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Machine Automation Making Cyber-Policy Violator More Resilient: A Proportionate Study

Machine Automation Making Cyber-Policy Violator More Resilient: A Proportionate Study

Gyana Ranjana Panigrahi, Nalini Kanta Barpanda, Madhumita Panda
Copyright: © 2021 |Pages: 17
ISBN13: 9781799866596|ISBN10: 1799866599|ISBN13 Softcover: 9781799866602|EISBN13: 9781799866619
DOI: 10.4018/978-1-7998-6659-6.ch018
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MLA

Panigrahi, Gyana Ranjana, et al. "Machine Automation Making Cyber-Policy Violator More Resilient: A Proportionate Study." Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science, edited by Mrutyunjaya Panda and Harekrishna Misra, IGI Global, 2021, pp. 329-345. https://doi.org/10.4018/978-1-7998-6659-6.ch018

APA

Panigrahi, G. R., Barpanda, N. K., & Panda, M. (2021). Machine Automation Making Cyber-Policy Violator More Resilient: A Proportionate Study. In M. Panda & H. Misra (Eds.), Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science (pp. 329-345). IGI Global. https://doi.org/10.4018/978-1-7998-6659-6.ch018

Chicago

Panigrahi, Gyana Ranjana, Nalini Kanta Barpanda, and Madhumita Panda. "Machine Automation Making Cyber-Policy Violator More Resilient: A Proportionate Study." In Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science, edited by Mrutyunjaya Panda and Harekrishna Misra, 329-345. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-6659-6.ch018

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Abstract

Cybersecurity is of global importance. Nearly all association suffer from an active cyber-attack. However, there is a lack of making cyber policy violator more resilient for analysts in proportionately analyzing security incidents. Now the question: Is there any proper technique of implementations for assisting automated decision to the analyst using a comparison study feature selection method? The authors take multi-criteria decision-making methods for comparison. Here the authors use CICDDoS2019 datasets consisting of Windows benign and the most vanguard for shared bouts. Hill-climbing algorithm may be incorporated to select best features. The time-based pragmatic data can be extracted from the mainsheet for classification as distributed cyber-policy violator or legitimate benign using decision tree (DT) with analytical hierarchy process (AHP) (DT-AHP), support vector machine (SVM) with technique for order of preference by similarity to ideal solution (SVM-TOPSIS) and mixed model of k-nearest neighbor (KNN AHP-TOPSIS) algorithms.

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