L3ogbiya

L3ogbiya's Stars

• yzhao062/pyod

  A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques

  Language:Python8.6k1.4k

• sharmi1206/network-capacity-predictor

  Sample tool to predict network traffic based on users and resource block utilization

  Language:Python21

• HawkPhantom/unsupervised-anomaly-detection

  A tool to detect anomalies in time-series.

  Language:Python1

• C4NU/Hetzner-Traffic-Alert-Bot

  Language:Python1

• KinshukBasu/CSC592_IoT_VLC

  Language:Python41

• kirkalyn13/network-monitor-backend

  Network Monitoring Dashboard App

  Language:Python2

• rajeevratan84/LTE-KPI-Anomaly-Detection

  LTE KPI Anomaly detection using time series forecasting, regression modeling, and correlation differences in KPI. Also, daily and weekly KPI degradation reports generated.

  Language:Python2

• 4Tron/Adaptive-Traffic-Signal-Control-System

  The name says everything...

  Language:Python2224

• javisys/Security-Tools-in-Python

  Several simple projects done in Python for network monitoring for Cybersecurity and high availability. All the modules needed are in their folder with the script.

  Language:Python292

• H21lab/Anomaly-Detection

  Scripts to help to detect anomalies in pcap file. Anomaly Detection using tensorflow and tshark.

  Language:Jupyter Notebook7921

• sergiovirahonda/AnomalyDetection

  This repository is part of an article about how to forecast and detect anomalies on time-series data. The main objective is to train a RNN regressor on the Bitcoin dataset to predict future values on then detect anomalies in the whole data window - that last step achieved by implementing a RNN Autoencoder. You'll see some other models in the notebooks that I've provided to you in case they are of your interest and this RNN regressor + RNN Autoencoder doesn't perform well for your purpose in any other scenario. The dataset used is available at https://www.kaggle.com/mczielinski/bitcoin-historical-data and contains BITCOIN/USD 1-minute candle data, from 2012-01-01 to 2020-12-31. I hope you can get advantage of this approach!

  Language:Jupyter Notebook117

• AmberSJones/pyhydroqc

  This software was designed with the purpose of anomaly detection and correction for time series water sensor data. This software was developed using the Logan River Observatory data set.

  Language:HTML2212

• SundaySky/cost-anomaly-detector

  Language:Python135

• Karthik-S-EC/Radiation-Pattern-of-Antennas-GUI-Python

  Plot of Radiation Pattern of Different Antennas with the number of side lobes and their amplitudes

  Language:Python17