Using Machine Learning and Data Analytics to Prevent

This assignment will be based on the events described in APT32.pdf, attached. Given the indicators of compromise (IOCs), specifically address how MACHINE LEARNING and DATA ANALYTICS would be used to detect and/or prevent an attack from APT 32. Take for granted that the audience understands what machine learning and data analytics are. A brief overview of these technologies may be made as an introduction, but this should be no more than half a page. SIEM software is being increasingly used to prevent sophisticated malware attacks. The content of this paper must be technically specific. Use the provided template as an outline, though you are allowed and encouraged to add more sections and/or subsections. You should address how machine learning and data analytics could detect and/or prevent attacks by APT 32 overall, and also address how their malware variants could be individually detected or prevented. To review, the variants described are WINDSHIELD, KOMPROGO, PHOREAL, BEACON, and SOUNDBITE. Each variant must be addressed in specific detail.

Notes:

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Formatting – Times New Roman 12-point, single-spaced font.

Sources – Include the two provided, and select an additional three (3) scholarly resources in APA format

Length – 3-5 pages, original visuals (not copied) are encouraged, but do not contribute to this page minimum

Content – This is a highly technical report. Each indicator of compromise (IOC) must be appropriately addressed with a specific method of detection/prevention using MACHINE LEARNING and DATA ANALYTICS

Some popular SIEM software made by Splunk, Sophos, Cisco, etc. are currently very popular, feel free to use these and/or others that you are familiar with in your use case scenario.

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