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hangeramber's Projects

-networking-network-intrusion-detection- icon -networking-network-intrusion-detection-

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

acid icon acid

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)

aco- icon aco-

[ECCV2022] Learning to Drive by Watching YouTube Videos: Action-Conditioned Contrastive Policy Pretraining

aijack icon aijack

reveal the vulnerabilities of machine learning models

autogpt icon autogpt

An experimental open-source attempt to make GPT-4 fully autonomous.

autopwn-suite icon autopwn-suite

AutoPWN Suite is a project for scanning vulnerabilities and exploiting systems automatically.

btle icon btle

Bluetooth Low Energy (BLE) packet sniffer and transmitter for both standard and non standard (raw bit) based on Software Defined Radio (SDR).

chatglm-6b icon chatglm-6b

ChatGLM-6B：开源双语对话语言模型 | An Open Bilingual Dialogue Language Model

chatgpt-on-wechat icon chatgpt-on-wechat

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 icon codegeex

CodeGeeX: An Open Multilingual Code Generation Model

codon icon codon

A high-performance, zero-overhead, extensible Python compiler using LLVM

d2l-zh icon d2l-zh

《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。

darts icon darts

A python library for easy manipulation and forecasting of time series.

deep-packet icon deep-packet

（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

densecl icon densecl

Dense Contrastive Learning (DenseCL) for self-supervised representation learning, CVPR 2021 Oral.

depression-detect---- icon depression-detect----

Predicting depression from acoustic features of speech using a Convolutional Neural Network.

diagrams icon diagrams

:art: Diagram as Code for prototyping cloud system architectures

discoart icon discoart

Create Disco Diffusion artworks in one line

dpdk_engineer_manual icon dpdk_engineer_manual

【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

evotorch icon evotorch

Advanced evolutionary computation library built directly on top of PyTorch, created at NNAISENSE.

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