The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Machine Automation Making Cyber-Policy Violator More Resilient: A Proportionate Study
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.
Related Content
|
Princy Pappachan, Sreerakuvandana, Mosiur Rahaman.
© 2024.
26 pages.
|
|
Winfred Yaokumah, Charity Y. M. Baidoo, Ebenezer Owusu.
© 2024.
23 pages.
|
|
Mario Casillo, Francesco Colace, Brij B. Gupta, Francesco Marongiu, Domenico Santaniello.
© 2024.
25 pages.
|
|
Suchismita Satapathy.
© 2024.
19 pages.
|
|
Xinyi Gao, Minh Nguyen, Wei Qi Yan.
© 2024.
13 pages.
|
|
Mario Casillo, Francesco Colace, Brij B. Gupta, Angelo Lorusso, Domenico Santaniello, Carmine Valentino.
© 2024.
30 pages.
|
|
Pratyay Das, Amit Kumar Shankar, Ahona Ghosh, Sriparna Saha.
© 2024.
32 pages.
|
|
|