| Title | Improving the Prediction Accuracy with Feature Selection for Ransomware Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Gao, Chulan, Shahriar, Hossain, Lo, Dan, Shi, Yong, Qian, Kai |
| Conference Name | 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) |
| Date Published | jun |
| Keywords | classification, composability, Data models, Data preprocessing, elbow method, feature extraction, machine learning, machine learning algorithms, Metrics, Predictive models, pubcrawl, random-forest, ransom ware, ransomware, resilience, Resiliency |
| Abstract | 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. |
| Notes | ISSN: 0730-3157 |
| DOI | 10.1109/COMPSAC54236.2022.00072 |
| Citation Key | gao_improving_2022 |
Improving the Prediction Accuracy with Feature Selection for Ransomware Detection