近日，微软和英特尔开发了防病毒软件，可以将恶意软件转换为2D图像，并可以通过神经网络对其进行检查。

• 名为STAMINA的软件可将恶意软件位转换为2D图像
• 然后使用计算机视觉软件检查这些图像
• 该方法可以减少软件检查的数据点数量
• 在对恶意软件进行分类方面显示出99％的成功率

微软和英特尔已经合作开发一种新型的恶意软件检测工具。

该项目称为静态恶意软件-图像网络分析（STAMINA），是科技巨头的共同努力，旨在开发一种软件，该软件可通过将恶意代码转换为可通过深度学习进行评估的灰度图像来嗅探出恶意代码。

英特尔和微软表示，一种名为STAMINA的新型病毒检测软件将恶意软件转换为2D图像，可以通过计算机视觉算法对其进行扫描。

具体来说，STAMINA使用设计用于分析图像的计算机视觉软件将一维恶意软件位转换为二维灰度图像，然后“查看”图像中可能指示特定类型恶意代码的模式。

组装图像后，STAMINA然后将其调整为较小的尺寸，以使其更易于查看。

据研究人员称，这种压缩有助于避免该软件评估数十亿像素（这可能会减慢该过程），并且不会对其识别恶意软件的能力产生负面影响。

据ZDNet称，对STAMINA的培训使用了从Windows Defender（该公司生产的防病毒软件）中提取的数百万个恶意软件示例，并且在其发现计算机病毒的任务中显示了早期的希望。

该系统对恶意软件进行分类的准确率略高于99％，误报率低于2.6％。

The approach could help reduce the amount of data that needs to be scanned by algorithms and make malware detection more efficient (stock)

显然，该AI在较小的文件大小方面已显示出更多的成功，但是据微软称，STAMINA最终可以部署为仅专注于较小的文件。

无论哪种方式，该工具都可以是对当前扫描恶意软件的方法的一种改进，该方法可以创建非常大的数据点，并增加了恶意软件掉入裂缝的机会。

Microsoft and Intel develop antivirus software that turns malware into 2D images that can be examined by a neural network

• The software, called STAMINA, converts malware bits into 2D images
• It then examines those images using computer visions software
• The approach could reduce the number of data points examined by software
• It has shown a 99 percent success rate in classifying malware

Microsoft and Intel have partnered up in an effort to develop a new kind of malware detection

The project, called Static Malware-as-Image Network Analysis (STAMINA), is a joint effort by the tech giants to develop a software that sniffs out malicious code by converting it into greyscale images that can be assessed by utilizing deep-learning.

Intel and Microsoft say a new kind of virus-detecting software called STAMINA converts malware into 2D images that can be scanned by a computer vision algorithm (stock)

Specifically, STAMINA converts one-dimensional malware bits into two-dimensional greyscale images and then 'looks' at the images for patterns that may indicate specific types of malicious code using computer vision software designed to analyze images.

One the image is assembled, STAMINA then resizes it into a smaller dimension to make it easier to view.

This compressions, according to researchers helps avoid needing the software to assess billions of pixels - which would likely slow the process - and does not negatively affect its ability to identify malware.

According to ZDNet, STAMINA is trained using millions of examples of malware pulled from Windows Defender - an antivirus software made by the company - and has shown early promise in its missions to spot computer viruses.

The system has a little more than 99 percent accuracy with classifying malware and a false positive rate of below 2.6 percent.

The approach could help reduce the amount of data that needs to be scanned by algorithms and make malware detection more efficient (stock)

The AI apparently has apparently shown more success with smaller file sizes but according to Microsoft, STAMINA could eventually be deployed to focus solely on smaller files.

Either way the tool could be an improvement over current methods of scanning for malware that create very large data points and increase the chances of malware falling through the cracks.