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Name: coder
Type: User
Name: coder
Type: User
Source code for the paper: Adaptive Clustering-based Malicious Traffic Classification at the Network Edge (https://homepages.inf.ed.ac.uk/ppatras/pub/infocom21.pdf)
ALAD (Proceedings of IEEE ICDM 2018) official code
安全场景、基于AI的安全算法和安全数据分析业界实践
The code for paper "ART: Abstraction Refinement-Guided Training for Provably Correct Neural Networks".
An advanced real time threat intelligence framework to identify threats and malicious web traffic on the basis of IP reputation and historical data.
Source codes for "Attackability Characterization of Adversarial Evasion Attack on Discrete Data" (SIGKDD 2020)
[NeurIPS 2020]auto_LiRPA: An Automatic Linear Relaxation based Perturbation Analysis Library for Neural Networks
A curated list of awesome adversarial machine learning resources
:octocat: Machine Learning for Cyber Security
Network Analysis Tool
Code for our USENIX Security 2021 paper -- CADE: Detecting and Explaining Concept Drift Samples for Security Applications
Efficient Robustness Verification for ReLU networks (this repository is DEPRECATED, don't use, see description)
A challenge to explore adversarial robustness of neural networks on CIFAR10.
Codes for reproducing the experimental results in "CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks", published at AAAI 2019
Certified defense to adversarial examples using CROWN and IBP. Also includes GPU implementation of CROWN verification algorithm (in PyTorch).
CROWN: A Neural Network Robustness Certification Algorithm for General Activation Functions (This repository is DEPRECATED; use https://github.com/huanzhang12/RecurJac-and-CROWN instead)
My attempt at reproducing the paper Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection
A Pytorch implementation of the paper `Deep Autoencoding Gaussian Mixture Model For Unsupervised Anomaly Detection` by Zong et al.
This repository contains the code for Characterizing the Decision Boundary of Deep Neural Networks
Demo of RouteNet in ACM SIGCOMM'19
Provably defending pretrained classifiers including the Azure, Google, AWS, and Clarifai APIs
The source code and dataset are used to demonstrate the DF model, and reproduce the results of the ACM CCS2018 paper
DL2 is a framework that allows training neural networks with logical constraints over numerical values in the network (e.g. inputs, outputs, weights) and to query networks for inputs fulfilling a logical formula.
FARE: Enabling Fine-grained Attack Categorization under Low-quality Labeled Data
Adversarial Training for Natural Language Understanding
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.