Leveraging Machine Learning Techniques for Windows Ransomware Network Traffic Detection

Authors: Omar M. K. Alhawi, James Baldwin, Ali Dehghantanha
Published: 2018-07-27

Source: https://arxiv.org/abs/1807.10440

PDF: https://arxiv.org/pdf/1807.10440

AIにより推定されたラベル

機械学習の応用 バックドアモデルの検知 モデル評価

※ こちらのラベルはAIによって自動的に追加されました。そのため、正確でないことがあります。
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Abstract

Ransomware has become a significant global threat with the ransomware-as-a-service model enabling easy availability and deployment, and the potential for high revenues creating a viable criminal business model. Individuals, private companies or public service providers e.g. healthcare or utilities companies can all become victims of ransomware attacks and consequently suffer severe disruption and financial loss. Although machine learning algorithms are already being used to detect ransomware, variants are being developed to specifically evade detection when using dynamic machine learning techniques. In this paper, we introduce NetConverse, a machine learning analysis of Windows ransomware network traffic to achieve a high, consistent detection rate. Using a dataset created from conversation-based network traffic features we achieved a true positive detection rate of 97.1 using the Decision Tree (J48) classifier.