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Arvindhan, M.
- Clustering Algorithm Networks Test Cost Sensitive for Specialist Divisions
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Authors
Affiliations
1 School of Computing Science and Engineering, Galgotias University, IN
2 Department of Computer Science and Engineering, Malla Reddy Institute of Technology and Science, IN
1 School of Computing Science and Engineering, Galgotias University, IN
2 Department of Computer Science and Engineering, Malla Reddy Institute of Technology and Science, IN
Source
ICTACT Journal on Image and Video Processing, Vol 10, No 2 (2019), Pagination: 2098-2102Abstract
It has been proven that deeper Convolutional Neural Networks (CNN) can result in better accuracy in many problems, but this accuracy comes with a high computational cost. Also, input instances have not the same difficulty. As a solution for accuracy vs. computational cost dilemma, we introduce a new test-cost-sensitive method for convolution neural networks. This method trains a CNN with a set Based on the difficulty of the input instance, the expert branches decide to use a shallower part of the network or go deeper to the end. The expert branches learn to determine: is the current network prediction wrong and if the instance passed to deeper network layers it will generate the right output; if not, then the expert branches will stop the process of computation. The experimental results on the standard CIFAR-10 dataset indicate that in comparison with basic models, the proposed method can train models with lower test cost and competitive accuracy.Keywords
Test-Cost-Sensitive Learning, Deep Learning, CNN with Expert Branches, Instance-Based Cost.
References
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- Data Mining Approach and Security Over DDOS Attacks
Abstract Views :148 |
PDF Views:0
Authors
Affiliations
1 School of Computing Science and Engineering, Galgotias University, IN
2 Department of Computer Science, Gambella University, IN
1 School of Computing Science and Engineering, Galgotias University, IN
2 Department of Computer Science, Gambella University, IN
Source
ICTACT Journal on Soft Computing, Vol 10, No 2 (2020), Pagination: 2061-2065Abstract
The benefit of on-demand services is one of the most important benefits of using cloud computing; therefore, the payment method in the cloud environment is pay per use. This feature results in a new type of DDOS attack called Economic Denial of Sustainability (EDoS), where as a result of the attack the customer pays the cloud provider extra. Similar to other DDoS attacks, EDoS attacks are divided into different groups, such as bandwidth-consuming attacks, specific target attacks, and connections-layer-exhaustion attacks. In this study, we propose a novel system for detecting different types of EDoS attacks by developing a pro le that learns from normal and abnormal behaviors and classifies them. In this sense, the extra demanding resources are allocated only to VMs that are found to be in a normal situation and thus prevent attack and resource dissemination from the cloud environment.Keywords
DDoS Attacks, EDoS Attacks, Cloud Computing, Machine Learning Detection.References
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