Evaluation of convolutional neural network features for malware detection
Evaluation of convolutional neural network features for malware detection
复制标题
用于恶意软件检测的卷积神经网络特征评估
DOI：
10.1109/isdfs.2018.8355390
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Yusuf Kartal
中科院分区：
文献类型：
--
作者：
Kemal Özkan;Ş. Işık;Yusuf Kartal
机器学习技术的进步已经提供了可以基于静态和动态特征来检测恶意程序。此外，整个文献中的一系列研究表明，一旦转换到图像域，恶意软件检测就可以以显着的准确率进行处理。为了实现这一点，已经开发了一些基于图像的技术，连同特征提取和分类器，以发现恶意软件二进制文件之间的关系，在灰度颜色表示。通过类似的方式，我们贡献了CNN功能来克服恶意软件检测问题。实验研究结果表明，当将机器学习系统应用于由12，279个恶意软件样本组成的36个恶意软件家族（包括良性类型）时，可以以85%的准确率对恶意软件类型进行分类。此外，我们已经达到了99%的准确率时，进行和实验的25个家庭有9339个恶意软件样本。
Advances in machine learning technologies have provided that malicious programs can be detected based on static and dynamic features. Moreover, a crowded set of studies throughout literature indicates that malware detection can be handled with remarkable accuracy rate once converted into image domain. To realize this, some image based techniques have been developed together with feature extraction and classifiers in order to discover the relation between malware binaries in grayscale color representation. With a similar way, we have contributed the CNN features to overcome the malware detection problem. Findings of experimental research support that the malware types can be classified with 85% accuracy rate when applying the machine learning system on 36 (including benign type) malware families consisting of 12,279 malware samples. Moreover, we have achieved the 99% accuracy rate when conducting and experiment on 25 families having 9, 339 malware samples.