Faculty Mentors in Cybersecurity

Chunsheng Xin

Professor

Department of Electrical & Computer Engineering

 

Research Areas: Wireless network security, Smart phone IoT security, privacy

Khan Iftekharuddin

Professor

Department of Electrical & Computer Engineering

Research Areas: Computer vision, face recognition, security monitoring

Michael Wu 

Director of CCSER and Professor

Department of Electrical & Computer Engineering 

Research Areas: Smart device, Internet of Things, social network security and privacy

Jiang Li

Associate Professor

Department of Electrical & Computer Engineering

Research Areas: Machine learning, intrusion detection

Brian K. Payne 

Vice Provost and Professor

Department of Sociology and Criminal Justice 

Research Areas: Cyber crime

Danella Zhao

Associate Professor

Department of Computer Science

Research Areas: UAV, Internet of Things

Sachin Shetty 

Associate Professor

Department of Modeling, Simulation and Visualization Engineering 

Research Areas: Risk assessment, cyber insurance

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Cong Wang

Assistant Professor

Department of Computer Science

 

Research Areas: Deep learning, mobile security 

Bin Hu

Assistant Professor

Department of Engineering Technology

 

Research Areas: Vehicular Networks, Safe Reinforcement Learning 

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Cesar Pinto
Jing Chen

Associate Professor

Department of Engineering Management & Systems Engineering

Research Areas: risk management in engineered systems

Assistant Professor

Department of Psychology

Research Areas: human factors, autonomous driving, human trust in automation, risk communication

Yaohang Li

Associate Professor

Department of Computer Science

Research Areas:  Machine Learning, Big Data Analysis, Parallel Computing, Computational Biology

Sample REU Projects

This is a list of sample REU projects. The actual project change every summer depending on the ongoing research of the mentor. Please refer to the research areas of mentors to select the mentors.

Radio Frequency based UAS Detection (Mentor: S. Shetty)

Rapid developments in the unmanned aerial systems (UAS) have made its usage in a variety of applications especially in military, high priority and sensitive government sites. The ability to accurately classify over-the-air radio signals will provide insights into spectrum utilization, device fingerprinting and protocol identification  These insights can help the Warfighter to constantly be informed about adversaries transmitters capabilities without their knowledge. Recently, few researches have proposed feature-based machine learning techniques to classify RF signals. However, these researches are mostly evaluated on simulated environments, less accurate, and failed to explore advance machine learning techniques. In this research, we proposed a feature-engineering based signal classification (RF-class) toolbox which implements RF signal detection, Cyclostationary Features Extraction and Feature engineering, Automatic Modulation Recognition to automatically recognize modulation as well as sub-modulation types of the received signal. To demonstrate the feasibility and accuracy of our approach, we have evaluated the performance on a real environment with an UAS (Drone DJI Phantom 4). Our initial experimental result showed that we were able to detect presence of drone signal under high SNR regimes. We would like to conduct the detection under low SNR regimes and presence of interference

Security & Privacy in Social Network Applications (Mentor: H. Wu)

Social networking is among the fastest growing information technologies, as evidenced by the popularity of such online social network (OSN) sites as Facebook, Twitter, LinkedIn, Instagram, and Google+. This project will investigate potential privacy leakage in existing social network encryption/perturbing solutions, and develop new social network graph perturbing algorithms to achieve secure and efficient data sharing.

Protection of Emerging Wireless Networks (Mentor: C. Xin)

The objective of this project is to secure the emerging wireless networks utilizing cognitive radio and dynamic spectrum access. To avoid harmful interference to primary users, cognitive radios rely on cooperative spectrum sensing to accurately detect idle channels for dynamic spectrum access. This project will develop algorithms to detect and countermeasure the spectrum sensing data falsification (SSDF) attack to cognitive radio networks. The participating student will utilize machine learning techniques to design an effective algorithm to exclude malicious sensing reports.

 Intrusion Detection by Deep Learning (Mentor: J. Li)

Intrusion detection plays a critical role in protecting modern computer systems. The objective of this project is to conduct intrusion detection by analyzing users' behavior patterns using deep learning techniques. An intrusion can be identified if a user has an abnormal behavior, through the classic machine learning approach that extracts features from user's data. The challenge is that we do not know in advance which features are informative. In this project, we will use deep learning as an automatic feature extractor for intrusion detection.

Public Discourse toward Security Agencies (Mentor: B. Payne)

 The objective of this project is to explore public discourse about federal agencies that protect our computer networks: The National Security Agency (NSA), the Federal Bureau of Investigations (FBI), and the Central Intelligence Agency (CIA). These agencies have as components of their mission the protection of computer networks and the investigation of threats to those networks. There has been public concern over the balance between protecting these networks and individual liberties. Officials must understand these concerns in order to address them effectively. This project will analyze the huge volume of Twitter data that contain variations of the terms NSA, FBI, and CIA.

Human Behavior and Security (Mentor: Jing Chen)

The objective of this project is to discover the relationship between human behavior and security. As social media, mobile devices, and cloud computing platforms become increasingly prevalent, the research and development of more effective ways to increase internet users security awareness and to encourage them to engage in secure behavior online become critical.  The REU student will design customized algorithms to conduct data analysis based on emerging big data analytics and data mining techniques, to find the relationship between human behavior and security.

Face Recognition for Security Applications (Mentor: K. Iftekharuddin)

In recent years video surveillance has been widely established in both private and public venues for security. This project plans to use a humanoid robotic platform known as NAO to accomplish complex recognition task such as face and facial expression recognition. The objective of this project is to develop a robust face and facial recognition algorithm using the recently introduced concept in the field of artificial neural network (ANN). The recognition algorithm can then be used in secure applications such as to detect potential intruders as well as identify their facial expression to measure the level of associated threat.