DroidDetective
emlearn
DroidDetective | emlearn | |
---|---|---|
6 | 5 | |
98 | 424 | |
- | 4.8% | |
0.0 | 9.2 | |
almost 2 years ago | 6 days ago | |
Python | Python | |
GNU General Public License v3.0 only | MIT License |
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DroidDetective
- Using machine learning to identify malware in Android applications
- A Machine Learning, Reverse Engineering, and Malware Analysis Framework for Android Applications
- A Machine Learning Malware Analysis Framework For Android Applications
- Using artificial intelligence / ML to identify malware in Android applications
emlearn
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EleutherAI announces it has become a non-profit
> My big gripe, and for obvious reasons, is that we need to step away from cloud-based inference, and it doesn't seem like anyone's working on that.
I think there are steps being taken in this direction (check out [1] and [2] for interesting lightweight transpile / ad-hoc training projects) but there is a lack of centralized community for these constrained problems.
[1] https://github.com/emlearn/emlearn
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Simple and embedded friendly C code for Machine Learning inference algorithms
Examples: Gaussian Mixture Models (GMM) for anomaly detection or clustering Mahalanobis distance (EllipticEnvelope) for anomaly detection Decision trees and tree ensembles (Random Forest, ExtraTrees) Feed-forward Neural Networks (Multilayer Perceptron, MLP) for classification Gaussian Naive Bayes for classification
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[D] Drop your best open source Deep learning related Project
https://github.com/emlearn/emlearn is a ML inference engine for microcontrollers and embedded systems, allowing to deploy models to any platform with a C99 compiler. Has also been used for network traffic analysis as a Linux kernel module, and embedded in Android apps.
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Regression with the C64
The C64 has 64 kB of RAM. That is more than many contemporary microcontrollers. Using something like https://github.com/emlearn/emlearn allows to generate portable C code of ML models for such targets. Should be able to classify digits (MNIST) no problem on such hardware. Assuming there is a workable C compiler available.
Disclosure: Maintainer of emlearn project.
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Ask HN: What are some tools / libraries you built yourself?
I built emlearn, a Machine Learning inference engine for microcontrollers and embedded systems. It allows converting traditional ML models to simple and portable C99, following best practices in embedded software (no dynamic allocations etc). https://github.com/emlearn/emlearn
What are some alternatives?
AutoDroid - A tool for automating interactions with Android devices - including ADB, AndroGuard, and Frida interactivity.
miceforest - Multiple Imputation with LightGBM in Python
yaralyzer - Visually inspect and force decode YARA and regex matches found in both binary and text data. With Colors.
cppflow - Run TensorFlow models in C++ without installation and without Bazel
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
fselect - Find files with SQL-like queries
pwndbg - Exploit Development and Reverse Engineering with GDB Made Easy
pico-wake-word - MicroSpeech Wake Word example on the Raspberry Pi Pico. This is a port of the example on the TensorFlow repository.
MDML - Malware Detection using Machine Learning (MDML)
sklearn-project-template - Machine learning template for projects based on sklearn library.
experta - Expert Systems for Python
gutenberg - A fast static site generator in a single binary with everything built-in. https://www.getzola.org