.png){kind=icon}
Return
Optimization Enabled Deep Learning-Based DDoS Attack Detection in Cloud Computing
.png){kind=icon}
OA
AI
DOI：10.1155/2023/2039217.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Cloud computing is a vast revolution in information technology (IT) that inhibits scalable and virtualized sources to end users with low infrastructure cost and maintenance. They also have much flexibility and these resources are supervised by various management organizations and provided over the Internet by known standards, formats, and networking protocols. Legacy protocols and underlying technologies consist of vulnerabilities and bugs which open doors for intrusion by network attackers. Attacks as distributed denial of service (DDoS) are one of most frequent attacks, which impose heavy damage and affect performance of the cloud. In this research work, DDoS attack detection is easily identified in an optimized way through a novel algorithm, namely, the proposed gradient hybrid leader optimization (GHLBO) algorithm. This optimized algorithm is responsible to train a deep stacked autoencoder (DSA) that detects the attack in an efficient manner. Here, fusion of features is carried out by deep maxout network (DMN) with an overlap coefficient, and augmentation of data is carried out by the oversampling process. Furthermore, the proposed GHLBO is generated by integrating the gradient descent and hybrid leader-based optimization (HLBO) algorithm. Also, this proposed method is assessed by various performance metrics, such as the true positive rate (TPR), true negative rate (TNR), and testing accuracy with values attained as 0.909, 0.909, and 0.917, accordingly.
Journal
IF:
3.7
Papers:
3.1K
Citations:
8.1K
Organization
Cited Papers
Improving deep neural networks with multi-layer maxout networks and a novel initialization method.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
ShareSaveEvaluation of maxout activations in deep learning across several big data domainsJOURNAL OF BIG DATA
OAAI
JOURNAL OF BIG DATAIF6.4
OAAIShareSaveShareSaveShareSaveShareSaveAssessment of zinc concentrations in surface sediment from urban and industrial sites of Umeda River, JapanWater SupplyOAAI
Water SupplyIF0
OAAIShareSaveAn Ensemble Deep Learning-Based Cyber-Attack Detection in Industrial Control SystemIEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAIShareSave
View more