论文标题
MDEA：通过进化对抗学习的恶意软件检测
MDEA: Malware Detection with Evolutionary Adversarial Learning
论文作者
论文摘要
恶意软件检测已使用机器学习来检测程序中的恶意软件。这些应用程序将原始的或处理过的二进制数据归为神经网络模型，以将其分类为良性或恶意文件。尽管这种方法已被证明有效地抵抗了动态变化，例如加密，混淆和包装技术，但它很容易受到特定的逃避攻击的影响，在这些攻击中，输入数据的小变化会导致测试时间错误分类。本文提出了一种新方法：MDEA，一种对抗性恶意软件检测模型使用进化优化来创建攻击样本，以使网络可靠地抵抗逃避攻击。通过使用进化的恶意软件样本来重新培训模型，其性能可以提高显着的余量。
Malware detection have used machine learning to detect malware in programs. These applications take in raw or processed binary data to neural network models to classify as benign or malicious files. Even though this approach has proven effective against dynamic changes, such as encrypting, obfuscating and packing techniques, it is vulnerable to specific evasion attacks where that small changes in the input data cause misclassification at test time. This paper proposes a new approach: MDEA, an Adversarial Malware Detection model uses evolutionary optimization to create attack samples to make the network robust against evasion attacks. By retraining the model with the evolved malware samples, its performance improves a significant margin.
