Crypto mining aws cloud

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In this blog post, we show you the steps involved in helping to detect and mitigate cryptocurrency mining threats on edge devices using AWS IoT Device Defender custom metrics. Cryptocurrency , sometimes called crypto-currency or crypto, is any form of currency that exists digitally or virtually and uses cryptography to secure transactions.

Cryptocurrency mining is a process of creating new digital coins and is a compute intensive activity that has been on the rise in recent years.

Cryptojacking is a type of cybercrime that involves the unauthorized use of devices edge computers, smartphones, tablets, or even servers to mine for cryptocurrency and illicitly create currency. As cryptocurrency prices rise and more powerful edge devices with GPU capabilities are used to run ML at the edge use cases, there is an increasing threat of cryptojackers to exploit security vulnerabilities on edge devices.

Note that to investigate an anomaly, you need to correlate the alarm details with other contextual information such as device attributes, device metric historical trends, security profile metric historical trends, standard metrics, and logs to determine if a security threat is present.

Figure: Solution architecture to help monitor and detect edge devices for crypto currency mining threats. This blog post deploys the solution to the us-east-1 N. Virginia region by default.

Clone the Git repository of this blog post and run the component repository build script:. To change the default region us-east-1 , modify region section in the com. Create a configuration file named credentials in the. Add your AWS credentials to the credentials file in the following format.

This container will represent your edge device with GPU resources. The deployed virtual device is added into gg-awsiot-blog thing group. After creating the deployment, your device will receive the deployment, apply it and report the status to the cloud. You can check the actual payloads that the component publishes.

You can proceed to create a security profile that uses custom the GPU metric and the ML at the edge average inference time metric to evaluate the cryptocurrency threat situation. Current values of custom metrics are within the expected behavior of the device. Increase of GPU load and average ML model inference time will represent this situation as an abnormality. After running the update, you can check the published payloads for the device to see the increasing custom metrics in the payload, using the following command.

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How to Perform Pyspark Analysis and Kryo serialization. Analyzing the data using Pyspark Part 1. Analyzing the data using Pyspark Part 2. Creating table using hive.

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