fnascimento-pucrs Goto Github PK

beginning-anomaly-detection-using-python-based-dl

Source Code for 'Beginning Anomaly Detection Using Python-Based Deep Learning' by Sridhar Alla and Suman Kalyan Adari

cyberattack-detection

Cyber Attack Detection thanks to Machine Learning Algorithms

ddos-attacks-dataset

This project contains three datasets having different modern reflective DDoS attacks such as PortMap, NetBIOS, LDAP, MSSQL, UDP, UDP-Lag, SYN, NTP, DNS, and SNMP. These datasets are based on the DCIC-DDoS2019 dataset proposed by man Sharafaldin et al. (2019). After the pre-processing of the DCIC-DDoS2019 dataset, we have created three different da

distributed-python

A Flask app integrated with Celery to execute tasks over a cluster of resources/compute units.

edge-detect

Repository for IEEE CCNC'21 paper titled "Edge-Detect: Edge-centric Network Intrusion Detection using Deep Neural Network".

federated-learning-with-python

fnascimento-pucrs.github.io

ids_dataset_exploration

Exploration of many IoT Intrusion Detection System datasets.

ids_dl

A Pytorch implementation of a proof-of-concept Intrusion Detection and Prevention system

ifogsim

The iFogSimToolkit (with its new release iFogSim2) for Modeling and Simulation of Resource Management Techniques in Internet of Things, Edge and Fog Computing Environments. In the new release Mobili Management, Microservice Management, and Dynamic Clustering mechanisms are added as new features.

intrusion-detection-system

This project is a network intrusion detector that uses machine learning algorithms to distinguish between bad (intrusions/attacks) and good (normal) connections. The UI is built using Flask framework and the KDD Cup 1999 dataset is used for training. Python, Flask, and Scikit-learn are the main technologies used in this project.

iot-network-intrusion-detection-system-unsw-nb15

Network Intrusion Detection based on various machine learning and deep learning algorithms using UNSW-NB15 Dataset

iot_botnet_nids

My masters project on Botnet Network Intrusion Detection on Resource Constrained Devices for IoT Applications

iotsim-edge

jekyll-hp

A beautiful, simple, clean, and responsive Jekyll theme for academics

mastering-pytorch

Mastering PyTorch, published by Packt

nsl-kdd

PySpark solution to the NSL-KDD dataset: https://www.unb.ca/cic/datasets/nsl.html

pytorch-recipes

Source Code for 'PyTorch Recipes' by Pradeepta Mishra

time-related-intrusion-detection-model-based-on-recurrent-neural-network

Here, we use RNN to deal with the network intrusion problem. The UNSW-NB15 dataset is used.

transfer-learning-based-intrusion-detection-in-5g-and-iot-networks

Development of a transfer learning system for the detection of cyber-attacks in 5G and IoT networks. Transfer learning will improve the accuracy and precision of cyber-attacks detection in 5G /IoT networks with limited computing capability and datasets with scarce labeled data.

violet

VIoLET: A Large-scale Virtual Environment for Internet of Things