INI Practicum 2021
Carnegie Mellon master's students tackle problems, pilot new ideas and develop solutions in collaboration with industry partners. Practicum projects span a variety of topics in computing, mobile systems and security, and range from fundamental research to software development.
Questions? Email ini-practicum@andrew.cmu.edu
2021 Practicum Project Summaries
Security Analysis of Consumer IoT System
Sponsor: Carnegie Mellon University | Fall 2021
With the rapid increase of IoT devices, the attack surface of in-home networks has grown significantly, imposing a security risk since these devices have become a primary target for cybercriminals. Many different types of attacks could be avoided with an anomaly detection system that aims to reduce the risk of IoT device attacks. The team developed the foundation for an anomaly detection system that specifically performs data collection and device type identification in the network. To achieve this, different tools and methods were used to complete each task including Scapy (powerful packet manipulation program), nProbe (extensible NetFlow probe/collector), and Machine Learning (ML) algorithms. Training of the ML models using a decision tree classifier and random forest classifier resulted in an accuracy of 97% and 98% respectively. These results contribute to the foundation of a more complex IoT anomaly detection system that will apply an ML detection model for each identified IoT device.
SGMP: Smart Grid Management Platform
Sponsor: SLAC | Fall 2021
SLAC National Accelerator Laboratory, funded by the U.S. Department of Energy, is building a Smart Grid Management Platform (SGMP) to address to integrate, manage and streamline the operation of distributed energy devices (DERs). The team utilizes cloud, web, IoT, mobile and augmented reality technologies to help smart grid researchers, utility operators and home users to effectively collect, visualize, analyze grid energy data in order to predict, plan and optimize grid operations. The team implemented (1) the home hub software that streams home DER data to the cloud datastore, (2) a flexible authentication and authorization framework to manage access to platform resources and user frontends, (3) an efficient and flexible API service to access DERs and analyze grid data, (4) a web-based monitoring and management frontend, and (5) a mobile app with intuitive augmented reality interface for home users to view home energy information. The deployed system offers flexible & realtime data analytics for researchers, effective & customizable operation management for operators, simple visual monitoring for home users, and easy onboarding of researchers via software abstraction.
BLOSEM: Blockchain for Optimized Security and Energy Management
Sponsor: SLAC | Fall 2021
Establishing end-to-end security of critical grid infrastructure such as grid assets is one of the most challenging areas in energy infrastructure management. Compromised assets installed in the energy grid can expose the entire energy delivery system to cyber threats and risk the collapse of critical national infrastructure. This project involves working with SLAC National Accelerator Labs to enhance grid resilience against cyber attacks by developing a blockchain-based distributed, decentralized, and privacy-preserving supply-chain management system. The system is used to verify grid asset integrity through the supply chain and identify if the assets were tampered with during transit. Particularly, the system is designed to protect against clone attacks and false data injection attacks on grid assets.
Cloud-native Machine Identity Management for Authentication and Authorization
Sponsor: Venafi | Fall 2021
SLAC National Accelerator Laboratory, funded by the U.S. Department of Energy, is building a Smart Grid Management Platform (SGMP) to address to integrate, manage and streamline the operation of distributed energy devices (DERs). The team utilizes cloud, web, IoT, mobile and augmented reality technologies to help smart grid researchers, utility operators and home users to effectively collect, visualize, analyze grid energy data in order to predict, plan and optimize grid operations. The team implemented (1) the home hub software that streams home DER data to the cloud datastore, (2) a flexible authentication and authorization framework to manage access to platform resources and user frontends, (3) an efficient and flexible API service to access DERs and analyze grid data, (4) a web-based monitoring and management frontend, and (5) a mobile app with intuitive augmented reality interface for home users to view home energy information. The deployed system offers flexible & realtime data analytics for researchers, effective & customizable operation management for operators, simple visual monitoring for home users, and easy onboarding of researchers via software abstraction.
Simulation, Modeling, Visualization, Algorithms, and Data Analysis for Improving Airport Surface Operations
Sponsor: NASA | Fall 2021
The project is focused at building AI technoques to develop decision supporting tools for improving the efficiency of aircraft operations at large airports. Airport surface operations present difficul challenges due to the dynamic nature of aircraft movementsand uncertainties that arise from weather and human errors. The fast time simulator of SFO airport, comrpised of dynamic models and algorithms, is used to evaluate concepts and solutions with the ultimate goal of addresing safety and efficiency concerns at large airports.