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awesome-deeplearning-resources

Deep Learning and deep reinforcement learning research papers and some codes

deep-q-learning-paper-to-code

federated-learning-faq

free

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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。

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