Computer Networks

Combining elements of computer science and information technology/assurance, ICASA's computer network research covers a wide variety of research topics. Researchers seek new and innovative methods to better understand computer networks by studying their behaviors, vulnerabilities, and interactions. Areas of research include: security in distributed and grid environments, intrusion detection, malware analysis and malicious code detection, network topology, and software agent interaction and control.

Point of Contact: Dr. Srinivas Mukkamala

Computer Networks Patents

Internet monitoring and alerting system, New Mexico Technical Research Foundation, 2015.
Planck, M.W., Colbaugh, R.D., Glass, K.L., Willard, G.N., Thiess, M.D., Ackley, D.M., Pollard, I.R., Mattax, J.P., Barber, B.M. and Shepard, N.M.
U.S. Patent 9,032,518

Computer Networks Publications

Designing Intrusion Detection Systems: Architectures and Perspectives. Annual Review of Communications, Vol. 57, International Engineering Consortium, 2004
Intrusion Detection Systems using Adaptive Regression Splines. Kluwer Academic Press, 2004   S. Mukkamala, A. H. Sung, A. Abraham, V. Ramos
Knowledge Discovery in Network Audit Trails. Lecture Notes in Computer Science, Springer, 2004, in press   S. Mukkamala, A. H. Sung
On the Performance of Learning Machines for Bankruptcy Detection, 2004, Under Review   A. S. Vieira, B. Ribeiro, S. Mukkamala, J. C. Neves, A. Sung
An Ensemble of Network Forensic Models and Significant Feature Analysis, Elsevier, 2004, Under Review   S. Mukkamala, A. H. Sung
Hybrid Multi Agent Framework for Detection of Stealthy Probes. Applied Soft Computing Journal, Elsevier, 2005, In Press   S. Mukkamala, A. H. Sung, A. Abraham
Intrusion Detection Using an Ensemble of Intelligent Paradigms. Journal of Network and Computer Applications, Elsevier, 2004, In Press   S. Mukkamala, A. H. Sung, A. Abraham
Identifying Significant Features for Network Forensic Analysis Using Artificial Intelligence Techniques. In International Journal on Digital Evidence, IJDE Volume 1, Issue 4, 2003   S. Mukkamala, A. H. Sung
Feature Selection for Intrusion Detection Using Neural Networks and Support Vector Machines. Journal of the Transportation Research Board of the National Academics, Transportation Research Record No 1822, pp. 33-39, 2003   S. Mukkamala, A.H. Sung
Mining Key Features from Network Audit Trails for Intrusion Detection Systems Using Machine Learning Techniques. Criminal Intelligence Digest, Central Bureau of Investigation, 2002   S. Mukkamala
“Supervised Inductive Learning with Lotka-Volterra derived models, ” in Eighth IEEE International Conference on Data Mining (ICDM2008), Pisa, December 2008.   K. Hovsepian, P. Anselmo, and S. Mazumdar
“A Modeling-based Classification Algorithm Validated with Simulated Data,” in Winter Simulation Conference (WSC 2008), Miami, 2008. (PDF)   K. Hovsepian, P. Anselmo, and S. Mazumdar
“A framework for near real-time event characterization within the internet”, in Network Science Workshop (pp. 59-66), IEEE, 2011.   M. Planck, K. Glass, I. Lyman, and R. Colbaugh
“Application of information diffusion in social networks: Verifying the use of web/blog topology entropy as an indicator for real world impact”, in 2nd International Workshop on Network Science (pp. 164-167), IEEE, 2013.   M. Planck, C. Brock, D. Bachman, J. Fenchel, and I.L. Pollard
“Initial indicators of topic success in Twitter: Using topology entropy to predict the success of Twitter hashtags”, in 2nd International Workshop on Network Science (pp. 160-163), IEEE, 2013.   M. Planck, I.L. Pollard, C. Brock, and A. George
“Audit data reduction via Multi-step decomposition Analysis: The Multi-Stage Analytic Engine--Insider Threat”, Submitted to the IEEE Symposium on Systems Security and Privacy. [Submitted]   I. Rose, N. Felts, A. George, and M. Planck




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