Search Results for "malware-patch"
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Showing 21 open source projects for "malware-patch"
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LitterBox
A secure sandbox environment for malware developers and red teamers
LitterBox is a controlled malware-analysis and payload-testing sandbox aimed at red teams who need to validate evasions and behaviors before deployment. It provides an isolated environment to exercise payloads against modern detection stacks, verify signatures and heuristics, and observe runtime characteristics without leaking binaries to third-party vendors. The README frames typical use cases: testing evasion, validating detections, analyzing behavior, and keeping sensitive tooling in-house. ... -
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Vision Transformer Pytorch
Implementation of Vision Transformer, a simple way to achieve SOTA
This repository provides a from-scratch, minimalist implementation of the Vision Transformer (ViT) in PyTorch, focusing on the core architectural pieces needed for image classification. It breaks down the model into patch embedding, positional encoding, multi-head self-attention, feed-forward blocks, and a classification head so you can understand each component in isolation. The code is intentionally compact and modular, which makes it easy to tinker with hyperparameters, depth, width, and attention dimensions. Because it stays close to vanilla PyTorch, you can integrate custom datasets and training loops without framework lock-in. ... -
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Elkeid
Open source solution that can meet the requirements of workloads
Elkeid is an open-source platform for security and intrusion-detection that aims to support a wide variety of deployment contexts — from bare-metal hosts to containers, Kubernetes clusters, and even serverless environments. It was born out of ByteDance’s internal security best practices, offering for community users a subset of its enterprise-grade capabilities. Elkeid combines kernel-level data collection, user-space agents, and runtime instrumentation (RASP) to detect malicious behavior,... -
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TorchIO
Medical imaging toolkit for deep learning
TorchIO is an open-source Python library for efficient loading, preprocessing, augmentation and patch-based sampling of 3D medical images in deep learning, following the design of PyTorch. It includes multiple intensity and spatial transforms for data augmentation and preprocessing. These transforms include typical computer vision operations such as random affine transformations and also domain-specific ones such as simulation of intensity artifacts due to MRI magnetic field inhomogeneity (bias) or k-space motion artifacts. ... -
Total Network Visibility for Network Engineers and IT ManagersThis means every device on your network, and every interface on every device is automatically analyzed for performance, errors, QoS, and configuration.
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MiniMax-01
Large-language-model & vision-language-model based on Linear Attention
MiniMax-01 is the official repository for two flagship models: MiniMax-Text-01, a long-context language model, and MiniMax-VL-01, a vision-language model built on top of it. MiniMax-Text-01 uses a hybrid attention architecture that blends Lightning Attention, standard softmax attention, and Mixture-of-Experts (MoE) routing to achieve both high throughput and long-context reasoning. It has 456 billion total parameters with 45.9 billion activated per token and is trained with advanced parallel... -
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Uranie
Uranie is CEA's uncertainty analysis platform, based on ROOT
...Is is available both for Unix and Windows platforms (a dedicated platform archive is available on request). Note : if you have downloaded version 3.12 before the 8th of february, a patch exists for a minor bug on TOutputFileKey file, don't hesitate to ask us. -
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ThinkReview
AI-powered code reviews for GitLab & Azure DevOps. Zero setup. Powered
...The extension Summarises merge requests , provided suggestions and find potential security issues , provide smart questions to dive deeper and chat with your pull requests The extension works out of the box without any complex setup - Supports custom Self hosted Gitlab as well as gitlab.com and gitlab enterprise Enhance your GitLab MRs and Azure Devops PRsworkflows with automatic patch diff access and AI-powered code reviews. Key Features: • Auto-detect MR and PR Pages: The extension automatically recognizes when you're viewing a GitLab merge request. • Works with any instance of gitlab and azure devops whether it's on a custom domain or on gitlab.com • Integrated AI Code Review: Get instant AI-powered code reviews displayed directly in the GitLab interface... -
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MAE (Masked Autoencoders)
PyTorch implementation of MAE
MAE (Masked Autoencoders) is a self-supervised learning framework for visual representation learning using masked image modeling. It trains a Vision Transformer (ViT) by randomly masking a high percentage of image patches (typically 75%) and reconstructing the missing content from the remaining visible patches. This forces the model to learn semantic structure and global context without supervision. The encoder processes only the visible patches, while a lightweight decoder reconstructs the... -
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Perceptual Similarity Metric and Dataset
LPIPS metric. pip install lpips
While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex. Despite this, the most widely used perceptual metrics today, such as PSNR and SSIM, are simple, shallow functions, and fail to account for many nuances of human perception. Recently, the deep learning community has found that features of the VGG network trained on ImageNet classification has been remarkably useful as a training... -
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TimeSformer
The official pytorch implementation of our paper
TimeSformer is a vision transformer architecture for video that extends the standard attention mechanism into spatiotemporal attention. The model alternates attention along spatial and temporal dimensions (or designs variants like divided attention) so that it can capture both appearance and motion cues in video. Because the attention is global across frames, TimeSformer can reason about dependencies across long time spans, not just local neighborhoods. The official implementation in PyTorch... -
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OpenCvSharp410 Face Recognition Detect
OpenCvSharp with Cuda + TBB Face Detection Recognition
The site says "Malware detected but NOT TRUE", it's just encrypted! Free Source Code Live Face Detection via Web Camera from OpenCvSharp 3.4.5 with CUDA + TBB Original clean source code structure with FREE binary test. reach out .BIN folder for direct tryout(0penCvSharp345.Cuda.x86.FaceDetect.EXE) Other FOLDER contains Face Recognition of OpenCvSharp410, no CUDA! -
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EmguCv410 Face Recognition Detect
EmguCv C# OpenCv Cuda Face Recognition + Gender, Emotion, Ethnicity
The site says "Malware detected but NOT TRUE", it's just encrypted! EmguCvSharp Face Detection Full source code: https://sourceforge.net/projects/emgucvsharp-live-face-detect/ . if (CudaInvoke.HasCuda) //THE CODE TO USE CUDA . Emgucv Gender detection Emgucv Emotion detection Emgucv Ethnicity detection Emgucv Face Recognition also includes Pedestrian detection For Live: Face Attendance System, Facial Emotion, Gender Recognition Security Application. -
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DeepMask
Torch implementation of DeepMask and SharpMask
DeepMask is an early, influential approach to class-agnostic object segmentation that learns to propose pixel-accurate masks directly from images. Instead of first generating boxes and then refining them, the network predicts a foreground mask and an “objectness” score for a given image patch, yielding high-quality segment proposals suitable for downstream detection or instance segmentation. The model is trained end-to-end to align mask shape with object extent, which markedly improves recall at a manageable number of proposals. In practice, DeepMask is run on an image pyramid with a sliding window, followed by non-maximum suppression to produce a compact set of candidates. ... -
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PokemonGo-Bot
The Pokemon Go Bot, baking with community
PokemonGo-Bot is a project created by the PokemonGoF team. Since no public API available for now, a patch to use HASH-Server was applied. PokemonGoF is not part of HASH-Server dev team and has no connection with it. Based on Python for botting on any operating system - Windows, macOS and Linux. Multi-bot supported. Able to edit bot if certain level has reached. Allow custom hash service provider, if any. GPS Location configuration. Search & spin Pokestops / Gyms. -
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Malware Analysis Network in Taiwan
MAN in Taiwan, MiT
Malware Analysis Network in Taiwan <Man in Taiwan, MiT> Welcome to contact us (TonTon@TWMAN.ORG) if you are interested in collaborating with us. This project is open source and distributed under the GNU General Public License version 3. Please feel free to add to or modify this source and propose changes or new converters. Developer & Copyrighted by : TonTon Hsien-De Huang Prompter: Jazz Yao-Tsung Wang, Figaro Chen-Ho Yang | Logo Desinger:Temaki Guo Community on Google+：http://X.TWMAN.ORG/Community/ SourceForge: https://sourceforge.net/projects/twmanplus/files/ FaceBook: https://www.facebook.com/TWMAN.PLUS -
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Virtual Botmaster
Simulate Botnet NetFlow traffic for research analysis
Simulate Botnet NetFlow traffic for research analysis -
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Botnet Detectors Comparer
Compares botnet detection methods
Compares botnet detection methods by computing the error metrics by reading the labels on a NetFlow file. The original NetFlow should have a new column for the ground-truth label, and a new column with the prediction label for each botnet detection method. This program computes all the error metrics (TPR, TNR, FPR, FNR, Precision, Accuracy, ErrorRate, FMeasure1, FMeasure2, FMeasure0.5) and output the comparison results. It also ouputs a png plot. The program can compare in a... -
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tway
moved to https://github.com/sroycode/tway
Currently two way astar is implemented. For those interested I have uploaded a patch of this for pgrouting v1.05 moved to https://github.com/sroycode/tway -
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The intention of this project is to give all serious users of the SNNS a place where they find a bugfix and patch management and where they get useful information about the SNNS.
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A packet dissector driven by machine learning algorithms. You train it to recognize specific types of packets by showing it examples and counterexamples of some packet type, and it will figure out which bits in the packet define it as the type you seek.
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