeev Kumar, Rakesh Srivastava

и другие.

Journal of The Institution of Engineers (India) Series B, Год журнала: 2024, Номер unknown

Опубликована: Авг. 21, 2024

Язык: Английский

Процитировано

2
Efficient Hybrid DDPG task scheduler for HPC and HTC in cloud environment DOI Creative Commons

Sudheer Mangalampalli,

Ganesh Reddy Karri, Sachi Nandan Mohanty

и другие.

IEEE Access, Год журнала: 2024, Номер 12, С. 108897 - 108920

Опубликована: Янв. 1, 2024

Task Scheduling is a crucial challenge in cloud computing as diversified tasks come rapidly onto console dynamically from heterogeneous resources which consists of different task lengths, processing capacities. Generating schedules for these type Cloud Service Provider(CSP).Therefore, to generate paradigm effectively by considering arising and match it with respective Virtual Machine (VM), scheduler formulated using Deep Deterministic Policy Gradient (DDPG) algorithm used methodology design scheduler. This works three stages. In the initial stage, are classified based on length capacity identify them whether they High Performance Computing (HPC) or Throughput (HTC) tasks. After classification, second be tracked matches corresponding nature Finally, third according VM priorities calculated electricity unit cost mapped VMs. Simulations conducted Cloudsim fabricated workload distributions realtime worklogs. our proposed Hybrid scheduler(HDDPGTS) evaluated over DQN, A2C algorithms. From results, proved that HDDPGTS significantly improved makespan, Energy consumption, scheduling overhead, scalability baseline approaches.

Язык: Английский

Процитировано

0