Teaching

• Computer Communications Networks
• Quantum Computation and Communication

SELECTED PUBLICATIONS

BOOKS

Active Networks and Active Network Management: A Proactive Management Framework by Stephen F. Bush and Amit Kulkarni, Kluwer Academic/Plenum Publishers, New York, Boston, Dordrecht, London, Moscow, 2001, 196 pp. Hardbound, ISBN 0-306-46560-4

• Bush, S. F. (2007), The Handbook of Computer Networks, John Wiley & Sons, chapter Active Networking, pp. 3008.
• Bush, S. F. (2004), Focus on Computer Science Research, Nova Science Publishers, Inc, chapter Complexity as a Framework for Prediction, Optimization, and Assurance, pp. 65-129.

RECENT JOURNAL PUBLICATIONS

• A Simple Metric for Ad Hoc Network Adaptation, Stephen F. Bush, IEEE Journal on Selected Areas in Communications Journal, vol. 23, no. 12, Dec 2005.
• An Active Model-Based Prototype for Predictive Network Management, Stephen F. Bush and Sanjay Goel, IEEE Journal on Selected Areas in Communications Journal, vol. 23, no. 10, Nov 2005.
• Low-Energy Network Time Synchronization as an Emergent Property, Stephen F. Bush, Proceedings of the Fourteenth International Conference on Computer Communications and Networks (IEEE ICCCN), October 17-19, 2005, San Diego, California USA.
• Genetically Induced Communication Network Fault Tolerance, Stephen F. Bush,Complexity Journal, vol. 9, issue 2, Nov-Dec 2003, Special Issue: "Resilient & Adaptive Defense of Computing Networks".
• Kolmogorov Complexity Estimates For Detection Of Viruses In Biologically Inspired Security Systems: A Comparison With Traditional Approaches (Conference Presentation), Sanjay Goel and Stephen F. Bush, Complexity Journal, vol. 9, issue 2, Nov-Dec 2003, Special Issue: "Resilient & Adaptive Defense of Computing Networks".
• Biological Models of Security for Virus Propagation in Computer Networks, Sanjay Goel and Stephen F. Bush, ;Login: Dec 2004.

SELECTED CONFERENCE PRESENTATIONS

• Graph Spectra of Carbon Nanotube Networks, Stephen F. Bush and Sanjay Goel, 1st International Conference on Nano-Networks, Lausanne, Switzerland, September 14-16, 2006.
• Enhancing Reliable Multicast Transport to Mitigate the Impact of Blockage, Stephen F. Bush, Praveen Kumar Gopala (Ohio State University, and Orhan Imer (General Electric Global Research Center), 11th International Workshop on Computer-Aided, Modeling, Analysis and Design of Communication Links and Networks, Trento, Italy, June 8-9, 2006.
• Sync and Swarm Behavior for Sensor Networks, Stephen F. Bush, Joint IEEE Communications Society and Aerospace Chapter Presentation, Rochester, NY, USA, June 7,2005. [slides]
• The Limits of Motion Prediction Support for Ad hoc Wireless Network Performance, Stephen F. Bush and Nathan Smith, The 2005 International Conference on Wireless Networks (ICWN-05) Monte Carlo Resort, Las Vegas, Nevada, USA, June 27-30, 2005.
• On The Effectiveness of Kolmogorov Complexity Estimation to Discriminate Semantic Types (Conference Presentation), Stephen F. Bush and Todd Hughes, Invited Paper: SFI Workshop: Resilient and Adaptive Defense of Computing Networks 2003, Santa Fe Institute, Santa Fe, NM, Nov 5-6, 2003.
• Extended Abstract: Complexity and Vulnerability Analysis, Stephen F. Bush, Complexity and Inference, June 2-5, 2003, DIMACS Center, Rutgers University, Piscataway, NJ, Organizers: Mark Hansen, Paul Vitányi, Bin Yu.

SELECTED PUBLICATIONS

• Molecular Communication

• Nano-Communications: A New Field? An Exploration into a Carbon Nanotube Communication Network by Stephen F. Bush and Yun Li.
• Characteristics of Carbon Nanotube Networks: The Impact of a Metallic Nanotube on a CNT Network by Stephen F. Bush and Yun Li.
• Network Characteristics of Carbon Nanotubes: A Graph Eigenspectrum Approach and Tool Using Mathematica by Stephen F. Bush and Yun Li, GE Global Research Technical Report 2006GRC023.
• Graph Spectra of Carbon Nanotube Networks by Stephen F. Bush and Sanjay Goel, Nano-Net 2006: 1st International Conference on Nano-Networks Lausanne, Switzerland, September 14-16, 2006.
• In-Line Network Management Prediction (TEXT format) (HTML format) (XML format) IETF Internet-Draft
• Active Virtual Network Management Prediction: Complexity as a Framework for Prediction, Optimization, and Assurance (Introduction to Active Networks) by Stephen F. Bush, Proceedings of the 2002 DARPA Active Networks Conference and Exposition (DANCE 2002), IEEE Computer Society Press, pp. 534-553, ISBN 0-7695-1564-9, May 29-30, 2002, San Francisco, California, USA.
• Information Assurance through Kolmogorov Complexity by Scott Evans and Stephen F. Bush and John Hershey, DARPA Information Survivability Conference and Exposition II (DISCEX-II 2001), June 12-14, 2001, Anaheim, California
• Predicting Resource Demand in Heterogeneous Active Networks by Virginie Galtier and Kevin L. Mills (NIST) and Yannick Carlinet and Stephen F. Bush and Amit Kulkarni, MILCOM, McLean, VA, October 28-31, 2001
• Complexity-Based Information Assurance by Stephen F. Bush, and Scott Evans, GE Global Research Technical Report, 20001CRD084, October, 2001
• The TRAC Mission Manager Autonomous Control Executive by Timothy L. Johnson, Hunt A. Sutherland, and Stephen F. Bush, IEEE Aerospace Conference, Big Sky, Montana, October 28-31, 2001
• Predicting and Controlling Resource Usage in a Heterogeneous Active Network by Virginie Galtier and Kevin L. Mills (NIST) and Yannick Carlinet and Stephen F. Bush and Amit B. Kulkarni, Proceedings of the Third International Workshop on Active Middleware Services, August 6 2001, pp. 35-44.
• Active Virtual Network Management Prediction by Stephen F. Bush, Trans-European Research and Education Networking Association (TERENA) 2001, Antalya, Turkey, May 14-17, 2001
• Detecting Distributed Denial-of-Service Attacks using Kolmogorov Complexity Metrics by Amit B. Kulkarni, Stephen F. Bush, and Scott C. Evans, GE CRD Technical Report 2001CRD176, December 2001.
• Active Network Management and Kolmogorov Complexity by Amit B. Kulkarni and Stephen F. Bush, OpenArch 2001, Anchorage Alaska, April 27-28, 2001
• Predicting and Controlling Resource Usage in an Active Network by Stephen F. Bush and Amit Kulkarni (GE), Virginie Galtier and Yannick Carlinet and Kevin L. Mills (NIST), Livio Ricciulli (Metanetworks), DARPA Active Networks PI Meeting, December 6-9, 2000, Atlanta, GA
• Technologies for Reliable Autonomous Control (TRAC) of UAVs by Pete Schaefer, Richard D. Colgren, Richard J. Abbott,Han Park, Amir Fijany, Forest Fisher, Mark L. James, Steve Chien, Ryan Mackey, Michail Zak,Timothy L. Johnson, Stephen F. Bush, Proceedings of the 19th Digital Avionics Systems Conference (DASC), Philadelphia, PA., 7-13 October 2000
• Active Jitter Control by Stephen F. Bush and Amit Kulkarni and Scott Evans and Luiz Galup, 7th International IS&N Conference, Intelligence in Services and Networks (ISN) '00, February 23-25, 2000, Kavouri, Athens, Greece, (PDF) Presentation (PPT)
• Islands of Near-Perfect Self-Prediction by Stephen F. Bush, Proceedings of VWsim'00: Virtual Worlds and Simulation Conference, WMC'00: 2000 SCS Western Multi-Conference, San Diego, SCS (2000) International Conference on Virtual Worlds and Simulation (VWSIM 2000), January 2000 (PDF) Presentation (PPT) (HTML)
• Active Network Management, Kolmogorov Complexity, and Streptichrons by Amit B. Kulkarni and Stephen F. Bush, GE CRD Technical Report 2000CRD107, December 2000
• Active Virtual Network Management Protocol by Stephen F. Bush, 13th Workshop on Parallel and Distributed Simulation, May 1-4, 1999, Atlanta, Georgia, USA, PADS '99 (PS)
• The Design and Analysis of the Virtual Network Configuration Algorithm by Stephen F. Bush, PhD Thesis, 1998, UMI Number 9905440
• Network Management of Predictive Mobile Networks by Stephen F. Bush, Victor S. Frost, Joseph B. Evans, Journal of Network and Systems Management, Vol. 7, No. 2, June, 1999
• A Control and Management Network for Wireless ATM Systems by Stephen F. Bush, Sunil Jagannath, Ricardo Sanchez, Joseph B. Evans, K. Sam Shanmugan and Victor S. Frost, ACM-Baltzer Wireless Networks (WINET), 1997, vol. 3, pages 267-283 WINET
• Mobile ATM Buffer Capacity Analysis by Stephen F. Bush, Joseph B. Evans and Victor Frost, 1996, vol. 1, No. 1, pages 67-73, February, ACM-Baltzer Mobile Networks and Nomadic Applications (NOMAD), ACM/Baltzer Journal on Wireless Networks Vol. 1 No. 1
• A Control and Management Network for Wireless ATM Systems by Stephen F. Bush, Sunil Jagannath, Ricardo Sanchez, Joseph B. Evans, K. Sam Shanmugan and Victor S. Frost, Presented at ICC'96
• Rapidly Deployable Radio Networks (RDRN) Architecture by Stephen F. Bush, Sunil Jagannath, Ricardo Sanchez, Joseph Evans, Victor Frost and Sam Shanmugan, TISL Technical Report 10920-09

SELECTED PROJECTS

• Fault-Tolerant Networks: Imperishable Networks
• Active Networks
• Information Warfare Vulnerability Analysis
• Information Assurance
• Brittle Systems

INTELLECTUAL PROPERTY

• United States Patent Application 20030110028

  Dialog processing method and apparatus for uninhabited air vehicles

  A dialog processing method and apparatus for uninhabited air vehicles is described. The apparatus contains a recognition unit for recognizing incoming data, an interpretation unit for interpreting the data according to a grammar and a response unit for generating an appropriate response to the incoming data. The method may utilize natural language processes and may reduce to a finite state machine. The incoming data is combined with uninhabited air vehicle state information to increase the accuracy of this interpretation. Additionally, the dialog states may be limited to customary air traffic control dialogs.
• United States Patent Application 20040156333

  System for evolutionary service migration

  A system in accordance with the present invention operates a wireless ad hoc network. The system includes a plurality of nodes and a plurality of protocols for governing transmission of data between the plurality of nodes. A first node of the plurality of nodes executes an evolutionary service migration algorithm for transferring a service from the first node to one of the remaining of the plurality of nodes.
• United States Patent Application 20040156370

  System for evolutionary adaptation

  A system in accordance with the present invention operates a wireless ad hoc network. The system includes a plurality of nodes and an active packet. The active packet implements a genetically programmed adaptation of one of the plurality of nodes in response to a change of condition of the one node of the plurality of nodes.
• United States Patent Application 20040156388

  System for maintaining quality of service

  A system in accordance with the present invention operates a wireless ad hoc network. The system includes a plurality of nodes and an active packet. The active packet maintains an optimal quality metric of a service for nodes of the plurality of nodes that utilize the service. The active packet utilizes an adaptable algorithm for achieving the optimal quality metric.
• United States Patent Application 20040157557

  System for a dynamic ad-hoc wireless network

  A system operates a wireless ad hoc network. The system includes a plurality of nodes and a plurality of packets for transmission between the plurality of nodes. The packets contain code for routing the packets between the plurality of nodes. The code adapts to a changing configuration of the plurality of nodes.
• United States Patent Application 20040250128

  System and method for network security analysis

  A system and method for network security analysis are provided, wherein points of high vulnerability in the network may be identified by evaluating the complexity of data in the network. Points of low complexity are determined to be highly vulnerable, while points of high complexity are determined to be less vulnerable.
• United States Patent Application 20050273274

  Method for identifying sub-sequences of interest in a sequence

  The present technique provides for the analysis of a data series to identify sequences of interest within the series. The analysis may be used to iteratively update a grammar used to analyze the data series or updated versions of the data series. Furthermore, the technique provides for the calculation of a minimum description length heuristic, such as a symbol compression ratio, for each sub-sequence of the analyzed data sequence. The technique may then compare a selected heuristic value against one or more reference conditions to determine if additional iteration is to be performed. The grammar and the data sequence may be updated between iterations to include a symbol representing a string corresponding to the selected heuristic value based upon a non-termination result of the comparison. Alternatively, the string corresponding to the selected heuristic value may be identified as a sequence of interest based upon a termination result of the comparison.
• United States Patent 6,697,762

  Apparatus and method for analyzing brittleness of a system

  The invention provides a computer implemented tool for analyzing the brittleness of a system with respect to at least one system performance measure. The tool comprises a statistical analyzer configured to receive at least one operating parameter input, o, and to provide at least one performance function output, f. The performance function output f provides an indication of system performance as a function of system operating parameter input o. A brittleness analyzer is coupled to the performance function output and is configured to provide a brittleness output based upon the performance function output f.
• United States Patent 6,498,968

  Optimistic distributed simulation for a UAV flight control system

  A real-time distributed model-based predictive control method having a time maintenance mechanism that solves all state synchronization problems. Method steps include generating a message that predicts a system value corresponding to a future time, and executing a control process using the system value contained within said message, resulting in a process state. The process state is then stored in a state queue. The local process time is rolled back to the latest valid local process time prior to said message if the message precedes the current process state time. Rolling back comprises retrieving the system state values at that latest valid local process time from said state queue and re-executing the control process, using the system value contained within the message, and storing the new state in the state queue. The method also includes rolling back if, upon receiving a message containing an observed system value, said observed system value differs from its predicted value by more than a predetermined tolerance. The method is carried out on a distributed processor control system.

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