Visible to the public Biblio

Filters: Keyword is ransom ware  [Clear All Filters]
2023-03-17
Gao, Chulan, Shahriar, Hossain, Lo, Dan, Shi, Yong, Qian, Kai.  2022.  Improving the Prediction Accuracy with Feature Selection for Ransomware Detection. 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC). :424–425.
This paper presents the machine learning algorithm to detect whether an executable binary is benign or ransomware. The ransomware cybercriminals have targeted our infrastructure, businesses, and everywhere which has directly affected our national security and daily life. Tackling the ransomware threats more effectively is a big challenge. We applied a machine-learning model to classify and identify the security level for a given suspected malware for ransomware detection and prevention. We use the feature selection data preprocessing to improve the prediction accuracy of the model.
ISSN: 0730-3157