An end-to-end regression Machine Learning project with open data, multiple models, rigorous validation, and a concise stakeholder-ready report with recommendations.
Machine Learning Regression for proactive attack pattern detection in IoT networks
By predicting flow_duration from basic network telemetry in real-time IoT traffic, we can spot unusual resource use early and surface potential attack patterns before they escalate. This enables proactive capacity planning (autoscaling, QoS tuning) and faster security response, reducing downtime and operating costs while keeping connected devices reliable.
In line with SDG 9 (Industry, Innovation & Infrastructure) and SDG 16 (Peace, Justice & Strong Institutions), this approach strengthens digital infrastructure and improves cyber-resilience for services that increasingly depend on IoT.
Impact: Securing IoT networks helps keep critical infrastructure – such as smart cities, healthcare, and energy systems – safe and reliable. Concretely, this means hospital sensor networks remain stable and smart city street lighting is protected from attack-driven disruptions.
The deliverables below include:
A Github link with jupyter notebook swith the full preprocessing and ML pipeline, the used dataset and the written report for stakeholders.
