# ergo

Published articles for ergo.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## How to Create a Malware Detection System With Machine Learning

DevFeed: [How to Create a Malware Detection System With Machine Learning](<https://devfeed.tech/articles/how-to-create-a-malware-detection-system-with-machine-learning-41262.md>)

Original publisher: [Read original article](<https://www.evilsocket.net/2019/05/22/How-to-create-a-Malware-detection-system-with-Machine-Learning/>)

Author: Simone Margaritelli

Published: 2019-05-22T21:59:13Z

Content type: tutorial

Language: en

Sources: [evilsocket](<https://devfeed.tech/sources/evilsocket.md>)

Topics: [Malware](<https://devfeed.tech/topics/malware.md>), [Machine Learning & Artificial Intelligence](<https://devfeed.tech/topics/machine-learning-artificial-intelligence.md>), [Neural Network](<https://devfeed.tech/topics/neural-network.md>), [Windows](<https://devfeed.tech/topics/windows.md>), [Code](<https://devfeed.tech/topics/code.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [antivirus](<https://devfeed.tech/tags/antivirus.md>), [binary-analysis](<https://devfeed.tech/tags/binary-analysis.md>), [classification](<https://devfeed.tech/tags/classification.md>), [computer-virus](<https://devfeed.tech/tags/computer-virus.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [cybersecurity](<https://devfeed.tech/tags/cybersecurity.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [deep-neural-networks](<https://devfeed.tech/tags/deep-neural-networks.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [dnn](<https://devfeed.tech/tags/dnn.md>), [ergo](<https://devfeed.tech/tags/ergo.md>), [feature-engineering](<https://devfeed.tech/tags/feature-engineering.md>), [features](<https://devfeed.tech/tags/features.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [keras](<https://devfeed.tech/tags/keras.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [malware](<https://devfeed.tech/tags/malware.md>), [malware-detection](<https://devfeed.tech/tags/malware-detection.md>), [neural-network](<https://devfeed.tech/tags/neural-network.md>), [neural-networks](<https://devfeed.tech/tags/neural-networks.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [portable-executable](<https://devfeed.tech/tags/portable-executable.md>), [security-research](<https://devfeed.tech/tags/security-research.md>), [tensorflow](<https://devfeed.tech/tags/tensorflow.md>), [tf](<https://devfeed.tech/tags/tf.md>), [windows-pe](<https://devfeed.tech/tags/windows-pe.md>)

### AI overview

A practical tutorial on using machine learning and artificial neural networks to detect Windows malware without relying on an explicit signatures database. It uses malware detection as an example for the ergo project, which automates parts of model creation, data encoding, GPU training, benchmarking, and deployment.

### Source excerpt

In this post we'll talk about two topics I love and that have been central elements of my (private) research for the last ~7 years: machi

## Presenting Project Ergo: How to Build an Airplane Detector for Satellite Imagery With Deep Learning

DevFeed: [Presenting Project Ergo: How to Build an Airplane Detector for Satellite Imagery With Deep Learning](<https://devfeed.tech/articles/presenting-project-ergo-how-to-build-an-airplane-detector-for-satellite-imagery-with-deep-learning-41260.md>)

Original publisher: [Read original article](<https://www.evilsocket.net/2018/11/22/Presenting-project-Ergo-how-to-build-an-airplane-detector-for-satellite-imagery-with-Deep-Learning/>)

Author: Simone Margaritelli

Published: 2018-11-22T17:15:50Z

Content type: tutorial

Language: en

Sources: [evilsocket](<https://devfeed.tech/sources/evilsocket.md>)

Topics: [Keras](<https://devfeed.tech/topics/keras.md>), [Tensorflow](<https://devfeed.tech/topics/tensorflow.md>), [Framework](<https://devfeed.tech/topics/framework.md>), [Deep learning](<https://devfeed.tech/topics/deep-learning.md>), [Deep neural networks](<https://devfeed.tech/topics/deep-neural-networks.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Code](<https://devfeed.tech/topics/code.md>), [Development](<https://devfeed.tech/topics/development.md>), [Back end](<https://devfeed.tech/topics/backend.md>), [Python](<https://devfeed.tech/topics/python.md>), [Shell](<https://devfeed.tech/topics/shell.md>)

Tags: [cnn](<https://devfeed.tech/tags/cnn.md>), [code](<https://devfeed.tech/tags/code.md>), [computer-vision](<https://devfeed.tech/tags/computer-vision.md>), [convolutional-neural-networks](<https://devfeed.tech/tags/convolutional-neural-networks.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [cudnn](<https://devfeed.tech/tags/cudnn.md>), [cybersecurity](<https://devfeed.tech/tags/cybersecurity.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [deep-neural-networks](<https://devfeed.tech/tags/deep-neural-networks.md>), [dnn](<https://devfeed.tech/tags/dnn.md>), [ergo](<https://devfeed.tech/tags/ergo.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [image-classification](<https://devfeed.tech/tags/image-classification.md>), [keras](<https://devfeed.tech/tags/keras.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [neural-networks](<https://devfeed.tech/tags/neural-networks.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [planes](<https://devfeed.tech/tags/planes.md>), [planes-detector](<https://devfeed.tech/tags/planes-detector.md>), [planesnet](<https://devfeed.tech/tags/planesnet.md>), [project-release](<https://devfeed.tech/tags/project-release.md>), [python](<https://devfeed.tech/tags/python.md>), [shell](<https://devfeed.tech/tags/shell.md>), [tensorflow](<https://devfeed.tech/tags/tensorflow.md>), [tf](<https://devfeed.tech/tags/tf.md>), [train](<https://devfeed.tech/tags/train.md>), [training](<https://devfeed.tech/tags/training.md>), [tutorial](<https://devfeed.tech/tags/tutorial.md>)

### AI overview

The article introduces Project Ergo, an open-source framework and manager for Keras-based machine-learning projects. It demonstrates prototyping, training, and testing a convolutional neural network with the PlanesNet dataset to build an airplane detector for satellite imagery, including CPU and GPU setup considerations.

### Source excerpt

It's been a while that i've been quite intensively playing with Deep Learning both for work related research and personal projects. More specifically, I've been using the Keras framework on top of a TensorFlow backend for all sorts of stuff. From big and complex projects for malware detection, to smaller and simpler experiments about ideas i just wanted to quickly implement and test - it didn't really matter the scope of the project, I always found myself struggling with the same issues: code reuse over tens of crap python and shell scripts, datasets and models that are spread all over my dev and prod servers, no real standard for versioning them, no order, no structure. So a few days ago I started writing what it was initially meant to be just a simple wrapper for the main commands of my training pipelines but quickly became a full-fledged framework and manager for all my Keras based projects. Today I'm pleased to open source and present project Ergo by showcasing an example use-case: we'll prototype, train and test a Convolutional Neural Network on top of the PlanesNet raw dataset in order to build an airplane detector for satellite imagery.