In academic year 2020–21, IUP received funds in a very competitive program, NCAE-C Cyber Curriculum and Research 2020 Program, that is supported by the National Security Agency to conduct state-of-the-art
research study focusing on improving IoT systems’ security.
The title of the funded project is “Investigating Effective and Efficient Anomaly Detection on IoT Systems via a Novel Fusion of Deep Learning Techniques.” Its main goal is to develop a practical framework (that is based on a sound theoretical foundation)
for the IoT anomaly detection in order to assess and improve the impact IoT systems have on the security of various networks. To achieve the project objectives, the project will develop a prototype for simulation and evaluation of the detection algorithms.
This includes simulation and initial evaluation of the detection algorithms, as well as, corresponding data results regarding the performance and efficiency of the algorithms. Then, it will develop a physical testbed for further analysis and evaluation
of the anomaly detection scheme. This includes an outline of architecture and implementation of IoT system, see the figure below for a proposed prototype for a smart building model.
So far, work on this project has resulted in the following publications/presentations:
Waleed Farag, the project director, the two Co-PIs, Drs. Wu and Ezekiel, and all student researchers are holding regular weekly meetings since early fall 2020. In these meetings, we address outstanding challenges, report of progress, and identify and
assign research tasks for the next week. Listed below are detailed meeting documents in 2020–21 for this IoT Anomaly Detection Research Project.