Cyber Threats Prediction Model using Advanced Data Science Approaches

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ID: 313244
2022
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Abstract
In the era of technology and the widespread use of the internet, internet users' data and personal information are more at risk. Among various cyber-attacks, DDOS is one of the most dangerous cyber-attacks, which uses single or multiple victims for the unavailability of resources on a small and large scale. The amount and intensity of cyber attacks are also increasing gradually with increasing internet usage. So, defensive strategies are also built with time to protect a network and network devices from many breaches and attacks attempted by many cyber terrorists. Instead of traditional defense mechanisms, data science makes it impressive and easy to predict and detect cyber attacks. This study proposed a data science-based prediction model using a substantial dataset CICDDOS2019. In this research, different models of Machine Learning, e.g., Decision Tree, Random Forest, SVM, and Naïve Bayes, are applied after making this dataset clean and considering the best relevant features for getting maximum accuracy to detect and predict the cyber threats.
Reference Key
imported_1777060001_69ebc8a10d919 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ali Raza
Journal Journal of Computing & Biomedical Informatics
Year 2022
DOI
10.56979/302/2022/52
URL
https://jcbi.org/index.php/Main/article/view/52
Keywords Keywords not found

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