PhD in Digital and Cyber Forensic Science
The PhD in digital and cyber forensic science is designed to produce professionals of the digital and cybersecurity realms with the critical-thinking, technical, and problem-solving skills and advanced discipline-specific knowledge to advance into leadership positions in business and industry as well as academia.
The program will allow students to explore the potential for forensically sound digital data capture and analysis and to develop new tools and methods for handling digital and cyber forensic evidence.
3D Printing Forensics
Read more on research areas.
Department of Defense Cyber Scholarship Program
DoD CySP sponsors students who are enrolled in or applying to universities designated as a CAE. Students selected as DoD CySP Scholars will receive the cost of tuition, books, a computer, travel support, and a stipend to cover room and board. Following graduation, students are eligible for full-time employment with various components and agencies across the DoD.
For more information on how to apply, visit the DoD CySP website.
Brain Frequency Based Evolutionary Encryption Method for IoT Devices
The security levels of existing encryption methods are becoming weaker with the increase of computational power. Because of the aforementioned situation, researchers have developed new methods with biological singularity, using fingerprints or a human face. Unfortunately, fingerprints and facial features with biological singularities can be easily manipulated or imitated. Therefore, this work creates a more secure strategy than existing encryption methods for the IoT devices using electrical signals produced by the brain. The work is built on mathematically analyzing the electrical signals created by an individual memory. There is a cryptography theory that evolves and becomes stronger with each use of the cipher, generating different signals in new experiences. At the same time, it can measure the stress created by the user at the time of danger and, unlike the existing encryption methods, can inform the security forces of all transactions.
Investigation of IndexedDB Persistent Storage for Digital Forensics
In the scope of this dissertation, the efficacy of IndexedDB for digital forensics is inspected by scrutinizing the forensic value and the best practices of extraction, processing, and presentation of artifacts. Furthermore, new techniques are suggested where IndexedDB can be utilized not only as a source of evidence, but also as a source of verification for the evidence that is collected from preceding sources of digital forensics. Accordingly, a series of single case pretest-posttest quasi experiments are constructed for population and evaluation of the artifacts in IndexedDB storage of web browsers. Subsequently, cornerstone tools are implemented to extract data from underlying storage technologies of IndexedDB. Methods of verification, linkage, and presentation are constructed over the extracted artifacts which potentially turns the extraction tools into investigation suites.
Faculty in the Department have an expertise centered around data visualizations, data analytics, machine learning, and deep learning, including different stages in a production pipeline of a complete machine learning solution for various problem domains. They are 1) data exploration, 2) building machine learning/deep learning models, 3) interpreting/explaining the learned models, 4) reporting results to the public. Students and faculty are currently working on projects related to analyzing information about climate change. For instance, there are projects that use data visualizations/data analytics to analyze data collected from soil profiles. There are also projects that use machine learning/deep learning to predict soil properties (e.g., pHs or carbon storage) from visible and near-infrared (Vis-NIR) spectra acquired from soil profiles. They are also working on projects that use deep neural networks (e.g., YOLO, R-CNN) to solve computer vision tasks (e.g., road damage detection).
A research team in the Department of Computer Science is focused on Machine Learning and Computer Vision. This topic delves into object detection of images with a variety of applications. Students and faculty have published several research papers on machine learning based methods for image processing and funded by NIH and Mayo clinic, Minnesota. In their research, machine learning techniques were used for human skin detection and to classify colonoscopy images into various classes based on severity that is widely used to detect colon cancer.
IoT system design
A research team in the Department of Computer Science is focused on IoT system design, sensor networks security, key distribution scheme, data privacy protocols, secure data transfer protocol for industrial control systems, hardware security and malware analysis for IoT systems, hardware-based distributed AI systems for anomaly detection over IoT network. The team is currently focused on the design and development of IDS for IoT network, industrial control system, and unmanned vehicle systems.
A research team in the Department of Computer Science is focused on Soft Computing, including Neural Networks, Evolutionary Computations, Fuzzy/Rough Set Theories, and Linear Matrix Inequalities. This includes Pattern Recognition and Machine Learning, Soft Computing and Natural Computation, Data Mining and Big Data Analytics, and Bioinformatics and Computational Biology. The research team has been recognized at several venues as best papers, best platform presentations, and numerous poster presentation awards.
Doctoral students in the Department are working with faculty members to design cybersecurity education programs by designing video games, virtual reality modules, and on robotics. The targeted learners are the community residents from P- 12 and adult learners. Also, our local SHSU undergraduate students and prospective educators will benefit from these programs.
Sensor Networks, Secure Time Synchronization for Wireless Sensor Networks
Due to the lack of central authority and random deployment of sensor nodes, wireless sensor networks (WSNs) are prone to security threats. In this project, faculty and students develop various methods to overcome attacks by secure and resilient time synchronization.
Visit the Department of Computer Science website for more information on research groups and topics.
Q: What are the requirements for admission?
The Ph.D. in Digital and Cyber Forensic Science is a full-time, on campus program that requires devoting a significant amount of time. Students are admitted as part of an annual cohort and have a fixed program of study in the first two years of the program.
Applicants seeking admission to the doctoral program in forensic science must submit the following directly to the Office of Graduate Admissions:
- Graduate Admissions Application.
- Application Fee.
- Bachelor's degree conferred by a regionally accredited institution in computer science, digital forensics, or a closely related field.
- Official transcript(s) from degree granting institution(s).
- Official transcripts from all colleges/universities attended.
- GPA of 3.5 or higher.
- Program Application.
- Personal statement, not to exceed 1000 words.
- Three letters of recommendation. A minimum of two letters must be from faculty who are sufficiently acquainted with the student to comment on potential for success in the doctoral program.
- Current resume or vita.
- Official GRE scores. A minimum GRE score of 300 is required for acceptance into the program. The GRE score is calculated as Verbal * 0.5 + Quantitative * 1.5.
- In some instances, a personal interview may be requested.
Q: What is the cost of enrollment?
Q: Where can I learn more about Scholarships and Financial Aid?