Published Paper Details:

This project presents novel ideas on boosting security and thus reducing the risk in Internet of Things (IoT) based on implementing Intrusion Detection Systems leveraging machine learning. The project details the designing of two IoT devices based on DHT11 sensors that are used to measure the temperature and humidity levels. These devices use datagram communication, for which data is transmitted through a Mosquitto broker using the MQTT protocol. In the server side, a machine learning model made out of Random Forest and Support Vector Machine (SVM) algorithms analyzes the information that passes it through for patterns hinting at intrusion or unusual behavior. Whenever alarm is identified from a possible security threat, notifications are there forwarded on the Telegram platform within the relevant authorities. This project integrates IoT, machine learning, and secured communications technologies together in order to enhance the robustness of connected environments that can very quickly detect threats and respond accordingly in a timely manner.