# deep neural networks

Published articles for deep neural networks.

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## Process Behaviour Anomaly Detection Using eBPF and Unsupervised-Learning Autoencoders

DevFeed: [Process Behaviour Anomaly Detection Using eBPF and Unsupervised-Learning Autoencoders](<https://devfeed.tech/articles/process-behaviour-anomaly-detection-using-ebpf-and-unsupervised-learning-autoencoders-41266.md>)

Original publisher: [Read original article](<https://www.evilsocket.net/2022/08/15/Process-behaviour-anomaly-detection-using-eBPF-and-unsupervised-learning-Autoencoders/>)

Author: Simone Margaritelli

Published: 2022-08-15T14:06:05Z

Content type: tutorial

Language: en

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

Topics: [eBPF](<https://devfeed.tech/topics/ebpf.md>), [Processes](<https://devfeed.tech/topics/processes.md>), [Linux Kernel](<https://devfeed.tech/topics/linux-kernel.md>), [Learning](<https://devfeed.tech/topics/learning.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [anomaly-detection](<https://devfeed.tech/tags/anomaly-detection.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [autoencoder](<https://devfeed.tech/tags/autoencoder.md>), [bcc](<https://devfeed.tech/tags/bcc.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>), [defensive-security](<https://devfeed.tech/tags/defensive-security.md>), [dnn](<https://devfeed.tech/tags/dnn.md>), [ebpf](<https://devfeed.tech/tags/ebpf.md>), [github](<https://devfeed.tech/tags/github.md>), [kernel](<https://devfeed.tech/tags/kernel.md>), [kprobe](<https://devfeed.tech/tags/kprobe.md>), [kretprobe](<https://devfeed.tech/tags/kretprobe.md>), [linux](<https://devfeed.tech/tags/linux.md>), [linux-security](<https://devfeed.tech/tags/linux-security.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [neural-networks](<https://devfeed.tech/tags/neural-networks.md>), [process-anomaly-detection](<https://devfeed.tech/tags/process-anomaly-detection.md>), [process-behaviour](<https://devfeed.tech/tags/process-behaviour.md>), [raw-syscalls](<https://devfeed.tech/tags/raw-syscalls.md>), [runtime-protection](<https://devfeed.tech/tags/runtime-protection.md>), [sys-enter](<https://devfeed.tech/tags/sys-enter.md>), [syscall-tracing](<https://devfeed.tech/tags/syscall-tracing.md>), [tensorflow](<https://devfeed.tech/tags/tensorflow.md>), [tracepoint](<https://devfeed.tech/tags/tracepoint.md>), [unsupervised-learning](<https://devfeed.tech/tags/unsupervised-learning.md>)

### AI overview

This tutorial describes using eBPF syscall tracing and an unsupervised autoencoder to detect process behavior anomalies at runtime. It explains an approach that models syscall frequency without requiring an explicit allowlist and discusses potential detection of exploitation, denial-of-service, and other attacks.

### Source excerpt

Hello everybody, I hope you've been enjoying this summer after two years of Covid and lockdowns :D In this post I'm going to describe how

## Pwnagotchi 1.0.0: An AI-assisted Raspberry Pi tool for WiFi security testing

DevFeed: [Pwnagotchi 1.0.0: An AI-assisted Raspberry Pi tool for WiFi security testing](<https://devfeed.tech/articles/weaponizing-and-gamifying-ai-for-wifi-hacking-presenting-pwnagotchi-1-0-0-41263.md>)

Original publisher: [Read original article](<https://www.evilsocket.net/2019/10/19/Weaponizing-and-Gamifying-AI-for-WiFi-Hacking-Presenting-Pwnagotchi-1-0-0/>)

Author: Simone Margaritelli

Published: 2019-10-19T08:45:28Z

Content type: article

Language: en

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

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Hacking](<https://devfeed.tech/topics/hacking.md>), [Raspberry Pi](<https://devfeed.tech/topics/raspberry-pi.md>), [REST API](<https://devfeed.tech/topics/rest-api.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [bettercap](<https://devfeed.tech/tags/bettercap.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>), [deep-reinforcement-learning](<https://devfeed.tech/tags/deep-reinforcement-learning.md>), [dnn](<https://devfeed.tech/tags/dnn.md>), [e-paper](<https://devfeed.tech/tags/e-paper.md>), [hacking](<https://devfeed.tech/tags/hacking.md>), [handshake-capture](<https://devfeed.tech/tags/handshake-capture.md>), [handshakes](<https://devfeed.tech/tags/handshakes.md>), [low-power](<https://devfeed.tech/tags/low-power.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [monitor-mode](<https://devfeed.tech/tags/monitor-mode.md>), [neural-networks](<https://devfeed.tech/tags/neural-networks.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [pi](<https://devfeed.tech/tags/pi.md>), [poc](<https://devfeed.tech/tags/poc.md>), [portable-hacking](<https://devfeed.tech/tags/portable-hacking.md>), [pwnagotchi](<https://devfeed.tech/tags/pwnagotchi.md>), [raspberry-pi](<https://devfeed.tech/tags/raspberry-pi.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [release](<https://devfeed.tech/tags/release.md>), [wifi](<https://devfeed.tech/tags/wifi.md>), [wifi-hacking](<https://devfeed.tech/tags/wifi-hacking.md>), [wireless-security](<https://devfeed.tech/tags/wireless-security.md>)

### AI overview

The article recounts the development and release of Pwnagotchi 1.0.0, a Raspberry Pi-based project that uses bettercap's REST API and adaptive algorithms to improve WiFi handshake collection and related security-testing capabilities. It describes the project's evolution from an initial Python proof of concept and discusses the use of a Waveshare e-Paper display for portable, low-power operation.

### Source excerpt

This is the story of a summer project that started out of boredom and that evolved into something incredibly fun and unique. It is also the story of how that project went from being discussed on a porch by just two people, to having a community made of almost 700 awesome people (and counting!) that gathered, polished it and made today's release possible. TL;DR: You can download the 1.0.0 .img file from here, then just follow the instructions. If you want the long version instead, sit back, relax and enjoy the ride. Let me tell you: it's going to be quite a long journey compared to my usual blog posts, but it'll be worth it (i hope) and fun (i hope even harder). Let's begin ...

## 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.

## A One-Year Plan to Learn and Apply Deep Learning

DevFeed: [A One-Year Plan to Learn and Apply Deep Learning](<https://devfeed.tech/articles/deep-learning-in-one-year-40100.md>)

Original publisher: [Read original article](<https://korbonits.com/blog/2015-03-07-deep-learning-in-one-year/>)

Published: 2015-03-07T11:49:38Z

Content type: opinion

Language: en

Sources: [Alex Korbonits](<https://devfeed.tech/sources/alex-korbonits.md>)

Topics: [Deep learning](<https://devfeed.tech/topics/deep-learning.md>), [Deep neural networks](<https://devfeed.tech/topics/deep-neural-networks.md>), [Computer vision](<https://devfeed.tech/topics/computer-vision.md>), [asr](<https://devfeed.tech/topics/asr.md>), [Natural language processing](<https://devfeed.tech/topics/nlp.md>), [Mathematics](<https://devfeed.tech/topics/mathematics.md>), [Bioinformatics](<https://devfeed.tech/topics/bioinformatics.md>), [ImageNet](<https://devfeed.tech/topics/imagenet.md>), [Amazon EC2](<https://devfeed.tech/topics/amazon-ec2.md>)

Tags: [computer-vision](<https://devfeed.tech/tags/computer-vision.md>), [data](<https://devfeed.tech/tags/data.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [deep-neural-networks](<https://devfeed.tech/tags/deep-neural-networks.md>), [mathematics](<https://devfeed.tech/tags/mathematics.md>), [natural-language-processing](<https://devfeed.tech/tags/natural-language-processing.md>)

### AI overview

The author sets a one-year goal of developing a working understanding of how to design, train, and implement deep learning systems. The planned exploration combines theory and engineering, including deep neural networks, computer vision, music genre classification, unsupervised learning, GPU computation, and tensor-based methods.

### Source excerpt

A birthday resolution: achieve a working understanding of deep learning within one year.