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1 – 2 of 2State intervention has helped the production and export of food products to soar to record levels, with markets in Asia and the Middle East replacing those of Europe. These…
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DOI: 10.1108/OXAN-DB286145
ISSN: 2633-304X
Keywords
Geographic
Topical
Mythili Boopathi, Meena Chavan, Jeneetha Jebanazer J. and Sanjay Nakharu Prasad Kumar
The Denial of Service (DoS) attack is a category of intrusion that devours various services and resources of the organization by the dispersal of unusable traffic, so that…
Abstract
Purpose
The Denial of Service (DoS) attack is a category of intrusion that devours various services and resources of the organization by the dispersal of unusable traffic, so that reliable users are not capable of getting benefit from the services. In general, the DoS attackers preserve their independence by collaborating several victim machines and following authentic network traffic, which makes it more complex to detect the attack. Thus, these issues and demerits faced by existing DoS attack recognition schemes in cloud are specified as a major challenge to inventing a new attack recognition method.
Design/methodology/approach
This paper aims to detect DoS attack detection scheme, termed as sine cosine anti coronavirus optimization (SCACVO)-driven deep maxout network (DMN). The recorded log file is considered in this method for the attack detection process. Significant features are chosen based on Pearson correlation in the feature selection phase. The over sampling scheme is applied in the data augmentation phase, and then the attack detection is done using DMN. The DMN is trained by the SCACVO algorithm, which is formed by combining sine cosine optimization and anti-corona virus optimization techniques.
Findings
The SCACVO-based DMN offers maximum testing accuracy, true positive rate and true negative rate of 0.9412, 0.9541 and 0.9178, respectively.
Originality/value
The DoS attack detection using the proposed model is accurate and improves the effectiveness of the detection.
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