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Twitter: Hanger
Type: User
Twitter: Hanger
BUSINESS CONTEXT: With the enormous growth of computer networks usage and the huge increase in the number of applications running on top of it, network security is becoming increasingly more important. All the computer systems suffer from security vulnerabilities which are both technically difficult and economically costly to be solved by the manufacturers. Therefore, the role of Intrusion Detection Systems (IDSs), as special-purpose devices to detect anomalies and attacks in the network, is becoming more important. The research in the intrusion detection field has been mostly focused on anomaly-based and misusebased detection techniques for a long time. While misuse-based detection is generally favored in commercial products due to its predictability and high accuracy, in academic research anomaly detection is typically conceived as a more powerful method due to its theoretical potential for addressing novel attacks. Conducting a thorough analysis of the recent research trend in anomaly detection, one will encounter several machine learning methods reported to have a very high detection rate of 98% while keeping the false alarm rate at 1%. However, when we look at the state of the art IDS solutions and commercial tools, there is no evidence of using anomaly detection approaches, and practitioners still think that it is an immature technology. To find the reason of this contrast, lots of research was done done in anomaly detection and considered various aspects such as learning and detection approaches, training data sets, testing data sets, and evaluation methods. BUSINESS PROBLEM: Your task to build network intrusion detection system to detect anamolies and attacks in the network. There are two problems. 1. Binomial Classification: Activity is normal or attack 2. Multinomial classification: Activity is normal or DOS or PROBE or R2L or U2R
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)
[ECCV2022] Learning to Drive by Watching YouTube Videos: Action-Conditioned Contrastive Policy Pretraining
reveal the vulnerabilities of machine learning models
Official Implement of "ADBench: Anomaly Detection Benchmark".
An experimental open-source attempt to make GPT-4 fully autonomous.
AutoPWN Suite is a project for scanning vulnerabilities and exploiting systems automatically.
AI自动化任务
Baselines ----- traffic flow prediction
Bluetooth experimentation framework for Broadcom and Cypress chips.
Bluetooth Low Energy (BLE) packet sniffer and transmitter for both standard and non standard (raw bit) based on Software Defined Radio (SDR).
ChatGLM-6B:开源双语对话语言模型 | An Open Bilingual Dialogue Language Model
基于大模型搭建的微信聊天机器人,同时支持微信、企业微信、公众号、飞书、钉钉接入,可选择GPT3.5/GPT4.0/Claude/文心一言/讯飞星火/通义千问/Gemini/GLM-4/LinkAI,能处理文本、语音和图片,访问操作系统和互联网,支持基于自有知识库进行定制企业智能客服。
CodeGeeX: An Open Multilingual Code Generation Model
A high-performance, zero-overhead, extensible Python compiler using LLVM
train neural networks up to 7x faster
Tp-Link Archer AX50 Authenticated RCE (CVE-2022-30075)
URL来对恶意网站和normal website分类 NLP
《动手学深度学习》:面向中文读者、能运行、可讨论。中英文版被全球175所大学采用教学。
给别人家的女朋友发早安
A python library for easy manipulation and forecasting of time series.
(DeepPacket网络流量分类--编程风格值得借鉴)Pytorch implementation of deep packet: a novel approach for encrypted traffic classification using deep learning
deeplearning.ai(吴恩达老师的深度学习课程笔记及资源)
Dense Contrastive Learning (DenseCL) for self-supervised representation learning, CVPR 2021 Oral.
Predicting depression from acoustic features of speech using a Convolutional Neural Network.
:art: Diagram as Code for prototyping cloud system architectures
Offline Quantization Tools for Deploy.
Create Disco Diffusion artworks in one line
【冲破内核瓶颈,让I/O性能飙升】DPDK工程师手册,官方文档,最新视频,开源项目,实战案例,论文,大厂内部ppt,知名工程师一览表
Advanced evolutionary computation library built directly on top of PyTorch, created at NNAISENSE.
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.