Mitigating DDoS Attacks: A Text-Mining Approach Completed Research

被引：0
|
作者：
Sharma, Kalpit [1 ]
Mukhopadhyay, Arunabha [1 ]
机构：
[1] Indian Inst Management Lucknow, Lucknow, Uttar Pradesh, India
来源：
DIGITAL INNOVATION AND ENTREPRENEURSHIP (AMCIS 2021) | 2021年
关键词：
DDoS; Latent Dirichlet Allocation; Naive Bayes; Cyber-risk; RISK-ASSESSMENT; INFORMATION; FRAMEWORK;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this study, we aim to assess and mitigate cyber-risk emanating for wrongly identifying critical themes in news articles about DDoS attacks by computing the probability of misclassification and expected losses associated with them. We use a hybrid approach comprising Latent Dirichlet Allocation and Kernel Naive Bayes classifier to ascertain the questions above. Subsequently, we suggest ways to mitigate cyber-risk by accepting, reducing, or passing it. Our study aims to help CTOs decide the best strategy to handle cyber-risk due to delayed response due to misidentifying critical themes.
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页数：10
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