[OPLIN 4cast] OPLIN 4Cast #698: Can you spot the malware in this picture?

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OPLIN 4Cast #698: Can you spot the malware in this picture?
May 13th, 2020

[image: Shadowy figure in a computer matrix] Sometimes when I'm searching
online for something hard to pin down in words, I click over to the Image
results. Images let me quickly scan for nuances of a concept, and from
there, I can identify the sites that might offer exactly what I'm looking
for.


I was reminded of this today reading about 'STAMINA'—
*STAtic Malware-as-Image Network Analysis*. Artificial intelligence
applications are getting very good at learning from images, so Intel and
Microsoft are exploring a way to convert binary computer code into
grayscale pictures. Their A.I. can then scan the images for patterns, and
in this way, identify malware with increased accuracy.

   -
   - Microsoft, Intel Introduce 'STAMINA' Approach to Malware Detection
   <https://www.securityweek.com/microsoft-intel-introduce-stamina-approach-malware-detection>
   [*Security Week*] "The approach is based on the inspection of malware
   binaries plotted as grayscale images, which has revealed that there are
   textural and structural similarities between binaries from the same malware
   families, and differences between different families or between malware and
   benign software."
   - Microsoft and Intel project converts malware into images before
   analyzing it
   <https://www.zdnet.com/article/microsoft-and-intel-project-converts-malware-into-images-before-analyzing-it/>
   [*ZDNet*] "Microsoft says it provided a sample of 2.2 million infected
   Portable Executable file hashes to serve as a base for the research. The
   research team said STAMINA achieved an accuracy of 99.07% in identifying
   and classifying malware samples, with a false positives rate of 2.58%."
   - Microsoft and Intel turn malware into images to help spot more threats
   <https://www.engadget.com/microsoft-intel-stamina-malware-classification-162552852.html>
   [*Engadget*] "Most malware detection relies on extracting binary
   signatures or fingerprints, but the sheer number of signatures makes that
   impractical. This could help anti-malware tools effectively keep up and
   reduce the chances of security threats slipping past defenses."
   - Microsoft and Intel Develop STAMINA to Detect and Classify Malware
   <https://winbuzzer.com/2020/05/11/microsoft-and-intel-develop-stamina-to-detect-and-classify-malware-xcxwbn/>
   [*WinBuzzer*] "While it’s clearly early days, if success is ongoing
   STAMINA could one day be implemented across Microsoft’s business to help
   detect malware. Microsoft says its access to vast data from Windows
   Defender puts it in a good position to train the service."

*From the Ohio Web Library <http://ohioweblibrary.org>:*

   - Litman, Diane J. "Artificial intelligence
   <http://2232/advanced/article?id=ar032470&st=ai%20image%20learning>." *World
   Book Advanced*, World Book, 2020,
   - “Visual Trick Has AI Mistake Turtle for Gun
   <https://proxy.oplin.org:2111/login.aspx?direct=true&db=sch&AN=126126098&site=ehost-live>
   .” *New Scientist*, vol. 236, no. 3151, Nov. 2017, p. 19.
   - Fang, Yong, et al. “DeepDetectNet vs RLAttackNet: An Adversarial
   Method to Improve Deep Learning-Based Static Malware Detection Model
   <https://proxy.oplin.org:2111/login.aspx?direct=true&db=aph&AN=142872806&site=ehost-live>
   .” *PLoS ONE*, vol. 15, no. 4, Apr. 2020, pp. 1–32.

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