Detection of malicious documents by extracting and interpreting macros in Microsoft Office files

Macros can greatly enhance the capabilities and convenience provided in documents. They also invite adversaries to include malicious code in lure documents, often used as initial access into a user’s environment. This project will extract and analyze macros and determine their indent and potential for malicious code execution. Reducing time to response through malicious code detection will allow analysts to spend their time on more meaningful work. Through applied machine learning and neural networks, we can detect and determine the impact to the customer’s bottom line.

Faculty Supervisor:

Ali Dehghantanha

Student:

Partner:

eSentire

Discipline:

Computer science

Sector:

Technology; Information and Communications Technology; Other

University:

University of Guelph

Program:

Accelerate

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