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This repository contains the code and data for a project that detects malware from IoT devices using a publish-subscribe model with Confluent and Databricks. The project streams IoT device data to Kafka, analyzes it, and detects malware using machine learning models such as Random Forest and Gradient Boosted Trees.

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AWS Initial Proposal.pdf
AWS Initial Proposal.pdf
 
 
README.md
README.md
 
 
code.ipynb.zip
code.ipynb.zip
 
 
report.pdf
report.pdf
 
 

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This repository contains the code and data for a project that detects malware from IoT devices using a publish-subscribe model with Confluent and Databricks. The project streams IoT device data to Kafka, analyzes it, and detects malware using machine learning models such as Random Forest and Gradient Boosted Trees.

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sql scikit-learn machine-learning-algorithms confluent classification apache-kafka databricks

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