iot

This is a mirror of the aioCoAP source code. For issue tracking, see its github project; CI at codeberg.

This project combines our own and well-known camera trap image classification pre-trained models under one roof. It integrates them via an ensemble model to increase the trustworthiness of the classification decision.

Thie project benchmarks various camera trap image classification models

This project aim to develop a context-aware anomaly detection mechanism/model that physically observes ICPS edge devices to detect cyberattacks: https://iotgarage.net/projects/ContextAwareSecurityforIndustrialControlSystems.html

This project aim to develop a context-aware anomaly detection mechanism/model that physically observes ICPS edge devices to detect cyberattacks: https://iotgarage.net/projects/ContextAwareSecurityforIndustrialControlSystems.html

This project implementes for low resource edge devices and performed well for extreme illumination changes such as day and nighttime. https://doi.org/10.1002/int.22471

Initiated as part of EPSRC PETRAS ResBE Project. Currently, the main repository for the ongoing development of the Smart Home Lab: https://iotgarage.net/projects/SmartHomeLab.html

This project created a dataset that captures cyber data (Network Traffic) and physical data (sensor data from primitive sensors such as temperature, humidity, motion, etc.) from a smart home with the aim of detecting complex cyber-physical anomalies. The dataset can be found here: https://dx.doi.org/10.21227/sez1-2928

This project aims to develop a Semantic Knowledge Driven On-demand Data Offerings for Quarriable Smart City Data Marketplace https://iotgarage.net/projects/SynchroniCitySmartHomeData.html

A secure IoT home security system built on the Azure Sphere that leverages on-device machine learning to detect home break-ins and provide a real-time notification to the owner via the included mobile app.