GARCH and ANN-based DDoS detection and filtering in cloud computing environment

Wang

et al. 2019

IJHPCN

In the wake of the development in science and technology, cloud computing has obtained more attention in different field. Meanwhile, outlier detection for data mining in cloud computing is playing significant role in different research domains and massive research works have devoted to outlier detection. However, the existing available methods spend high computation time. Therefore, the improved algorithm of outlier detection, which has higher performance to detect outlier, is presented. In this paper, the proposed method, which is an improved spectral clustering algorithm (SKM++), is fit for handling outliers. Then, pruning data can reduce computational complexity and combine distance-based method Manhattan distance (dist m) to obtain outlier score. Finally, the method confirms the outlier by extreme analysis. This paper validates the presented method by experiments with a real collected data by sensors and comparison against the existing approaches, the experimental results turn out that our proposed method precedes the existing.

Section: Related Workmentioning

confidence: 99%

Outlier detection of time series with a novel hybrid method in cloud computing

Wang

et al. 2019

IJHPCN

A survey of rainfall forecasting using artificial neural network

Zou

et al. 2018

IJES

Rainfall has a great impact on agriculture and people's daily travel, so accurate prediction of precipitation is well worth studying for researchers. Traditional methods like numerical weather prediction (NWP) models or statistical models can't provide satisfied effect of rainfall forecasting because of nonlinear and dynamic characteristics of precipitation. However, artificial neural network (ANN) has an ability to obtain complicated nonlinear relationship between variables, which is suitable to predict precipitation. This paper mainly introduces background knowledge of ANN and several algorithms using neural network applied to precipitation prediction in recent years. It is proved that neural network can greatly improve the accuracy and efficiency of prediction.

International Journal of Cloud Applications and Computing

A Heuristic-Based Task Scheduling Policy for QoS Improvement in Cloud

Tripathi

Kumar

2022

Cloud computing is a big step in the parallel and distributed computing that offers pervasive access to the entire stack of computing resources located in the data center via the Internet in a virtualized manner. QoS is an important research direction in the cloud and is a collection of constraints that meets the service level agreement (SLA) between the users and service providers. These constraints are waiting time, completion time, response time, makespan, resource utilization, effective utilization of bandwidth, and load balancing. This work presents a distribution plan for transferring task loads to different virtual machines (VMs) with an efficient load balancing mechanism that is best suited in heterogeneous environments and improve the QoS parameter of users and cloud vendors simultaneously. In this paper, the expected uniform load of tasks that can be mapped to a particular VMs is calculated and after that, the optimal average completion time (OACT) of expected uniform tasks load to each VM is calculated.