Ransomware Detection and Classification Using Random Forest: A Case Study with the UGRansome2024 Dataset

arXiv:2404.12855v1 Announce Type: new
Abstract: Cybersecurity faces challenges in identifying and mitigating ransomware, which is important for protecting critical infrastructures. The absence of datasets for distinguishing normal versus abnormal network behaviour hinders the development of proactive detection strategies against ransomware. An obstacle in proactive prevention methods is the absence of comprehensive datasets for contrasting normal versus abnormal network behaviours. The dataset enabling such contrasts would significantly expedite threat anomaly mitigation. In this study, we introduce UGRansome2024, an optimised dataset for …

abnormal arxiv case challenges classification critical cs.cr cybersecurity dataset datasets detection development forest important network normal prevention proactive protecting random ransomware ransomware detection strategies study