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5g-monarch

Repository for 5G-Monarch paper

5g_cloud_deployment

This repository is an initiative to automate the deployment process of 5G core network in cloud environment.

5gad

This is a dataset of 5G network traffic for use with machine learning tools to benchmark attack detection capabilities for multiple different models. The dataset contains simulated normal and attack 5G network traffic.

cleanerbot

cml-ids

This is the code of CML-IDS

continuous_control_with_deep_rl

This repo contains an implementation of a very popular algorithm used in deep Reinforcement Learning named DDPG (Deep Deterministic Policy Gradient). This project is done as a part of Udacity's Deep Reinforcement Learning nanodegree program. In this project, I tried to solve a Unity ML agent environment named Reacher with DDPG algorithm.

data-science-for-beginners

10 Weeks, 20 Lessons, Data Science for All!

designing-a-minimum-distance-to-class-mean-classifier

the objectives of this experiment is to know how a simple classifier works. The classifier implemented in this experiment may not work correctly in all situation but the purpose to know how a classifier works can be accomplished. Firstly in the introduction section we will discuss the basic things of a classifier and also we will know what includes in our experiment. Then we will go for the implementation of our experiment. We will use MATLAB tools to implement our classifier. After that we will perform a simple result analysis on the result. Then we will conclude our experiment.

early-intrusion-detection-in-sdn

This project aims to detect intrusion in an SDN network as early as possible.

encrypted_network_traffic_classification_in_sdn

This repository describes the demonstration of encrypted network traffic classification in SDN environment. A testbed is created using Mininet in this project. A RYU controller application is developed to classify network traffic in real-time.

fastbook

Draft of the fastai book

fl_ids

Public resources for classes, tutorials, and demonstrations.

free5gc

Open source 5G core network base on 3GPP R15

free5gc-compose

ftp_server

In this project I tried to implement FTP protocol using socket programming in JAVA.

gtp5g

GTP-U Linux Kernel Module

implementing-k-means-clustering

the objective of this experiment is to understand one of the very popular clustering algorithm known as K-Means clustering algorithm. This is an unsupervised learning method which means the class label is unknown here. But to measure the performance of the algorithm we need to know the ground truth. Here in this experiment we will use cluster purity as performance measure of the classifier. Here we use the dataset that has 150 data with four dimension each. We will cluster the whole dataset into three cluster here, so in our experiment k=3.

implementing-minimum-error-rate-classifier

the objective of this experiment is to classify some sample points using the posterior probabilities which uses Gaussian distribution to calculate the likelihood probabilities. The objective of this type of classifier is to minimize the error rate during classification. So this classifier takes decision based on the most posterior probabilities. This classifier is also known as Bayes classifier with minimum error.

implementing-the-perceptron-algorithm-for-finding-the-weights-of-a-linear-discriminant-function

the objective of this experiment is to apply perceptron algorithm to find the weights of a linear discriminant function. Perceptron algorithm is an incremental way for finding the weights of a linear discriminant function. In this experiment we will apply this algorithm to find weights of a given linear discriminant function. In this algorithm we start with a random weights and gradually we will forward to the actual weights. Though this algorithm has some drawbacks we will apply it for its simplicity. There are two implementation of this algorithm: one is batch processing (also known as many at a time), and the other is one at a time. We will evaluate our sample data with both process and in the result analysis section we will compare the performance of this two methods with different learning rate.

in-network_ml

int_p4

This project aims to develop network-based intrusion detection system using ML models. We use data plane programming to collect features and deploy our ML model in the data plane insteaded of control plane.

int_path

llm-course

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

llm-powerhouse-a-curated-guide-for-large-language-models-with-custom-training-and-inferencing

LLM-PowerHouse: Unleash LLMs' potential through curated tutorials, best practices, and ready-to-use code for custom training and inferencing.

ml-for-beginners

12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all

ml-interviews-book

https://huyenchip.com/ml-interviews-book/

ntlflowlyzer

p4-learning

Compilation of P4 exercises, examples, documentation, slides for learning or teaching

p4-mininet-tutorials

P4 language tutorials

pkt_viewer

This project aims to develop a visualization tool that is useful to identify network congestion.