NCAE-C IoT Anomoly Detection Researcher

fig: https://www.iup.edu/cybersecurity/images/Grants/IoT_Anomaly_Detection_Research/iot-device-diagram.png

Summary (Adapted from IUP website)

Indiana University of Pennsylvania (IUP) received funding during the 2020–21 and 2021–22 academic years through the highly competitive NCAE-C Cyber Curriculum and Research 2020 Program, supported by the National Security Agency, to conduct advanced research aimed at improving the security of IoT systems. The project, titled "Investigating Effective and Efficient Anomaly Detection on IoT Systems via a Novel Fusion of Deep Learning Techniques," focuses on developing a practical framework for detecting anomalies in IoT systems to enhance network security. The project includes creating a prototype for simulating and evaluating detection algorithms, followed by developing a physical testbed for further analysis and refinement of the anomaly detection scheme.

My Contributions

Technologies Used: Python, MATLAB, Machine Learning, Scikit-learn t-SNE, Wireshark, Network Security, MultiThreading

• Utilized machine learning to detect and stop anomalous security threats on a network in real time.
• Developed python scripts, with the Scikit-learn t-distributed Stochastic Neighbor Embedding, to assist in the visualization of network data.
• Continued ongoing research from prior newly graduated students.