恶意软件检测中解决样本不平衡问题的策略

互联网技术已经使人们的生活和工作发生了巨大的改变．然而，人们在享受互联网提供的便利的同时，也承受着恶意程序带来的威胁．在数字化时代的今天，与恶意程序的对抗已成为信息领域的焦点．由于恶意软件检测中的恶意软件样本难于获取，同时，标记大量的样本也需要花费大量的人力和物力，所获得的恶意软件样本远远少于正常软件样本，因此各类的训练样本之间存在分布不平衡的分类问题．为了解决该问题，本文提出采用SMOTE过采样方法，通过合理的增加少数类样本来解决样本不平衡问题．

Malware Detection in the Strategy to Solve the Problem of Unbalanced Samples

Great changes have occurred in people's daily life and routine work due to the widely used internet technology. However, we have to face threaten from the malware. Due to this, detecting malware has received more and more attentions in recent years. Malware samples are hard to obtain. Meanwhile, it needs to cost a lot of resources. So there are less malware samples and malware detection is an imbalance problem. Imbalance problem means that the distributions of various types of training samples are imbalanced. To solve this problem, a suitable over-sampling method is employed via a reasonable increase in samples of a few samples to address imbalances.