Dr. Daniel G. Schwartz received a B.A. in Mathematics from Portland State University, Portland, Oregon, in 1969, an M.S. in Mathematics from Simon Fraser University, British Columbia, Canada, in 1974, and a Ph.D. in Systems Science from Portland State University in 1981. He joined the Department of Computer Science at Florida State University in 1984.
Research
Dr. Schwartz's research has spanned mathematical logic, fuzzy logic, formal methods in artificial intelligence, and their applications. He currently is engaged in project for the US Army involving case-based reasoning and data mining for computer network intrusion detection. This has led to an interest generally in information security. Dr. Schwartz also has interests in the area of digital libraries, which in turn entails concern with databases, software engineering, and Internet applications. His recent teaching includes courses on formal logic, artificial intelligence, databases, Java, and enterprise systems programming.
Selected Publications
- Schwartz, D.G., Dynamic reasoning with qualified syllogisms, Artificial Intelligence, 93, 1-2 (1997) 103--167.
- Schwartz, D.G., Layman's probability theory: a calculus for reasoning with linguistic likelihood, Information Sciences, 126, 1-4 (2000) 71--82.
- Schwartz, D.G., Agent-oriented epistemic reasoning: subjective conditions of knowledge and belief, Artificial Intelligence, 148, 1-2 (2003) 177-195.
- Stoecklin, S., Schwartz, D.G., Yilmaz, E., and Patel, M., A metadata architecture for case-based reasoning. The 2004 International Conference on Artificial Intelligence (IC-AI'04), Las Vegas, NV, June 21--24, 2004, pp. 790--794.
- Long, J., Schwartz, D., and Stoecklin, S., Multi-sensor network intrusion detection: a case-based approach. WSEAS Transactions on Computers, 4, 12 (2005) 1768--1776.
- Long, J., Schwartz, D.G., and Stoecklin, S., Application of case-based reasoning to multi-sensor network intrusion detection. WSEAS/IASME International Conference on Computational Intelligence, Man-Machine Systems, and Cybernetics (CIMMACS'05), Miami, Florida, USA, November 17--19, 2005, pp. 260-269.
- Long, J., Schwartz, D.G., and Stoecklin, S., Distinguishing false from true alerts in Snort by data mining patterns of alerts. Data Mining, Intrusion Detection, Information Assurance, and Data Networks Conference, SPIE Defense and Security Symposium 2006, Orlando (Kissimmee), Florida, April 17--21, 2006, pp. 62410B-1--62410B-10.
- Long, J. and Schwartz, D.G., Case-oriented alert correlation. WSEAS Transactions on Computers, 7, 3 (2008) 98--112.
