scholarly journals Cloud Load Estimation with Deep Logarithmic Network for Workload and Time Series Optimization

2021 ◽  
Vol 3 (3) ◽  
pp. 234-248
Author(s):  
N. Bhalaji
Keyword(s):  
Time Series ◽  
Cloud Computing ◽  
Deep Learning ◽  
Extreme Points ◽  
Learning Approach ◽  
Load Estimation ◽  
Learning Method ◽  
Network Computing ◽  
Noise Interference

In recent days, we face workload and time series issue in cloud computing. This leads to wastage of network, computing and resources. To overcome this issue we have used integrated deep learning approach in our proposed work. Accurate prediction of workload and resource allocation with time series enhances the performance of the network. Initially the standard deviation is reduced by applying logarithmic operation and then powerful filters are adopted to remove the extreme points and noise interference. Further the time series is predicted by integrated deep learning method. This method accurately predicts the workload and sequence of resource along with time series. Then the obtained data is standardized by a Min-Max scalar and the quality of the network is preserved by incorporating network model. Finally our proposed method is compared with other currently used methods and the results are obtained.

A deep learning approach for hydrological time-series prediction: A case study of Gilgit river basin

2020 ◽  
Vol 13 (3) ◽  
pp. 915-927 ◽  
Author(s):  
Dostdar Hussain ◽  
Tahir Hussain ◽  
Aftab Ahmed Khan ◽  
Syed Ali Asad Naqvi ◽  
Akhtar Jamil
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
River Basin ◽  
Time Series Prediction ◽  
Learning Approach

scholarly journals A Stacked Deep Learning Approach for IoT Cyberattack Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Bandar Alotaibi ◽  
Munif Alotaibi
Keyword(s):  
Deep Learning ◽  
Detection Method ◽  
Denial Of Service ◽  
Large Datasets ◽  
Learning Approach ◽  
Distributed Denial Of Service ◽  
Learning Method ◽  
Traffic Data ◽  
Security Breaches ◽  
Iot Devices

Internet of things (IoT) devices and applications are dramatically increasing worldwide, resulting in more cybersecurity challenges. Among these challenges are malicious activities that target IoT devices and cause serious damage, such as data leakage, phishing and spamming campaigns, distributed denial-of-service (DDoS) attacks, and security breaches. In this paper, a stacked deep learning method is proposed to detect malicious traffic data, particularly malicious attacks targeting IoT devices. The proposed stacked deep learning method is bundled with five pretrained residual networks (ResNets) to deeply learn the characteristics of the suspicious activities and distinguish them from normal traffic. Each pretrained ResNet model consists of 10 residual blocks. We used two large datasets to evaluate the performance of our detection method. We investigated two heterogeneous IoT environments to make our approach deployable in any IoT setting. Our proposed method has the ability to distinguish between benign and malicious traffic data and detect most IoT attacks. The experimental results show that our proposed stacked deep learning method can provide a higher detection rate in real time compared with existing classification techniques.

scholarly journals A deep learning approach for anomaly detection with industrial time series data: a refrigerators manufacturing case study

2019 ◽  
Vol 38 ◽  
pp. 233-240 ◽  
Author(s):  
Mattia Carletti ◽  
Chiara Masiero ◽  
Alessandro Beghi ◽  
Gian Antonio Susto
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Series Data ◽  
Learning Approach

scholarly journals SSIM—A Deep Learning Approach for Recovering Missing Time Series Sensor Data

2019 ◽  
Vol 6 (4) ◽  
pp. 6618-6628 ◽  
Author(s):  
Yi-Fan Zhang ◽  
Peter J. Thorburn ◽  
Wei Xiang ◽  
Peter Fitch
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Sensor Data ◽  
Learning Approach
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