Cybercrime is one of the major digital threats of this century. In particular, ransomware attacks have significantly increased, resulting in global damage costs of tens of billion dollars. In this paper, we train and test different Machine Learning and Deep Learning models for malware detection, malware classification and ransomware detection. We introduce a novel and flexible ransomware detection model that combines two optimized models. Our detection results on a limited dataset demonstrate good accuracy and F1 scores.
翻译:网络犯罪是本世纪的主要数字威胁之一。 特别是,赎金软件袭击大幅增加,导致全球损失成本达数百亿美元。 在本论文中,我们培训和测试了不同的机器学习和深学习模式,用于恶意软件检测、恶意软件分类和赎金软件检测。我们引入了一种新型和灵活的赎金软件检测模式,将两种优化模型结合起来。我们在有限的数据集上的检测结果显示了良好的准确性和F1分数。