aouedions11

AirDL

aouedions11.github.io

Attack-and-Anomaly-Detection-in-IoT-Sensors-in-IoT-Sites-Using-Machine-Learning-Approaches

Attack and Anomaly detection in the Internet of Things (IoT) infrastructure is a rising concern in the domain of IoT. With the increased use of IoT infrastructure in every domain, threats and attacks in these infrastructures are also growing commensurately. Denial of Service, Data Type Probing, Malicious Control, Malicious Operation, Scan, Spying and Wrong Setup are such attacks and anomalies which can cause an IoT system failure. In this paper, performances of several machine learning models have been compared to predict attacks and anomalies on the IoT systems accurately. The machine learning (ML) algorithms that have been used here are Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). The evaluation metrics used in the comparison of performance are accuracy, precision, recall, f1 score, and area under the Receiver Operating Characteristic Curve. The system obtained 99.4% test accuracy for Decision Tree, Random Forest, and ANN. Though these techniques have the same accuracy, other metrics prove that Random Forest performs comparatively better.

awesome-federated-computing

:books: :eyeglasses: A collection of research papers, codes, tutorials and blogs on Federated Computing/Learning.

Awesome-Federated-Learning

Federated Learning Library: https://fedml.ai

behavioral-model

The reference P4 software switch

clustered-federated-learning

Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints

Federated-Learning-PyTorch

Implementation of Communication-Efficient Learning of Deep Networks from Decentralized Data

Network_Traffic_Classification

SSFL-Benchmarking-Semi-supervised-Federated-Learning

Benchmarking Semi-supervised Federated Learning

aouedions11/Federated-Learning-PyTorch

Implementation of Communication-Efficient Learning of Deep Networks from Decentralized Data

aouedions11/aouedions11.github.io

aouedions11/FedIoT

Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection

aouedions11/metrics-server

Scalable and efficient source of container resource metrics for Kubernetes built-in autoscaling pipelines.

aouedions11/ml_privacy_meter

Machine Learning Privacy Meter: A tool to quantify the privacy risks of machine learning models with respect to inference attacks, notably membership inference attacks

aouedions11/Network_Traffc_prediction

aouedions11/nomaly-based-Intrusion-Detection-Technique-for-IoT-Enabled-Smart-Cities

This study proposes a two- level classification technique for the anomaly detection-based IDS architecture for fog-edge sides. Targeted for IoT-smart city networks, the upper layer network uses a gradient boosting classifier while the lower layer network employs deep learning (DL) based on the combination of a long-short-term memory and a convolutional neural network (CNN-LSTM).

aouedions11/opacus

Training PyTorch models with differential privacy

aouedions11/open-neuromorphic

List of open source neuromorphic projects: SNN training frameworks, DVS handling routines and so on.

aouedions11/openfl

An open framework for Federated Learning.

aouedions11/PFL-Non-IID

Personalized federated learning simulation platform with non-IID and unbalanced dataset

aouedions11/zero-shot-unlearning

Official repo of the paper Zero-Shot Machine Unlearning accepted in IEEE Transactions on Information Forensics and Security

aouedions11/5G3E-dataset

aouedions11/albumentations

Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125

aouedions11/atic-frontend-app

aouedions11/awesome-graph-transformer

Papers about graph transformers.

aouedions11/deep-learning-time-series

List of papers, code and experiments using deep learning for time series forecasting

aouedions11/Failed-ML

Compilation of high-profile real-world examples of failed machine learning projects

aouedions11/FedAA

aouedions11/federated

A collection of Google research projects related to Federated Learning and Federated Analytics.

aouedions11/federated-learning-lib

A library for federated learning (a distributed machine learning process) in an enterprise environment.

aouedions11/FedGen

Code and data accompanying the FedGen paper

aouedions11/GNN_NTP

aouedions11/GradAttack

GradAttack is a Python library for easy evaluation of privacy risks in public gradients in Federated Learning, as well as corresponding mitigation strategies.

aouedions11/kedro-mlflow

A kedro-plugin for integration of mlflow capabilities inside kedro projects (especially machine learning model versioning and packaging)

aouedions11/membership-inference-machine-learning-literature

aouedions11/ML-Doctor

code for ML Doctor

aouedions11/pytorch-OpCounter

Count the MACs / FLOPs of your PyTorch model.

aouedions11/SNN_HAR

Pytorch implementation of Spiking Neural Networks for Human Activity Recognition.

aouedions11/tensorflow-deep-learning

All course materials for the Zero to Mastery Deep Learning with TensorFlow course.