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Ojugo, Arnold
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An Empirical Evaluation On Comparative Machine Learning Techniques For Detection of The Distributed Denial of Service (DDoS) Attacks Ojugo, Arnold; Eboka, Andrew Okonji
Journal of Applied Science, Engineering, Technology, and Education Vol. 2 No. 1 (2020)
Publisher : Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (493.514 KB) | DOI: 10.35877/454RI.asci2192

Abstract

The advent of the Internet that aided the efficient sharing of resources. Also, it has introduced adversaries whom are today restlessly in their continued efforts at an effective, non-detectable means to invade secure systems, either for fun or personal gains. They achieve these feats via the use of malware, which is both on the rise, wreaks havoc alongside causing loads of financial losses to users. With the upsurge to counter these escapades, users and businesses today seek means to detect these evolving behavior and pattern by these adversaries. It is also to worthy of note that adversaries have also evolved, changing their own structure to make signature detection somewhat unreliable and anomaly detection tedious to network administrators. Our study investigates the detection of the distributed denial of service (DDoS) attacks using machine learning techniques. Results shows that though evolutionary models have been successfully implemented in the detection DDoS, the search for optima is an inconclusive and continuous task. That no one method yields a better optima than hybrids. That with hybrids, users must adequately resolve the issues of data conflicts arising from the dataset to be used, conflict from the adapted statistical methods arising from data encoding, and conflicts in parameter selection to avoid model overtraining, over-fitting and over-parameterization.
Forging An Optimized Bayesian Network Model With Selected Parameters For Detection of The Coronavirus In Delta State of Nigeria Ojugo, Arnold; Otakore, Oghenevwede Debby
Journal of Applied Science, Engineering, Technology, and Education Vol. 3 No. 1 (2021)
Publisher : Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (495.914 KB) | DOI: 10.35877/454RI.asci2163

Abstract

Machine learning algorithm have become veritable tools for effective decision support towards the construction of systems that assist experts (individuals) in their field of exploits and endeavor with regards to problematic tasks.. They are best suited for tasks where data is explored and exploited; and cases where the dataset contains noise, partial truth, ambiguities and in cases where there is shortage of datasets. For this study, we employ the Bayesian network to construct a model trained towards a target system that can help predict best parameters used for classification of the novel coronavirus (covid-19). Data was collected from Federal Medical Center Epidemiology laboratory (a centralized databank for all cases of the covid-19 in Delta State). Data was split into training and investigation (test) dataset for the target system. Results show high predictive capability.
Forging An Optimized Bayesian Network Model With Selected Parameters For Detection of The Coronavirus In Delta State of Nigeria Ojugo, Arnold; Otakore, Oghenevwede Debby
Journal of Applied Science, Engineering, Technology, and Education Vol. 3 No. 1 (2021)
Publisher : Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (495.914 KB) | DOI: 10.35877/454RI.asci2163

Abstract

Machine learning algorithm have become veritable tools for effective decision support towards the construction of systems that assist experts (individuals) in their field of exploits and endeavor with regards to problematic tasks.. They are best suited for tasks where data is explored and exploited; and cases where the dataset contains noise, partial truth, ambiguities and in cases where there is shortage of datasets. For this study, we employ the Bayesian network to construct a model trained towards a target system that can help predict best parameters used for classification of the novel coronavirus (covid-19). Data was collected from Federal Medical Center Epidemiology laboratory (a centralized databank for all cases of the covid-19 in Delta State). Data was split into training and investigation (test) dataset for the target system. Results show high predictive capability.
Co-Authors Eboka, Andrew Okonji Otakore, Oghenevwede Debby Otakore, Oghenevwede Debby