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handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

hello

Just learning to use GitHub

hust-thesis-word

华科硕士论文word模板

iclocs

Imperial College London Optimal Control Software (ICLOCS)

imbalanced-classification-u.s.-income

imbalanced-learn

A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning

intromatlabdynamics

A basic introduction to Matlab, guided towards simulation and analysis of dynamical systems

introtodeeplearning

Lab Materials for MIT 6.S191: Introduction to Deep Learning

intrusion-detection-system

I have tried some of the machine learning and deep learning algorithm for IDS 2017 dataset. The link for the dataset is here: http://www.unb.ca/cic/datasets/ids-2017.html. By keeping Monday as the training set and rest of the csv files as testing set, I tried one class SVM and deep CNN model to check how it works. Here the Monday dataset contains only normal data and rest of the days contains both normal and attacked data. Also, from the same university (UNB) for the Tor and Non Tor dataset, I tried K-means clustering and Stacked LSTM models in order to check the classification of multiple labels.

intrusion-detection-systems

This is the repo of the research paper, "Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security".

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.

itkst42-network-data-classifier

A network data classifier for UNSW-NB15 data set. This is an university course work for "ITKST42 Information Security Technology".

julia-mit

Tutorials and information on the Julia language for MIT numerical-computation courses.

kdd-cup-99-python

Analysis and preprocessing of the kdd cup 99 dataset using python and scikit-learn

kdd99-cnn-1

Intrusion Detection Based on Convolutional Neural Network with kdd99 data set

kddcup99

KDD-CUP99 网络入侵检测数据集的处理与研究

keras

Deep Learning for humans

leetcode

My own leetcode solution summary, hope it will be helpful for anyone interested.

libgendesktop

listen1_desktop

one for all free music in china (Windows, Mac, Linux desktop)

logoly

A Pornhub Flavour Logo Generator

lstm-ids

Network data classifier based on the recurrent neural network.

machine-learning-algorithms-for-detecting-network-attacks-with-unsw-nb15-data-set

Due to the increasingly development of network technology recently, there are various cyber-attacks posed the huge threats to different fields around the world. Many studies and researches about cyber-security are carried out by experts in order to construct a safe network environment for people. The aim of the work is to build the detection models for classifying the attack data. Hence, we applied the UNSW-NB15 network data set which combines both normal and modern low-level attacks because we would like to create the experimental scenario close to the real world. Two classifiers are logistic regression and decision tree model for binary classification in the work. The deployed technique for decision tree achieved the highest result with 99.99% of testing accuracy compare to the 78.15% of logistic regression classifier. On the other hand, the KNN model is used for categorizing the multi-class in the project, and the averaged accuracy for testing is around 23% for ten categories classification.

machine-learning-flappy-bird

Machine Learning for Flappy Bird using Neural Network and Genetic Algorithm

makefiletutorial

Learn make by example

manim

Animation engine for explanatory math videos

manual

Source code for https://www.v2ray.com/

missing-semester

The Missing Semester of Your CS Education 📚

ml-based-ids-cicids2017

This is a simple DNN based application for an IDS based on the CICIDS2017 dataset

mth_4115

Numerical Analysis, 2nd Edition (9780321783677): Timothy Sauer. Course notes and code assignments.