Pinned Repositories
awesome-deeplearning-resources
Deep Learning and deep reinforcement learning research papers and some codes
Deep-Q-Learning-Paper-To-Code
Federated-Learning-FAQ
free
翻墙、免费翻墙、免费科学上网、免费节点、免费梯子、免费ss/ssr/v2ray/trojan节点、蓝灯、谷歌商店、翻墙梯子
Iot-Cyber-Security-with-Machine-Learning
IoT networks have become an increasingly valuable target of malicious attacks due to the increased amount of valuable user data they contain. In response, network intrusion detection systems have been developed to detect suspicious network activity. UNSW-NB15 is an IoT-based network traffic data set with different categories for normal activities and malicious attack behaviors. UNSW-NB15 botnet datasets with IoT sensors' data are used to obtain results that show that the proposed features have the potential characteristics of identifying and classifying normal and malicious activity. Role of ML algorithms is for developing a network forensic system based on network flow identifiers and features that can track suspicious activities of botnets is possible. The ML model metrics using the UNSW-NB15 dataset revealed that ML techniques with flow identifiers can effectively and efficiently detect botnets’ attacks and their tracks.
learn_dl
Deep learning algorithms source code for beginners
Machine-Learning-WU
ML-notes
notes about machine learning
NetworkIntrusionDetection
This repository is for exploring various ML and DL techniques for network intrusion detection
PyTorch_Primer
一个关于pytorch的入门教程，该教程写于2018年，之后pytorch版本经过更新，部分代码需要更改，使用者请自行改之。
CANDY404's Repositories
CANDY404/Deep-Q-Learning-Paper-To-Code
CANDY404/awesome-deeplearning-resources
Deep Learning and deep reinforcement learning research papers and some codes
CANDY404/Federated-Learning-FAQ
CANDY404/free
翻墙、免费翻墙、免费科学上网、免费节点、免费梯子、免费ss/ssr/v2ray/trojan节点、蓝灯、谷歌商店、翻墙梯子
CANDY404/Iot-Cyber-Security-with-Machine-Learning
IoT networks have become an increasingly valuable target of malicious attacks due to the increased amount of valuable user data they contain. In response, network intrusion detection systems have been developed to detect suspicious network activity. UNSW-NB15 is an IoT-based network traffic data set with different categories for normal activities and malicious attack behaviors. UNSW-NB15 botnet datasets with IoT sensors' data are used to obtain results that show that the proposed features have the potential characteristics of identifying and classifying normal and malicious activity. Role of ML algorithms is for developing a network forensic system based on network flow identifiers and features that can track suspicious activities of botnets is possible. The ML model metrics using the UNSW-NB15 dataset revealed that ML techniques with flow identifiers can effectively and efficiently detect botnets’ attacks and their tracks.
CANDY404/learn_dl
Deep learning algorithms source code for beginners
CANDY404/Machine-Learning-WU
CANDY404/ML-notes
notes about machine learning
CANDY404/NetworkIntrusionDetection
This repository is for exploring various ML and DL techniques for network intrusion detection
CANDY404/PyTorch_Primer
一个关于pytorch的入门教程，该教程写于2018年，之后pytorch版本经过更新，部分代码需要更改，使用者请自行改之。
CANDY404/test