HHHeJiahui's Stars
HHHeJiahui/Fed-2
adap/flower
Flower: A Friendly Federated AI Framework
fuergaosi233/wechat-chatgpt
Use ChatGPT On Wechat via wechaty
jayeshmanani/Decision-Tree-Classifier-using-scikit-learn
In this notebook, we will use scikit-learn to perform a decision tree based classification of weather data. The file daily_weather.csv is a comma-separated file that contains weather data. This data comes from a weather station located in San Diego, California. The weather station is equipped with sensors that capture weather-related measurements such as air temperature, air pressure, and relative humidity. Data was collected for a period of three years, from September 2011 to September 2014, to ensure that sufficient data for different seasons and weather conditions is captured. Let's now check all the columns in the data.
harshilpatel1799/IoT-Network-Intrusion-Detection-and-Classification-using-Explainable-XAI-Machine-Learning
The continuing increase of Internet of Things (IoT) based networks have increased the need for Computer networks intrusion detection systems (IDSs). Over the last few years, IDSs for IoT networks have been increasing reliant on machine learning (ML) techniques, algorithms, and models as traditional cybersecurity approaches become less viable for IoT. IDSs that have developed and implemented using machine learning approaches are effective, and accurate in detecting networks attacks with high-performance capabilities. However, the acceptability and trust of these systems may have been hindered due to many of the ML implementations being ‘black boxes’ where human interpretability, transparency, explainability, and logic in prediction outputs is significantly unavailable. The UNSW-NB15 is an IoT-based network traffic data set with classifying normal activities and malicious attack behaviors. Using this dataset, three ML classifiers: Decision Trees, Multi-Layer Perceptrons, and XGBoost, were trained. The ML classifiers and corresponding algorithm for developing a network forensic system based on network flow identifiers and features that can track suspicious activities of botnets proved to be very high-performing based on model performance accuracies. Thereafter, established Explainable AI (XAI) techniques using Scikit-Learn, LIME, ELI5, and SHAP libraries allowed for visualizations of the decision-making frameworks for the three classifiers to increase explainability in classification prediction. The results determined XAI is both feasible and viable as cybersecurity experts and professionals have much to gain with the implementation of traditional ML systems paired with Explainable AI (XAI) techniques.
yliang725/Anomaly-Detection-IoT23
A research project of anomaly detection on dataset IoT-23
Chinmayi27/Detection-of-IoT-Botnet-Attacks
An anomaly detection and attack classification pipeline for commercial IoT devices.
husseinalygit/N-BaIoT-reloaded
This is a code reproduction for the paper titled "N-BaIoT—Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders"
Hx96/N-BaIoT-Network-based-Detection-of-IoT-Botnet-Attacks-Using-Logistic-regression
a classification for N-BaloT which is a Anomaly Detection
Alinshans/MyTinySTL
Achieve a tiny STL in C++11
qinguoyi/TinyWebServer
:fire: Linux下C++轻量级WebServer服务器
youngyangyang04/Skiplist-CPP
A tiny KV storage based on skiplist written in C++ language| 使用C++开发，基于跳表实现的轻量级键值数据库🔥🔥 🚀