John M. Fossaceca, Ph.D.


John M. Fossaceca, Ph.D. has 30+ years of experience in the integration and development of embedded software, applications and hardware for secure communications, next generation telecommunications and adaptive detection and sensing systems for start-up, mid-size and fortune 500 companies. Over this period he has held several senior level positions leading engineering teams. His educational background includes Electrical Engineering (B.E., Manhattan College, and M.S., Syracuse University); Business Administration (M.B.A., Virginia Polytechnic Institute and State University); Systems Engineering (Ph.D., George Washington University).
In his current role as Vice President for Technology & Product Management at cyber security company, Ultra Electronics, 3eTI, he pursues new technologies and develops product strategy to define products for industrial and commercial applications designed to protect critical infrastructure from cyber-attacks. He has held prior roles as Executive VP and COO at Comtech Mobile Datacom leading efforts to modernize CMDC’s satellite-based Blue Force Tracking system. Dr. Fossaceca let the engineering team that developed the first FIPS 140-2 and Common Criteria validated WiFi products and served as the Principal Investigator on several SBIR programs for secure wireless initiatives. Earlier in his career, Dr. Fossaceca was an Engineering Director with AT&T and Lucent Technologies in Holmdel and Murray Hill, NJ, where he contributed to products ranging from consumer telephony to next-generation packet based communication systems. He also worked in research and development for Thomson Consumer Electronics and General Electric. He has conducted research in machine learning for network intrusion detection, has authored papers in several refereed technical journals and is inventor on several patents related to adaptive detection and signal processing. Dr. Fossaceca is a member of IEEE, ISA, AFCEA and InfraGard.


Fossaceca, J. M., Mazzuchi, T. A., & Sarkani, S. (2015). MARK-ELM: Application of a novel multiple kernel learning framework for improving the robustness of network intrusion detection. Expert Systems with Applications, 42(8), 4062-4080..

Journal Refereed

Neural Computing and Applications, Springer
Information Sciences, Elsevier
Expert Systems with Applications, Elsevier