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1 US/UK International Technology Alliance (ITA) John Gowens ARL Collaborative Alliance Manager Jack Lemon MoD Collaborative Alliance Manager Dinesh Verma.

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Presentation on theme: "1 US/UK International Technology Alliance (ITA) John Gowens ARL Collaborative Alliance Manager Jack Lemon MoD Collaborative Alliance Manager Dinesh Verma."— Presentation transcript:

1 1 US/UK International Technology Alliance (ITA) John Gowens ARL Collaborative Alliance Manager Jack Lemon MoD Collaborative Alliance Manager Dinesh Verma & David Watson Program Managers Network and Information Sciences IBM

2 2 The ITA Vision  Creating an international collaborative research culture  Academia, Industry, Government in US and UK  Innovative multidisciplinary approaches  Developing ground-breaking fundamental science  Empower innovators  Develop understanding of the root cause of military technical challenges  Making an impact on coalition military effectiveness  Focus on key problems with a critical mass of researchers  Gain synergies from UK/US alignment  Innovative transition model A US/UK Alliance conducting collaborative research focused on improving coalition operations by:

3 3 U.S. Gov. Industry Academia U.K. Gov. INDUSTRY 9.BBNT Solutions LLC 10.The Boeing Corporation 11.Honeywell Aerospace Electronic Systems 12.IBM Research 13.Klein Associates ACADEMIA 1.Carnegie Mellon University 2.City University of New York 3.Columbia University 4.Pennsylvania State University 5.Rensselaer Polytechnic Institute 6.University of California Los Angeles 7.University of Maryland 8.University of Massachusetts INDUSTRY 8.IBM UK 9.LogicalCMG 10.Roke Manor Research Ltd. 11.Systems Engineering & Assessment Ltd. ACADEMIA 1.Cranfield University, Royal Military College of Science, Shrivenham 2.Imperial College, London 3.Royal Holloway University of London 4.University of Aberdeen 5.University of Cambridge 6.University of Southampton 7.University of York ITA Team Overview

4 4 Technical Areas 1.Network Theory  Enable the formation/operation of ad hoc coalition teams 2.Security Across a System of Systems  Fundamental underpinnings for adaptive networking and security to support complex system-of-systems 3.Sensor Information Processing and Delivery  Sensor information processing/delivery from distributed sensor networks to support enhanced decision-making 4.Distributed Coalition Planning and Decision Making  Understand and support complex human, social, and technical interactions in distributed coalition teams Goal: Enhancing distributed, secure, and flexible decision-making to improve coalition operations

5 5 International Technology Alliance in Network and Information Sciences Collaborative Alliance Managers/Consortium Managers Jay Gowens (ARL) Jack Lemon (MoD) Dinesh Verma (IBM) Dave Watson (IBM-UK) International Technology Alliance in Network and Information Sciences Collaborative Alliance Managers/Consortium Managers Jay Gowens (ARL) Jack Lemon (MoD) Dinesh Verma (IBM) Dave Watson (IBM-UK) Security Across a System-of-Systems Trevor Benjamin (Dstl) Greg Cirincione (ARL) John Mcdermid (York) Dakshi Agrawal (IBM) Security Across a System-of-Systems Trevor Benjamin (Dstl) Greg Cirincione (ARL) John Mcdermid (York) Dakshi Agrawal (IBM) Network Theory Ananthram Swami (ARL) Tom McCutcheon (Dstl) Don Towsley (U Mass) Kang-Won Lee (IBM) Network Theory Ananthram Swami (ARL) Tom McCutcheon (Dstl) Don Towsley (U Mass) Kang-Won Lee (IBM) Sensor Information Processing Tien Pham (ARL) Gavin Pearson (Dstl) Thomas La Porta (PSU) Vic Thomas (Honeywell) Sensor Information Processing Tien Pham (ARL) Gavin Pearson (Dstl) Thomas La Porta (PSU) Vic Thomas (Honeywell) Distributed Coalition Planning Jitu Patel (Dstl) Mike Strub (ARL) Nigel Shadbolt (SHamp) Graham Bent (IBM) Distributed Coalition Planning Jitu Patel (Dstl) Mike Strub (ARL) Nigel Shadbolt (SHamp) Graham Bent (IBM) Policy Based Security Management Calo, IBM Policy Based Security Management Calo, IBM Energy Efficient Security Architectures and Infrastructures Paterson, Royal Holloway Energy Efficient Security Architectures and Infrastructures Paterson, Royal Holloway Trust and Risk Management in Dynamic Coalition Environments Clark, York Trust and Risk Management in Dynamic Coalition Environments Clark, York Theoretical Foundations for Analysis/Design of Wireless and Sensor Networks Towsley, U Mass Theoretical Foundations for Analysis/Design of Wireless and Sensor Networks Towsley, U Mass Interoperability of Wireless Networks and Systems Lee, IBM Hancock, RMR Interoperability of Wireless Networks and Systems Lee, IBM Hancock, RMR Biologically-Inspired Self-Organization in Networks Lio, Cambridge Pappas, IBM Biologically-Inspired Self-Organization in Networks Lio, Cambridge Pappas, IBM Quality of Information of Sensor Data Bisdikian, IBM Quality of Information of Sensor Data Bisdikian, IBM Task-Oriented Deployment of Sensor Data Infrastructures La Porta, Penn State Task-Oriented Deployment of Sensor Data Infrastructures La Porta, Penn State Complexity Management of Sensor Data Infrastructures Szymanski, RPI Complexity Management of Sensor Data Infrastructures Szymanski, RPI Mission Adaptive Collaborations Poltrock, Boeing Mission Adaptive Collaborations Poltrock, Boeing Cultural Analysis Sieck, Klein Assoc Cultural Analysis Sieck, Klein Assoc Semantic Integration & Coalition Planning Smart, Southhampton Braines, IBM Semantic Integration & Coalition Planning Smart, Southhampton Braines, IBM

6 6 Accomplishments Key US/UK Collaborations Enabled Policy Management Sloman (Imperial) Bellovin (Columbia) Calo/Lobo (IBM-US) Biologically Inspired Techniques Lio (Cambridge) Seshan (CMU) Towsley (U. Mass) Mission Specific Sensor Network Configuration Leung (Imperial) La Porta (Penn State) Operations Analysis using Second Life Wagget (IBM-UK) US Military Academy (Graham) Semantic Battlespace Infosphere Shadbolt (Southampton) Hendler (RPI) Technical Results Attained Policy based self managed cells for coalition operations Wireless sensor network design based on human circulatory systems. Models to analyze properties of MANETs in non-asymptotic case Quality of Information calculus to improve detection methods for sensor network Lightweight scalable infrastructure for sensor information collection and dissemination Multi-player online role playing game based Paradigms to model coalition operations

7 7 Network Theory (Towsley U. Mass, Lee IBM-US) Network Theory (Towsley U. Mass, Lee IBM-US) Fundamental underpinnings for adaptive networking to support complex system-of-systems and ad hoc coalition teams Theoretical foundations for design of wireless and sensor networks (Towsley, U. Mass) Interoperability of wireless networks and systems (Hancock, RMR/Lee IBM-US) Biologically-Inspired self-organization in networks (Lio Cambridge/Pappas IBM-US) Finding hidden community structure and motifs in networks Simple biological network Lethal Slow-growth Non-lethal Unknown Mathematical models of interoperation to enable design of coalition networks Analysis of community patterns in biological networks and their applications to wireless systems. Models analyzing MANETs and performance of protocols FY08-09 Objectives

8 8 Security Across a System-of-Systems (Mcdermid York, Agrawal IBM-US) Security Across a System-of-Systems (Mcdermid York, Agrawal IBM-US) Fundamental underpinnings for adaptive security to support complex system-of-systems and ad hoc coalition teams Policy based security management (Calo, IBM-US) Energy efficient security architectures and infrastructures (Paterson, Royal Holloway) Trust and risk management in dynamic coalition environments (Murdoch, York) Fixed infrastructure free security mechanism Enablement of secure dynamic communities of interest Identity based trust management systems for MANETs FY08-09 Objectives Special-purpose Modeling Notations State Machines, ACLs, Other Config Languages Natural Language Vocabulary Battle Space Ontologies Natural Language Specifications Abstract Policies Cross-cutting Interaction Models Platform- specific Configurations Policy Specification Layer Abstract Policy Layer Concrete Policy Layer Implementation and Configuration Layer Real-time Updates

9 9 Sensor Information Processing/Delivery (La Porta Penn State, Thomas Honeywell) Sensor Information Processing/Delivery (La Porta Penn State, Thomas Honeywell) Sensor information processing and delivery from distributed multi- modal sensor systems within adaptive sensor networks Quality of Information of sensor data (Bisdikian, IBM-US) Task-oriented deployment of sensor data infrastructure (La Porta, Penn State) Complexity management of sensor data infrastructure (Szymanski, RPI) Quality of information representations to facilitate fusion at multiple levels Adaptive data infrastructures based on mission requirements and sensor- mission matching algorithms Information overload reduction techniques for military sensor networks FY08-09 Objectives Service Oriented Architecture for Sensor Networks Quality of Information (7) Deployment (8) Management (9) QoI Updated Configuration Updated Configuration Target Operating point

10 10 Distributed Coalition Planning/Decision-Making (Shadbolt Southampton, Bent IBM-UK) Distributed Coalition Planning/Decision-Making (Shadbolt Southampton, Bent IBM-UK) Planning and decision-making that takes into consideration the human, social, and technical interactions anticipated in distributed coalition teams Mission adaptive collaborations (Poltrock, Boeing) Cultural analysis (Sieck, Klein Assoc) Shared situational awareness/ Semantic Battlespace Infosphere (Waggett, IBM-UK) Improved understanding of multinational planning and decision making Agile, adaptive collaboration among humans and software agents engaged in collaborative decision-making Semantic Integration and Collaborative Planning FY08-09 Objectives

11 11 Project 1: Theoretical foundations for design of wireless and sensor networks Team –U. Mass, BBN, ARL, Imperial, Cambridge, SEA, RMR, Dstl Goal –determine fundamental performance limits in military mobile multi-hop ad hoc wireless networks. –develop robust optimization framework for the design of resource allocation algorithms in such networks. Key US/UK Collaboration –U. Mass and Imperial for cooperative diversity using MIMO antennas –SEA and U. Mass collaboration with potential visits/training experiences. Key 2006 Achievements –Analysis of power reduction attributes in cooperative diversity –Analysis of 1-D and 2-D arrays with duty-cycling Key Objectives –Analysis of Cooperative Networking –Analysis of Robust optimization of routing and rate control –Protocols for Mission Specific Network Configuration joint task with TA-3 Project 8 Military Relevance –Understanding characteristics of networks is fundamental necessity for NCO –Results will lead to better protocols for MANETs, and better network design/planning tools. Power reduction by cooperative transmission

12 12 Project 2: Interoperability of Wireless Systems and Networks Team –IBM, Honeywell, UCLA, CUNY, IBM-UK, ARL, Imperial, Cambridge, RMR, Dstl Goal –Investigate fundamental technical issues related to the interoperation of heterogeneous wireless networks and systems US/UK Collaborations –Imperial, IBM UK and IBM US for MANET monitoring –Cambridge and IBM US on Inter Domain Routing –Imperial, IBM-US and UCLA on Epidemic Data Dissemination Key Achievement for 2006 –Analysis of capacity gains using Opportunistic Spectrum Scavenging in Coalition Networks –Investigated scalable and efficient data dissemination in MANETs using a novel network coding technology, and improved data delivery ratio while reducing the overhead. –Developed a formal inter-domain meta-routing framework for multi-domain MANETs. –Extended network coding models for multi-party and multi- hop network coding. –Formulation of finite MANETs in terms of static equivalent graphs for analysis Objectives –Task 1: Network Monitoring and Troubleshooting in MANETs –Task 2: Inter-domain Wireless Routing in MANETs –Task 3: Data Delivery Using Controlled Epidemic Multicasting Military Relevance –Analysis of network characteristics of coalition environments. Each different network will have different performance characteristics, access policies, operational goals, … The different network requirements lead to different internal MANET routing mechanisms

13 13 Project 3: Biologically Inspired Self-Organization in Wireless Networks Team –IBM, CMU, U. Mass, BBN, ARL, Cambridge, RMR, Dstl Goal –Leverage millennia of evolution of biological systems to design better wireless networks US/UK Collaborations –Cambridge, CMU and IBM US working together on BioInspired Topology Control Mechanisms –Cambridge, U. Mass and BBN working together on dynamic graphs –Cambridge, U. Mass, ARL and IBM working together on organization of BioWire. Key Achievements for 2006 –Developed algorithms for identification of hidden patterns in communication graphs. –Organization of BioWire 2007 as a catalyst for biologically inspired approaches –Using the Human Circulation Model to design efficient duty- cycling wireless sensor networks Objectives –Task 1: Mobility Models for Dynamic Graphs and Information Dissemination –Task 2: MANET Topology Control Military Relevance –Develop self-organizing systems that are as resilient as biological systems. Simple biological network Lethal Slow-growth Non-lethal Unknown Small Hop Count Wireless Network 

14 14 Project 4: Policy based Security Management Team –IBM, Honeywell, Columbia, ARL, Imperial, Cambridge, CESG, Goal –Automate the process of enforcing and validating operational security policies into coalition networks. US/UK Collaborations –Imperial and IBM UK working together on developing policy analysis and refinement algorithms. –Cambridge and Columbia working together on policy enforcement in coalition MANETs. Key Achievements for 2006 –Developed architecture for self-managing secure cells in dynamic environments. –Specifications for formal representations of security policies in coalition networks Objectives –Task 1: Policy Refinement Algorithms –Task 2: Foundations for Policy Specification and Analysis –Task 3: Policy based Enablement of Secure Dynamic Communities –Task 4: Distributed Policy Enforcement for Secure Information Flows Military Relevance –Simplify compliance with security policies of coalition networks. 1. UserIntuitive notation Feedback on feasibility 2. Safety, liveness goals 3. Abstract state machine 4. Concrete state machine 5. System and concrete policies compilation analysis enforceability refinement 6. Policy negotiation

15 15 Project 5: Energy Efficient Security Architectures Team –IBM, UMD, CUNY, ARL, Royal Holloway, York, Dstl Goal –Enable security for information flows in flexible dynamic coalitions with multiple communities, dynamic node mobility and constrained power. Pioneer use of new security infrastructures, key management techniques and lightweight security mechanisms/protocols in dynamic, mobile, ad hoc military networking environments Understand interactions between security and heterogeneity in military networking environments make security an enabler rather than a hindrance for collaboration in dynamic CoIs US/UK Collaborations –RHUL, UMD and IBM US working together on applying threshold cryptography to MANETs. Key Achievements for 2006 –Developed techniques for inter-operation of entities with different trust authorities to enable dynamic coalition formation. –Developed usage models and scenarios for Identity based keying in MANET environments Objectives –Threshold approaches to building security services in MANETs –Lightweight security infrastructures for MANETs –Mechanisms enabling secure information flows Military Relevance –Efficient security protocols for better efficiency of coalition networks TATA Secure channel Authentic public parameters Alice’s ID X Info Flow

16 16 Project 6: Trust and Risk in Coalition Environments Team –IBM, UMD, ARL, York, Holloway, Cranfield, Dstl Goal –Incorporate the concept of acceptable trust in coalition operations to make security and enabler of coalition operations, as opposed to a hinderance. US/UK Collaborations –York, RHUL and IBM US working together on development of trust and risk calculus. Key Achievements for 2006 –Developed techniques for fuzzy logic based risk calculation and access control Objectives –Dynamic Distributed Risk Estimation in MANETs –Risk Calculations Military Relevance –Enable an understanding of trust and risk trade-offs in coalition operations Initial Bootstrapping Adversary Model Adversary Model Adversary Models Trust Algebra To trust or not to trust? That is the question Trust level is high Armed Person Approaching

17 17 Project 7: Quality of Information in Sensor Networks Team –IBM, Honeywell, UCLA, CUNY, UMD, ARL, Imperial, Dstl Goal –Develop technologies to describe, analyze and estimate the quality of information delivered by a sensor network. How good is the sensor information and how is it affected by network and sensor characteristics US/UK Collaborations –Imperial and Honeywell working together on development of Impact of routing and energy on QoI. –Imperial and IBM working together on QoI Calculus Key Achievements for 2006 –Developed statistical and physical model techniques for in-network blind calibration. –Analysis of relationship between QoI and Sensor Sampling Policies –Impact of routing on timeliness of information Objectives –QoI Specification and Analysis Framework –Sensor characteristics and QoI –QoI and Network Services –QoI Calculus for Event Detection Military Relevance –Improvements in the quality of information delivered by the sensor network infrastructure Contextual or multiscale information Another modality on the same node Nodes of Same Altitude or Depth Proximate Nodes Measurements at same time previous day Recent Measurements

18 18 Project 8: Mission Oriented Sensor Configuration Team –Penn State, CUNY, IBM, ARL, Aberdeen, Imperial, IBM-UK, Dstl Goal –Develop technologies to capture mission requirements and to configure, provision and optimize sensor information fusion infrastructure to best support the mission requirements. US/UK Collaborations –IBM UK and IBM US working on applying message fabric infrastructure to sensor networks. Key Achievements for 2006 –Developed algorithms for optimal assignment of sensors to missions –Pioneered use of message queue infrastructure for sensor information processing Objectives –Sensor Mission Matching –Mission Specific Network Configuration –Direction and Dissemination Military Relevance –Optimal use of resources to get “best” and most important intelligence in a timely manner to the right parties Mission Operation Task Component System Platform Capability Capability requirements to perform tasks to standard under given conditions

19 19 Project 9: Complexity Reduction of Sensor Deployments Team –RPI, IBM, CUNY, ARL, Aberdeen, Southampton, IBM-UK, Dstl Goal –Develop technologies to capture mission requirements and to configure, provision and optimize sensor information fusion infrastructure to best support the mission requirements. Key Achievements for 2006 –Developed paradigm for sensor as a distributed network database –Development of opportunistic routing mechanisms for sensor networks Objectives –User Oriented Information Processing and Retrieval Paradigms –Semantically Mediated Data Fusion –Root Cause Analysis and Overload Protection Military Relevance –Simplify the management and interpretation of sensor information by the warfighter during tactical operations. Service Composition Optimized Deployment Component Discovery Mission Tasking Process Choreography Tactical Information Sensor Tasking Integration (Enterprise Service Bus) QoS Layer (Security, Management & Monitoring Infrastructure Services) Data Architecture (meta-data) & Business Intelligence Governance Sensors Systems domain Network domain CCCC CCC CMC Central Mission Control Central Banking Authority Budget Allocations Mapping of Missions Into Budget Decisions Mission Commanders Bids for services Allocation Decisions Bidding Strategies Allocation Policies Sensor Networks SN

20 20 Project 10: Mission Adaptive Collaborations Team –Boeing, CMU, CUNY, ARL, Aberdeen, IBM-UK, Dstl Goal –Develop and validate a theory for agile, adaptive collaboration among humans and software agents. US/UK Collaboration –Aberdeen and CMU have significant rotation and cross- collaborative activities –IBM UK in significant studies with US Military Academy, West Point Key Achievements for 2006 –Second Life Metaverse system based validation for Recognition Primed Decision Model –Analysis and Models of of Variability in Complex Collaborative Processes Objectives –Models of Hybrid Human Agent Teams: Agent support for ad hoc adaptive teamwork Perform task analysis of military tasks Develop models of hybrid human-agent teamwork Develop agent technologies to implement the models –Computer Mediated Social Interactions Establish game/simulation environments where people and agents can collaborate Develop analysis methods that reveal team activities and context Military Relevance –Enable the war fighters in coalition to understand when and how to collaborate and use software assistance for improved effectiveness. Analyze Military Task Develop Reasoning Model Agent implementing Reasoning Model Evaluate Accuracy Of Model Validate with Human Team

21 21 Project 11: Cultural Analysis Team –Klein, Columbia, Boeing, ARL, Cranfield, IBM-UK, SEA, Dstl Goal –Understand the differences in cultural behavior between US and UK and mitigate the frictions of culture in coalition operations –advance the state of the art in cultural analysis in cognition, language, social interaction to improve coalition operations. Key Achievements for 2006 –New methodology for cultural network analysis was developed Objectives –Cultural modelling of Planning and Intent –Analysis of culturally dependent communication patterns Military Relevance –mitigates the friction of culture in coalition operations.

22 22 Project 12: Shared Situational Awareness Team –Boeing, RPI, Honeywell, Klein, Southamton, IBM-UK, Dstl, Goal –Develop technologies and techniques to improve coalition interoperability, information exploitation, shared understanding and collaborative planning through semantic integration, improved information representation and formal plan representation. US/UK Collaboration –Southampton and RPI working together on semantic technologies –Boeing and IBM-UK working on collaborative planning model Objectives –Semantic Integration and Interoperability –Plan representation with collaborative planning model Military Relevance –Improved situational awareness and better planning tools. Task 1 Task 2 Information RepresentationShared Understanding Semantic IntegrationInformation Exploitation Information ExchangeCommunication & Collaboration Integrative Framework for Semantic Integration Rules (Adaptive Selection, Automatic Parameterization) Empirical Evaluation Semantic Integration Techniques MAFR A GLUEPROM PT Others

23 23 Project 3 Biologically Inspired Self-Organization in Wireless Networks Champion: Pietro Lio, Cambridge and Vasilieos Pappas, IBM CMU Roke Manor Research Ltd University of Cambridge IBM Research BBN

24 24 Project 3 Team US –Academia Srini Seshan, CMU Don Towsley, Jim Kurose, U. Massachusetts –Industry Vasilieos Pappas, Kang-won Lee, Asser Tantawy, IBM Prithwish Basu, BBN –Government Ananthram Swami, ARL UK –Academia Pietro Lio, Jon Crowcoft, Cambridge –Industry Mark West, RMR –Government Abigail Solomon, Tom McCutcheon, Dstl

25 25 Project 3 Overview Goal –Leverage millennia of evolution of biological systems to design better wireless networks US/UK Collaborations –Cambridge, CMU and IBM US working together on BioInspired Topology Control Mechanisms –Cambridge, U. Mass and BBN working together on dynamic graphs –Cambridge, U. Mass, ARL and IBM working together on organization of BioWire. Key Achievements for 2006 –Developed algorithms for identification of hidden patterns in communication graphs. –Organization of BioWire 2007 as a catalyst for biologically inspired approaches –Using the Human Circulation Model to design efficient duty-cycling wireless sensor networks Objectives –Task 1: Mobility Models for Dynamic Graphs and Information Dissemination –Task 2: MANET Topology Control Military Relevance –Develop self-organizing systems that are as resilient as biological systems.

26 26 Project 3 Achievements Key Collaborations Enabled  Cambridge (Lio), CMU (Seshan) and IBM US (Pappas) --- bio-inspired topology control mechanisms  U. Mass (Towsley), Cambridge (Crowcroft/Lio), and BBN (Redi) --- dynamic graphs Biologically-inspired techniques for resilient self-organizing networks ITA-Sponsored Biowire 2007 Workshop  Focus on bio-inspired design of wireless networks  Organized by ARL-Dstl-IBM-Cambridge with over 50 confirmed speakers  University of Cambridge, 2-5 April Using the Human Circulation Model to design efficient duty-cycling wireless sensor networks

27 27 Mobility Models for Dynamic Graphs Problem –What are representative models for dynamic graphs representing MANETs? Hypothesis –Dynamic graphs representing MANETs represent topology patterns and information dissemination models that are isomorphous to those found in epidemic spread of viruses. Validation of Hypothesis –Obtain traces of mobility of dynamic wireless networks from U. Massachusetts DieselNet Infrastructure –Obtain mathematical models representing movement and information dissemination patterns. –Compare patterns to those obtained from epidemiology patterns found in Cambridge research efforts. –Determine Similarities and Differences If hypothesis can be validated –Apply distributed models of epidemic propagation to disseminate information in military networks. Simple biological network Lethal Slow-growth Non-lethal Unknown Small Hop Count Wireless Network 

28 28 MANET Topology Control Problem –How can we develop a good topology representing the structure of MANETs and wireless sensor Network? Assertion –Synchronization Pulses created by Circulatory Systems provide a good approach for energy-efficient duty-cycling in wireless sensor networks. –Models for epidemiological propagation and assembly of circulatory systems provides mechanisms for distributed self- organization Approach –Develop a network design algorithm modeled after circulatory system. –Obtain mathematical models representing growth of biological networks. –Adapt biological models to analyze topology formation in MANETs and compare effectiveness to non-biological approaches. e.g. Ant Colony Optimization Heart Cells Artery Vein Capillary Blood flow Lungs Mammalian Circulatory System

29 29 Security Across a System-of-Systems (Mcdermid York, Agrawal IBM-US) Security Across a System-of-Systems (Mcdermid York, Agrawal IBM-US) Fundamental underpinnings for adaptive security to support complex system-of-systems and ad hoc coalition teams Policy based security management (Calo, IBM-US) Energy efficient security architectures and infrastructures (Paterson, Royal Holloway) Trust and risk management in dynamic coalition environments (Murdoch, York) Fixed infrastructure free security mechanism Enablement of secure dynamic communities of interest Identity based trust management systems for MANETs FY08-09 Objectives Special-purpose Modeling Notations State Machines, ACLs, Other Config Languages Natural Language Vocabulary Battle Space Ontologies Natural Language Specifications Abstract Policies Cross-cutting Interaction Models Platform- specific Configurations Policy Specification Layer Abstract Policy Layer Concrete Policy Layer Implementation and Configuration Layer Real-time Updates

30 30 Columbia University Imperial College University of Cambridge Project 4 Policy based Security Management Champion: Seraphin Calo, IBM Honeywell Aerospace Electronic Systems IBM Research

31 31 Project 4 Team US –Academia Steve Bellovin, Columbia –Industry Seraphin Calo, Jorge Lobo, IBM Thomas Markham, Honeywell –Government Greg Cirincione, ARL UK –Academia Jon Crowcoft, Cambridge Morris Sloman, Emil Lupu, Imperial –Government Chris Lloyd, CESG

32 32 Project 4 Overview Goal –Automate the process of enforcing and validating operational security policies into coalition networks. US/UK Collaborations –Imperial and IBM UK working together on developing policy analysis and refinement algorithms. –Cambridge and Columbia working together on policy enforcement in coalition MANETs. Key Achievements for 2006 –Developed architecture for self-managing secure cells in dynamic environments. –Specifications for formal representations of security policies in coalition networks Objectives –Task 1: Policy Refinement Algorithms –Task 2: Foundations for Policy Specification and Analysis –Task 3: Policy based Enablement of Secure Dynamic Communities –Task 4: Distributed Policy Enforcement for Secure Information Flows Military Relevance –Simplify compliance with security policies of coalition networks.

33 33 Policy Life Cycle

34 34 Policy Operation in Coalitions Commanders Specifies Operational Policies Commanders Specifies Operational Policies US: Share Mission-Critical Information on Need to Know Basis UK: Isolate Coalition Traffic from UK only traffic Policy System Translates Operational Policies Into Machine-Readable Operational Policies Policy System Translates Operational Policies Into Machine-Readable Operational Policies Policy System Analyzes Operational Policies for conflicts/errors Policy System Analyzes Operational Policies for conflicts/errors Policy System Refines Operational Policies Into Deployable Policies Policy System Refines Operational Policies Into Deployable Policies Policy System Distributes Deployable Policies to MANET Devices Policy System Distributes Deployable Policies to MANET Devices Enforce Policies MANET Devices Enforce Policies Enforcement Support, Black Box device Capture US: XML Representation UK: XML Representation UK: Isolation Not Feasible: require additional Comm. Vehicle US: Policy possible with known configuration US: CIM-SPL/XACML representation of access control, Encryption and communication policies UK: CIM-SPL representation of Comm Equipment Access Filters A distributed messaging system designed for MANETs Policy System Validates Compliance In Post-Mortem Policy System Validates Compliance In Post-Mortem Compare Black Box and Monitored Information to Policy Policy System updates Devices to Policies Effective Post Operation Policy System updates Devices to Policies Effective Post Operation Enforcement Support, Black Box device Capture

35 35 Research Challenges Definition Translation Analysis Refinement Deployment Enforcement Audit Reset Formal RepresentationsAvailable for some Policy Models Refinement Algorithms Deconflicting Algorithms Refinement for Security Policies Coalition Compatibility Analysis Deployment in MANET environments Available for Wired Environments C&N CTA MANET Infrastructure Enforcement with Power Constraints Incorporating Experiences Enabled by Refinement Red – Unsolved Research Problems Green – Known in State of Art Policy Models and Languages Policy Auditors & Validators Enforcement understood in wired networks Available for some domains Task 1 Task 4 Task 2 Task 3

36 36 Policy Refinement Goals: Establish a layered policy model to reason about dynamic security policies. Develop algorithms for refinement of policies between levels. A layered policy model has been formulated with four levels – Specification, Abstract, Concrete, and Executable. At each layer –policies need to be represented in a suitable notation Transformation procedures –map between the policies between layers Policy Specification In Natural Language Subclasses (NLS) In a Formal Language (FL) System Side Algorithms & Tools User Side Author NL policies Convert NL policies to FL policies Author FL policies Convert FL policies to NL policies Abstract Policy Models Privacy / Security Ontologies Policy Transformation Policy Synchronization Goals, High Level Policies In System Context Concrete Policy Sets Executable Policies Information Control Flow Policy Ratification Policy Authoring Policy Ratification Databases, XML Stores, Rule Engines, State Machines, etc Large Scale Analyses of NL and FL Policies Survey & Coding of Related Practices Policy Transformation Policy Synchronization Human Factors Based Design & Usability Studies Policy Presentation Processing & User Interaction User Preference s in a FL User-Level Paradigms for Preferences Preference Specification Tools AC & Audit Policies Data User Risk Choices & Model Model Model Consent

37 37 Theoretical Foundations for Policy Specification and Analysis Analyze Policies at different levels –Algorithms for feasibility and analysis –Determination of deployability and enforceability –Applicability analysis –Conflict removal and negotiation across domains 1. UserIntuitive notation Feedback on feasibility 2. Safety, liveness goals 3. Abstract state machine 4. Concrete state machine 5. System and concrete policies compilation analysis enforceability refinement 6. Policy negotiation

38 38 Policies to Enable Secure Dynamic Community Establishment Objective –define policy-based algorithms for establishing communities of mobile entities Develop Algorithms for –policy deployment in response to changing conditions in dynamic communities –revocation of non-relevant or unsafe policies. –Discovery, authentication and role-assignment of network elements –Self-management and self-protection –negotiation of trust relationships

39 39 Distributed Policy Enforcement for Secure Information Flows D evelop dynamic, distributed security mechanisms for information flows. Adapt network structures to optimize information dissemination. Schemes for optimizing flows based upon the filtering requirements of end nodes in the system. Aggregation of secure flows to minimize transmissions Development of an abstract policy algebra for distributing security enforcement throughout a MANET. Exploit Policy Algebra for deployment and enforcement Outside A BC Let C(Pi) be the cost of a policy being installed at node i. Switch configuration if C(PA) < C(PB) + C(PC) Let R(Pi) be the risk function of Policy P at node i. If R(Pi) > L, reject policy deployment

40 40 IBM Research City University of New York Roke Manor Research Ltd. Royal Holloway University of London Project 5 Energy Constrained Security Mechanisms for MANETs Champion: Kenny Paterson, Royal Holloway University of Maryland University of York

41 41 Project 5 Team US –Academia Jonathan Katz, UMD Kent Boklan, CUNY –Industry Pankaj Rohatagi, Tal Rabin et. al. IBM –Government Richard Gopaul, ARL UK –Academia Kenny Paterson, Stephen Wolthusen, RHUL John Mcdermid, John Clark, John Murdoch, York –Government Helen Phillips, Dstl

42 42 Project 5 Overview Goal –Enable security for information flows in flexible dynamic coalitions with multiple communities, dynamic node mobility and constrained power. Pioneer use of new security infrastructures, key management techniques and lightweight security mechanisms/protocols in dynamic, mobile, ad hoc military networking environments Understand interactions between security and heterogeneity in military networking environments make security an enabler rather than a hindrance for collaboration in dynamic CoIs US/UK Collaborations –RHUL, UMD and IBM US working together on applying threshold cryptography to MANETs. Key Achievements for 2006 –Developed techniques for inter-operation of entities with different trust authorities to enable dynamic coalition formation. –Developed usage models and scenarios for Identity based keying in MANET environments Objectives –Threshold approaches to building security services in MANETs –Lightweight security infrastructures for MANETs –Mechanisms enabling secure information flows Military Relevance –Efficient security protocols for better efficiency of coalition networks

43 43 Threshold Cryptography for Survivable MANET Infrastructures What are the cryptographic solutions for complex situations which have –Low interaction and low computation –Adaptive thresholds –Require graceful degradation with number of compromised nodes –Distributed key management with provable signing properties

44 44 Lightweight Security Infrastructures for MANETs Goal: –Investigate alternative security infrastructures for MANETs Approach using ID-PKC and CL-PKC –Public keys derived directly from system identities (e.g. an IP address). –Private keys generated and distributed to users by a Trusted Authority (TA) using a master key. –Allows encryption without certificates or directories TATA Secure channel Authentic public parameters Alice’s ID Challenges –How to perform Namespace/identifier selection for scalability and interoperability –How to develop distributed trust authorities

45 45 Secure Information Flow Goal: Develop mechanisms for secure information flows in MANETs Understand trade-off between availability and protection in presence of compromised nodes: Challenges: –What is the right security metadata semantics for MANETS –How can one handle uncertainty in labels and data transformation –What are the efficient metadata transmission methods. –How does one detect and react to breaches in metadata integrity Wired or Satellite Infrastructure X Info Flow

46 46 IBM Research Cranfield University Royal Holloway University of London Project 6 Trust and Risk Management for MANETs Champion: John Clark, York University of Maryland University of York

47 47 Project 6 Team US –Academia Virgil Gligor, UMD –Industry Dakshi Agrawal, Josyula Rao et. al. IBM –Government Natalie Ivanic, ARL UK –Academia Kenny Paterson, Shane Balfe, RHUL John Mcdermid, John Clark, John Murdoch, York Howard Chivers, Cranfield University –Government Olwen Wirthington, Dstl

48 48 Project 6 Overview Goal –Incorporate the concept of acceptable trust in coalition operations to make security and enabler of coalition operations, as opposed to a hinderance. US/UK Collaborations –York, RHUL and IBM US working together on development of trust and risk calculus. Key Achievements for 2006 –Developed techniques for fuzzy logic based risk calculation and access control Objectives –Dynamic Distributed Risk Estimation in MANETs –Risk Calculations Military Relevance –Enable an understanding of trust and risk trade-offs in coalition operations

49 49 Risk and Trust Estimation in MANETs: Technical Approach Goals –Define trust that is usable in MANETs Advanced probabilistic mechanisms for computing trust Algebra for composing trust in an adhoc network – Develop Feedback mechanism/learning algorithms for adjusting recommendation weights Subactivities – Develop formal adversary models models of adversary behavior are a crucial factor in determining risk. – Develop trust calculus and logic Provides a way to combine and computer metrics for trust – Develop boot-strapping protocols and mechanisms How does one establish trust in the beginning How can initialization steps be simplified and more efficient. Initial Bootstrapping Adversary Model Adversary Model Adversary Models Trust Algebra To trust or not to trust? That is the question Trust level is high Armed Person Approaching

50 50 Risk Information, Policy & Decision Support Goal: Algorithms and Mechanisms to –Determine factors affecting risk decisions –Determine information needed for risk decisions –Handle lack of required information –Formulate risk policy formulation and control its evolution –Express risk data to make it usable SubActivities –Extension of fuzzy logic risk calculation mechanisms to uncertain environments and risk modulating factors. –Automatic Inference of risk policy and its evolution with additional factors –Presentation of risk – How to incorporate factors of timeliness, operational effectiveness and security exposure when summarizing risk of an activity

51 51 Risk Calculation Estimated Risk Estimated Loss (disclosure)  Estimated Probability (disclosure) MLS COMPUTES PROBABILITY AS BINARY OUR NEW APPROACH COMPUTES NON-BINARY PROBABILITY  COMPUTES non-binary P1 based on object level (value) and subject level (trustworthiness)  COMPUTES non-binary P2 based on fuzzy subject and object category membership. BINARY: SUCCUMBING TO TEMPTATION BINARY: INADVERTENT DISCLOSURE RISK VS. POTENTIAL JOB REQUIREMENTS NON BINARY TRADEOFF: INADVERTENT DISCLOSURE RISK VS. JOB REQUIREMENTS SUCCUMBING TO TEMPTATION

52 52 Honeywell Aerospace Electronic Systems University of California Los Angeles Imperial College, London Project 7 Quality of Information of Sensor Data Champion: Chatschik Bisdikian, IBM University of Maryland IBM Research CUNY

53 53 Project 7 Team US –Academia Mani Srivastava, UCLA Ping Ji, CUNY Anthony Ephremides, UMD –Industry Chatschik Bisdikian, Pankaj Rohatagi, IBM Vicaraj Thomas, Jim Richardson, Yunjung Yi, Honeywell –Government Tien Pham, ARL UK –Academia Duncan Gillies, Erol Gelenbe, Imperial –Government Robert Young, Dstl

54 54 Project 7 Overview Goal –Develop technologies to describe, analyze and estimate the quality of information delivered by a sensor network. How good is the sensor information and how is it affected by network and sensor characteristics US/UK Collaborations –Imperial and Honeywell working together on development of Impact of routing and energy on QoI. –Imperial and IBM working together on QoI Calculus Key Achievements for 2006 –Developed statistical and physical model techniques for in-network blind calibration. –Analysis of relationship between QoI and Sensor Sampling Policies –Impact of routing on timeliness of information Objectives –QoI Specification and Analysis Framework –Sensor characteristics and QoI –QoI and Network Services –QoI Calculus for Event Detection Military Relevance –Improvements in the quality of information delivered by the sensor network infrastructure

55 55 QoI Specification and Analysis Framework Goal: –A formalization of QoI that allows applications to query and reason about sensors, information processing functions and information flows in terms of their QoI attributes. SubActivities –Application Selection –Application Formalization –Application Characterization –QoI Abstraction and Characteristics Provenance QoI TimelinessAccuracyIntegrity

56 56 Sensor Characteristics and QoI Goal: To understand –What factors affect the integrity of sensor measurements? –How can we formally model their impact? –What is the impact of integrity of an individual sensor on the eventual post- fusion QoI? –How to detect failure of sensor integrity? –How to be resilient to such sensor integrity failure? Approach –Modeling Sensor misbehavior –Multivariate Analysis to detect incipient sensor faults –Model based data cleaning –Sensor Data Querying with in-network fault detection and diagnosis –Understanding Integrity of sensor Information Contextual or multiscale information Another modality on the same node Nodes of Same Altitude or Depth Proximate Nodes Measurements at same time previous day Recent Measurements

57 57 QoI and Network Services Goals –How to model impact of network behavior on QoI? Subactivities –How to exploit sensor proximity information to improve QoI? –How to design congestion control protocols that provide a given level of QoI –Source quenching mechanisms to ensure a degree of redundancy in networks. –How to obtain time-synchornization and localization in sensor networks Primal data flows/streams (raw data) Aggregated data flows/streams Derived data flows/streams …… Sensor Network A (dataType_A) Sensor Network N (dataType_N) Data collection “layer”Aggregation layer aggregation point A aggregation point N … …… Inference layer Derived high level knowledge … … …… Decision maker(s)

58 58 QoI Calculus for Event Detection Goal: –Determining QoI from event detection information Approach –Hypothesis Testing based on fusion of several event signatures Which event hypothesis is true and map it to accuracy. –Determine sampling rate to accurately detect event signature How less frequent than Nyquist frequency can one go. event characte ristics event characte ristics sensor operationa l characteri stics sensor operationa l characteri stics QoI signal sample measurements sensor module (sampler) fusion module signal s(t) decide whether event E has occurred based on the measured signal samples E?E? connectivity noise n(t) fast-decaying signal slow-decaying signal

59 59 IBM Research Pennsylvania State University IBM UK University of Aberdeen Project 8 Task Oriented Deployment of Sensor Infrastructure Champion: Thomas La Porta, Penn State University City University of New York Imperial College, London

60 60 Project 8 Team US –Academia Thomas La Porta, Penn State Ted Brown, Amotz Bar-Noy, CUNY –Industry Archan Misra, IBM –Government Raju Damarla, ARL UK –Academia Alun Preece, Aberdeen Kin Leung, Imperial –Industry Andy Stanford-Clark et. al., IBM –Government Stuart Colley, Dstl

61 61 Project 8 Overview Goal –Develop technologies to capture mission requirements and to configure, provision and optimize sensor information fusion infrastructure to best support the mission requirements. US/UK Collaborations –IBM UK and IBM US working on applying message fabric infrastructure to sensor networks. Key Achievements for 2006 –Developed algorithms for optimal assignment of sensors to missions –Pioneered use of message queue infrastructure for sensor information processing Objectives –Sensor Mission Matching –Mission Specific Network Configuration –Direction and Dissemination Military Relevance –Optimal use of resources to get “best” and most important intelligence in a timely manner to the right parties

62 62 Sensor Mission Matching Find an assignment of sensors to missions that (i)maximizes number of satisfied missions (max number) (ii)maximizes number of missions according to strict priority (iii)maximizes the sum of demands of satisfied missions (max utility) (iv)maximizes the sum of values of satisfied missions (max utility) (v)minimizes the sum of demands minus the utility of assigned sensors (partial satisfaction) (ongoing) Sensor field at rest - Low priority event Sensor field at rest - High priority event Sensor field at rest

63 63 Sensor Mission Matching Approach Define mission representation –Starting point: US and UK approaches, for which we have access to documentation Define a formal Sensor Ontology in a standard representation language (e.g. OWL) Define algorithms and protocols to optimally assign sensors to mission –Semantic reasoner to determine “best” data sources –Distributed algorithms to assign deployed sensors Matching Phase I: Semantic Matching (Fitting) Select best set of sensor for a mission Phase II: assigning specific sensors Consider case where one sensor can service more than one mission (simultaneous or TDMA) Consider multi-modal sensors Consider incremental sequence of missions, where there is a cost for switching form the current assignment Consider the delay/cost of implementing a distributed solution Mission Operation Task Component System Platform Capability Capability requirements to perform tasks to standard under given conditions

64 64 Mission Specific Network Configuration (joint with TA1) Problem statement: consider all aspects of mission-specific network configuration, from initial node deployment to network configuration to sensor output adaptation, that require dynamic adaptation over the lifetime of multiple, competing, concurrent missions Approach: Initial node deployment – geometric considerations of initial node placement considering both sensing and communication – consider realistic environments with non-accurate placement, obstacles, etc. Apply Network Utility Maximization (NUM) framework – Provides dynamic tuning of sources – Sources set rates to maximize their utility based on a “price” the network charges – Routers set prices based on their current level of congestion  Must be adapted to WSNs Network reconfiguration – Enable new nodes (sensors) and links to accommodate traffic – Consider bandwidth (interference) limitations

65 65 Direction and Dissemination Problem statement: Data must be delivered efficiently to those that need it (e.g., soldiers, analysts, other sensors) within a time constraint in the face of varying network conditions and mobility Approach – Information Filters Only necessary information is sent to consumers Achieved by user feedback and machine learning Modularized according to roles on a team Real-time, time varying information filters –Eliminate information that is not useful given delivery constraints – Define temporal and priority relationships between data items mission and consumer specific – Delivery schedule Consider mobility of consumers and sources, varying network bandwidth, intermittent connectivity Local dissemination protocols –Used for passing network state information and local observations between sensors Push schedules for direction –Provide missions descriptions and assignments to sensors

66 66 IBM Research City University of New York IBM UK University of Aberdeen 4 Project 9 Complexity Management of Sensor Data Infrastructure Champion: Boleslaw Szymanski, RPI Rensselaer Polytechnic Institute University of Southampton

67 67 Project 9 Team US –Academia Boleslaw Szymanski, UCLA Abbe Moskowitz, CUNY –Industry Mandis Beigi, Dinesh Verma, IBM –Government Lance Kaplan, ARL UK –Academia Alun Preece, Aberdeen Mark Nixon, Southampton –Industry Graham Bent et. al., IBM –Government Matt Brown, Dstl

68 68 Project 9 Overview Goal –Develop technologies to capture mission requirements and to configure, provision and optimize sensor information fusion infrastructure to best support the mission requirements. Key Achievements for 2006 –Developed paradigm for sensor as a distributed network database –Development of opportunistic routing mechanisms for sensor networks Objectives –User Oriented Information Processing and Retrieval Paradigms –Semantically Mediated Data Fusion –Root Cause Analysis and Overload Protection Military Relevance –Simplify the management and interpretation of sensor information by the warfighter during tactical operations.

69 69 User Oriented Information Processing and Retrieval Paradigms Goal: –To provide a comprehensive approach to the full spectrum of interactions of commanders and analysts with the sensor networks and their data Sub activities: –Service Oriented Architecture for Sensor Networks: to enable an easy integration of sensor data with other sources data sources –Sensor Networks as a Distributed Database to extends well-known and well- understood paradigm to sensor data retrieval. – Market-based sensor network tasking: facilitates distributed assignment of sensor networks to tasks by coalition commanders optimizes the current set of executed missions Service Composition Optimized Deployment Component Discovery Mission Tasking Process Choreography Tactical Information Sensor Tasking Integration (Enterprise Service Bus) QoS Layer (Security, Management & Monitoring Infrastructure Services) Data Architecture (meta-data) & Business Intelligence Governance Sensors Systems domain Network domain CCCC CCC CMC Central Mission Control Central Banking Authority Budget Allocations Mapping of Missions Into Budget Decisions Mission Commanders Bids for services Allocation Decisions Bidding Strategies Allocation Policies Sensor Networks SN

70 70 Semantically Mediated Data Fusion Goal: –Extend data fusion by incorporating trust and uncertainly in a semantically- mediated framework Approach 1. Trust Modelling Yu and Singh FIRE Travos 2. Data Fusion Bayesian (probabilistic) methods Evidential methods Rough sets and fuzzy methods 3. Feature Set Selection Algorithmic methods Statistical approaches 4. Ontologies to capture semantic information Ontologies Information Sources TrustCertaintyContext Classifi-cations Fusion Processes Feature extraction Feature analysis ? Measure 1 Measure 2 Two different classes Decision boundary Feature space After semantically-enhanced fusion After one sensor compromised Initial position, two sensors poor sensor good sensor

71 71 Root Cause Analysis & Overload Protection Goal –Move sensor information processing to higher levels of interpretation –Reduce the information overload on the user of the sensor network Approach –Apply root cause analysis techniques from systems management domain to sensor information fusion –Develop an algebra to determine the mapping between sensor monitored information and events. –Methods to refine and reduce sensor information stream based on human perceived event Sensor Measurements M1 M2 Event Processing


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