NEW DELHI: Manipulating images to hide malware is common. Once the image is opened on a system, the malware loader starts the decryption process. The decrypted file is then loaded on to the device memory triggering a malware attack.

Now, Microsoft and Intel have found a way to use images to detect malware attacks.

Intel Labs and Microsoft Threat Protection Intelligence are collaborating on a project named Static Malware-as-Image Network Analysis (STAMINA), which will turn any malicious code into images and use deep learning models to study them.

Classical malware detection approaches involve extracting binary signatures or fingerprints of the malware. However, due to growing number of malwares and signatures, matching signature has become challenging.

The other approaches include static and dynamic analysis. The former analyses the malware without executing it, but its performance can suffer from code obfuscation. The latter executes the malware in an sandbox to analyse it. It is effective but can be more time consuming.

That is where researchers turned to image-based transfer learning approach for static malware classification, using real-world data set. They used a Microsoft dataset of 2.2 million hashes of malware binaries and 10 columns of data.

A combination of known malware, potentially unwanted applications and unknown binaries with no known history were taken and converted into a stream of raw pixel data.

This one-dimensional pixel stream was then converted into a two-dimensional or 2D image to allow image analysis algorithms to work on them. The width and height were figured out by the file size after converting to pixel stream, following an empirically validated table.

Image height is calculated as the number of pixels divided by the width. After reshaping, the images were resized for transfer learning techniques.

Resizing does not adversely impact the classification result, since the system trains a very deep neural network to extract the deep-represented features, researches pointed out.

The 2D images were then fed into a deep neural network (DNN) that was trained using 60% of known malware samples. The DNN would scan and identify the image as clean or infected.

According to researchers, image-based technique used on x86 program binaries, achieved 99.07% accuracy with 2.58% false positive rate.

The study further showed that samples allowed all characteristics of the malwares to be captured during training. However, for applications of bigger size, STAMINA may not be fully effective as the software cannot convert billions of pixels into JPEG images and then resize them.

That is where meta-data-based methods can be more reliable over sample-based models.

The post How deep learning can be used to detect malware using 2D images appeared first on Artificial Intelligence.

NEW DELHI: Manipulating images to hide malware is common. Once the image is opened on a system, the malware loader starts the decryption process. The decrypted file is then loaded on to the device memory triggering a malware attack.

Now, Microsoft and Intel have found a way to use images to detect malware attacks.

Intel Labs and Microsoft Threat Protection Intelligence are collaborating on a project named Static Malware-as-Image Network Analysis (STAMINA), which will turn any malicious code into images and use deep learning models to study them.

Classical malware detection approaches involve extracting binary signatures or fingerprints of the malware. However, due to growing number of malwares and signatures, matching signature has become challenging.

The other approaches include static and dynamic analysis. The former analyses the malware without executing it, but its performance can suffer from code obfuscation. The latter executes the malware in an sandbox to analyse it. It is effective but can be more time consuming.

That is where researchers turned to image-based transfer learning approach for static malware classification, using real-world data set. They used a Microsoft dataset of 2.2 million hashes of malware binaries and 10 columns of data.

A combination of known malware, potentially unwanted applications and unknown binaries with no known history were taken and converted into a stream of raw pixel data.

This one-dimensional pixel stream was then converted into a two-dimensional or 2D image to allow image analysis algorithms to work on them. The width and height were figured out by the file size after converting to pixel stream, following an empirically validated table.

Image height is calculated as the number of pixels divided by the width. After reshaping, the images were resized for transfer learning techniques.

Resizing does not adversely impact the classification result, since the system trains a very deep neural network to extract the deep-represented features, researches pointed out.

The 2D images were then fed into a deep neural network (DNN) that was trained using 60% of known malware samples. The DNN would scan and identify the image as clean or infected.

According to researchers, image-based technique used on x86 program binaries, achieved 99.07% accuracy with 2.58% false positive rate.

The study further showed that samples allowed all characteristics of the malwares to be captured during training. However, for applications of bigger size, STAMINA may not be fully effective as the software cannot convert billions of pixels into JPEG images and then resize them.

That is where meta-data-based methods can be more reliable over sample-based models.

The post How deep learning can be used to detect malware using 2D images appeared first on Artificial Intelligence.

San Francisco, A team of researchers has used Artificial Intelligence (AI) to turn two-dimensional (2D) images into stacks of virtual three-dimensional (3D) slices showing activity inside organisms.

Using deep learning techniques, the team from University of California, Los Angeles (UCLA) devised a technique that extends the capabilities of fluorescence microscopy, which allows scientists to precisely label parts of living cells and tissue with dyes that glow under special lighting.

In a study published in the journal Nature Methods, the scientists also reported that their framework, called “Deep-Z,” was able to fix errors or aberrations in images, such as when a sample is tilted or curved.

Further, they demonstrated that the system could take 2D images from one type of microscope and virtually create 3D images of the sample as if they were obtained by another, more advanced microscope.

“This is a very powerful new method that is enabled by deep learning to perform 3D imaging of live specimens, with the least exposure to light, which can be toxic to samples,” said senior author Aydogan Ozcan, UCLA chancellor’s professor of electrical and computer engineering.

In addition to sparing specimens from potentially damaging doses of light, this system could offer biologists and life science researchers a new tool for 3D imaging that is simpler, faster and much less expensive than current methods.

The opportunity to correct for aberrations may allow scientists studying live organisms to collect data from images that otherwise would be unusable.

Investigators could also gain virtual access to expensive and complicated equipment, said researchers.

“Deep-Z” was taught using experimental images from a scanning fluorescence microscope, which takes pictures focused at multiple depths to achieve 3D imaging of samples.

In thousands of training runs, the neural network learned how to take a 2D image and infer accurate 3D slices at different depths within a sample.

Then, the framework was tested blindly – fed with images that were not part of its training, with the virtual images compared to the actual 3D slices obtained from a scanning microscope, providing an excellent match.

The researchers also found that Deep-Z could produce 3D images from 2D surfaces where samples were tilted or curved.

“This feature was actually very surprising,” said Yichen Wu, a UCLA graduate student who is co-first author of the publication. “With it, you can see through curvature or other complex topology that is very challenging to image.”

The post Artificial Intelligence can convert 2D images into 3D appeared first on Artificial Intelligence.