Current Projects

Smart & Connected Rural Communities, PI: Dr. Cajetan M. Akujuobi

The primary goal of the Smart and Connected Rural Community planning grant is to explore how technology can be used to enhance the lives of rural residents. The City of Prairie View (CPV) is selected for this planning project for its distinctive rural nature. Rural community's unique challenges are low income, aging population,  and aging Infrastructures. This project investigates the needs and desires of Prairie View Community (PVC) residents and tailors smart tools to meet those needs. See detail here.

Cyber Security for Supervisory Control and Data Acquisition (SCADA) Systems, PI: Dr. Shumon Alam

Industrial control systems (ICS) are vital for various industries such as the electric, water, oil & natural gas, chemical, manufacturing, postal service, air traffic and many U.S. critical infrastructures.  ICS systems generally include Supervisory Control and Data Acquisition (SCADA), Distributed Control System (DCS) and Programmable Logic Controller (PLC). SCADA is generally used to control dispersed systems using centralized data acquisition and supervisory control, DCS is used to control production system within a local area where as PLC is used for controlling process at the field level. The threat to SCADA system has increased to manifolds because of the emerging IoT. Current solutions includes protections at various layers of the process and these are followed by the IT solutions such as implementing Firewall, DMZ, Proxy Servers, NAT and PAT at various network-segments. These are mainly OSI layer 3 and 4 based protections but 80% of cyber-attacks originate in application layer.  Behavior and signature based intrusion detection system is also proposed in various research to detect malicious traffic.  The existing solutions are not enough to fight the current advancement in cyber-attack.   This project will investigate cyber-attack scenarios and develop robust intrusion detection solutions for the SCADA system using stateful protocol analysis. A model will be developed using state machine to analyze state transitions in the application layer communications.

Wideband Spectrum Sensing for Cognitive Radio Networks, PI: Dr. Shumon Alam

Cognitive radio is a useful solution to improve spectrum utilizations. For future cognitive radio networks, it is very important that secondary users reliably detect spectral opportunities across a wide frequency range. Many narrowband spectrum sensing techniques have been developed but robust opportunistic spectrum access and higher throughput wideband spectrum sensing is crucial for cognitive radio systems. Various algorithms have been proposed such as the Nyquist Wideband Sensing, Sub-Nyquist Wideband sensing, Compressive Sensing-based Wideband Sensing. Nearly all sub-Nyquist technique requires sparse signal but in future the wideband signal may not be sparse in the frequency domain. This project will investigate to mitigate some of the known challenges of adaptive and cooperative wideband sensing and develop a robust wideband spectrum sensing algorithm for future cognitive radio networks.

A More Secured Friend Search Engine in Online Social Networks, PI: Dr. Na Li

A large number of applications have been developed by Online Social Networks (OSN) to entertain and serve their users. However, less attention has been paid to the possible privacy breaches when those applications are played by users in the OSNs. This project particularly researches the vulnerabilities of Friend Search Engine, one of the most popular applications developed by most of the OSNs to search for individual users’ friends. It also studies the advanced attacks of compromising OSN users’ friendship privacy through Friend Search Engine, such as collusion attacks. Investigating and understanding attack strategies will help to model malicious search behaviors so as to detect collusive attackers. The final goal of this project is to design and implement a more secured Friend Search Engine against collusion attacks. This interdisciplinary project involving both computer science and social behavior will make the scientific community deeply understand how people’s social behaviors (e.g., search behaviors) may expose other people’s privacy.