Effective Ransomware Attacks Detection Using CNN Algorithm

Effective Ransomware Attacks Detection Using CNN Algorithm

Class Imbalance; Cybersecurity; Machine Learning Algorithms; Ransomware Detection; Social Media

This study identified ransomware threats in social media platforms by evaluating the performance of Assessing different machine-learning algorithms in various aspects of detecting and classifying ransomware content. The primary problem revolves around the need to enhance cybersecurity within the dynamic landscape of social media, where users are increasingly susceptible to malicious attacks. The research objectives involve assessing the effectiveness of different algorithms, including Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Decision Trees, K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Gradient Boosting (GBoost), in distinguishing between ransomware and benign content. A dataset consisting of 6,245 records with 15 features is employed to achieve this. The methods encompass data preprocessing, algorithm implementation, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The research results revealed significant variations in algorithm performance, with Decision Tree and GBoost exhibiting exceptional accuracy while class imbalance challenges and model optimization issues were identified. These findings provide valuable insights into the complex realm of ransomware detection in social media, offering a foundation for future research and cybersecurity improvements in the digital space

Huang J1, Catherin H2

https://ijicom.respati.ac.id/index.php/ijicom/article/view/62/50

December 2023

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