Wednesday, July 15, 2020
Home Lead Story Intel-Microsoft Collaborated Project Turns Malware into Images

Intel-Microsoft Collaborated Project Turns Malware into Images

Intel and Microsoft joined hands to work on the study

Researchers from Intel and Microsoft have joined forces to study the use of deep learning for malware threat detection in a project that first converts malware into images.

The basis for this study is the observation that if malware samples are turned into grayscale images, the textural and structural patterns can be used to effectively classify them as either benign or malicious, as well as cluster malicious samples into respective threat families, Microsoft said.

The researchers used an approach that they called static malware-as-image network analysis (STAMINA), Jugal Parikh and Marc Marino from Microsoft Threat Protection Intelligence Team wrote in a blog post.

For the first part of the collaboration, the researchers built on Intel’s prior work on deep transfer learning for static malware classification and used a real-world dataset from Microsoft to ascertain the practical value of approaching the malware classification problem as a computer vision task.

Using the dataset from Microsoft, the study showed that the STAMINA approach achieves high accuracy in detecting malware with low false positives.

The results were detailed in a paper titled “STAMINA: Scalable deep learning approach for malware classification”.

intel-logo
The researchers used an approach that they called static malware-as-image network analysis (STAMINA). Wikimedia Commons

Read More: Healthy Eating Habits in Toddlers Reduces Chances Of Heart Realted Risks Later: Study

To establish the practicality of the STAMINA approach, which posits that malware can be classified at scale by performing static analysis on malware codes represented as images, the study covered three main steps: image conversion, transfer learning, and evaluation.

The study was performed on a dataset of 2.2 million PE file hashes provided by Microsoft. This dataset was temporally split into 60:20:20 segments for training, validation, and test sets, respectively.

The joint research encourages the use of deep transfer learning for the purpose of malware classification. (IANS)

STAY CONNECTED

18,985FansLike
362FollowersFollow
1,785FollowersFollow

Most Popular

The Impacts To Online Marketing After COVID

The covid-19 pandemic has had a huge impact on the majority of our lives so far in 2020. But as the world of retail...

Only 3% Indians Realize the Importance of Adequate Protein Diet

Although 95 percent of Indian mothers claim to know protein as a macro-nutrient, only three percent of the population really understands the prominent functions...

Here’s Why Pregnant Women Should be Beware of UTIs

Although the monsoon brings respite from summer's scorching heat, it also invites a plethora of allergies and infections. Pregnant women should be cautious because...

International Mandela Day: Following Madiba’s Footsteps

While the mention of South Africa immediately brings alive the imagery of safaris, glamping, bungee jumping, diverse food, and warm people, its rich and...

The 5 Undeniable Reasons for Purchasing HGH

Are you tired of the endless disappointment that comes with trying numerous diet pills and diet programs? Don't worry, and you aren't alone. Most...

This Facebook Robot Walks On Power Lines To Install Fiber-Optic Cable

Facebook has developed an aerial fiber deployment solution that uses a robot to safely deploy a specialized fiber-optic cable on medium-voltage (MV) power lines...

In Conversation With Dr. Chandra Shekhar Mayanil on Yog, Dhyan and Hinduism

Dr. Chandra Shekhar Mayanil is a Neuroscientist who is currently living in Naperville, Illinois. He has a very profound knowledge about yog, dhyan and...

Cloud Kitchens in India: A New Normal

There is a certain distinction in the concept and coinage of the term 'cloud kitchen'; the concept of takeaway or delivery only, without dining,...

Recent Comments