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Cybersecurity Through Nimble Task Allocation: Semantic Workflow Reasoning for Mission-Centered Network Models

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Cybersecurity Through Nimble Task Allocation: Semantic Workflow Reasoning for Mission-Centered Network Models Cybersecurity Through Nimble Task Allocation: Semantic Workflow Reasoning for Mission-Centered Network Models Yolanda Gil Information Sciences Institute University of Southern California [email protected] http://www.isi.edu/~gil USC Information Sciences Institute Yolanda Gil 1 Knowledge Technologies at ISI: From Basic Research to Transition AFOSR DARPA DOD 2000 2005 TRELLIS: Basic JIST: Capturing decisions Transition to IC’s research on information and evidence in RDEC experimental analysis capture intelligence analysis platform NSF AFRL DOD 2002 2008 Semantic workflows for Windward: Scalable Transition to IC’s large-scale scientific data Knowledge Discovery RDEC experimental analysis through Distributed Analysis platform AFOSR W3C 2005 2010 MathTrust: Basic research Provenance Incubator on social trust of open leading to standards for information sources provenance on the web AFOSR 2011 Mission - Centered USC InformationNetwork Sciences Models Institute Yolanda Gil 2 http://www.w3.org/2005/Incubator/prov/wiki W3C Provenance Standard Need standard representation for origins of information • Trust in open systems (Web), computational science, process validation and compliance W3C Provenance Incubator launched in 2009 (Y. Gil, Chair) • Collected use cases for provenance • Developed provenance requirements for the Web • Created mappings for existing provenance vocabularies • State-of-the-art report • Provenance in Web architecture Final report in Dec 2012 proposed charter for a Working Group on provenance WG started 4/2011 to develop PROV standard, released final request for comments 8/2012 USC Information Sciences Institute Yolanda Gil 3 W3C PROV Standard (2012) PROV Primer PROV Ontology PROV Data Model PROV Notation PROV Constraints PROV Access and Query USC Information Sciences Institute Yolanda Gil 4 New Award (2011-2015): “Cybersecurity Through Nimble Task Allocation: Semantic Workflow Reasoning for Mission-Centered Network Models” Problem: Existing cybersecurity measures for network intrusion follow similar pattern • Discover intrusion -> Find vulnerability-> Fix problem -> Repeat • Unclear that these approaches can lead to secure systems Approach: Accomplish the mission assuming the network is compromised and subject to deception • Focus on protecting the mission, rather than the network Key Idea: Mission-Centered Network Models • Semantic workflows represent mission – High level tasks + goals + constraints • Reasoning to specialize and map workflow onto network Benefits: Nimble task allocation implies unpredictability to opponent USC Information Sciences Institute Yolanda Gil 5 Problem Addressed Existing cybersecurity measures for network intrusion follow similar pattern • Discover intrusion -> Find vulnerability-> Fix problem -> Repeat Vast amounts of communications occurring throughout the network • Network models focus on physical and logical level • No models to describe high-level information flow USC Information Sciences Institute Yolanda Gil 6 Key Idea: Semantic Workflows Represent Missions and their Task Decompositions Workflows are important to model because they represent task-driven information flow within the network • Workflows represent how information is derived from retrieving and processing data obtained from distributed sources Workflows arise from: • Predefined SOPs to reflect common information processing tasks by users • Dynamic creation by users using mashup construction tools • Analysis of actual network traffic data USC Information Sciences Institute Yolanda Gil 7 Related Work: Reconstructing Flows Extracting application use from network flow [Labovitz et al 2010] [Maier et al 2009] [Fukuda et al 2005] [Bartlett et al 2007] • Classifying traffic based on: – Port use – Packet signatures based on packet payload – Blind techniques based on application communication patterns • Short-lived flows, low-rate flow periodicity, service discovery, etc. Program slicing to reconstruct causal difference graphs [Johnson et al 2011] USC Information Sciences Institute Yolanda Gil 8 Mission-Centered Network Models MISSION AT TIME T Tasks as Workflows Results & Provenance Mission: M= {G,W,R,π} • G: goals Composed of Trusted because Task • W: semantic workflows Layer Workflow Dynamic Workflow Fragments Policies Provenance • R: resources • π: policies Assembled from Related through Software Data Network = {L,P,C} Components Sources Logical • Layer L: logical resources • P: physical resources Executed in Populated by • C: connections Physical Layer USC Information Sciences Institute Yolanda Gil 9 Semantic Workflows to Executable Workflows MISSION AT TIME T Tasks as Workflows Results & Provenance Four stages of workflow elaboration: Composed of Trusted because Task 1. Workflow template Layer Workflow Dynamic Workflow Fragments Policies Provenance – M= {G,W,R,π} 2. Workflow instance – WI= f(M,D) Assembled from Related through 3. Execution-ready Software Data Components Sources workflow (map to L) Logical Layer – ERW=f(WI,π,L} Executed in Populated by 4. Executable workflow (map to P) Physical – EW=g(ERW,π,P} Layer USC Information Sciences Institute Yolanda Gil 10 Example: Airlift Workflow (Adapted from [Burstein et al 2008]) Goal Airlift all patients to specified hospitals Workflow Specify Flight Satisfy Flight Parameters Constraints Repeat for Repeat while Find Airport POE each PAX more PAX Fail to obtain (Port of Embarcation) Dest. destinations. permission Near to Find Airport POD Passengers (PAX) (Port of Debarcation) Find Planes to carry Create Scheduled Task: Near to Hospitals Passengers between Request Landing Fly plane P to POE and POD during Time At POE the specified time period Pickup at POE Data flow: Identify Passenger Lat/Lon Request Landing Location Identify nearest APT Create Scheduled Task: Time at POD By incrementally Fly plane P to Delivery at POD Logical Expanding region Resources Resource Requests Reqs to Airport Assert flights Query to Queries to Resource Requests Permission Reqs to Assert flights to Airlift Units Admins In Schedule DB Geographic Server AIRPORTS Server to Each Airlift Unit Airport Admin Svcs to schedule DB Physical ResourcesUSC Information Sciences Institute Yolanda Gil 11 Protect Mission While Network is Under Attack Goal Airlift all patients to specified hospitals Workflow Specify Flight Satisfy Flight Parameters Constraints Repeat for Repeat while Find Airport POE each PAX more PAX Fail to obtain (Port of Embarcation) Dest. destinations. permission Near to Find Airport POD Passengers (PAX) (Port of Debarcation) Find Planes to carry Create Scheduled Task: Near to Hospitals Passengers between Request Landing Fly plane P to POE and POD during Time At POE the specified time period Pickup at POE Data flow: Identify Passenger Lat/Lon Request Landing Location Identify nearest APT Create Scheduled Task: Time at POD By incrementally Fly plane P to Delivery at POD Logical Expanding region Resources Resource Requests Reqs to Airport Assert flights Query to Queries to Resource Requests Permission Reqs to Assert flights to Airlift Units Admins In Schedule DB Geographic Server AIRPORTS Server to Each Airlift Unit Airport Admin Svcs to schedule DB Physical Resources A A A USC Information Sciences Institute Yolanda Gil 12 Effects of Attack Resource Requests Reqs to Airport Assert flights Query to Queries to Resource Requests Permission Reqs to Assert flights to Airlift Units Admins In Schedule DB Geographic Server AIRPORTS Server to Each Airlift Unit Airport Admin Svcs to schedule DB A1 A2 A3 A4 A1: Assess trust on this already executed task • Check provenance records A2: Attack does not affect physical resource selected • Proceed as planned A3: Attack affects physical resource selected • Reassign task to alternative physical resource A4: Has not occurred yet, but resource is critical • Replicate resource, ensure protection of resource USC Information Sciences Institute Yolanda Gil 13 Research Challenges (I): Robust Missions through MCNMs Ability to trust already executed tasks • Analyze provenance record, resubmit if needed Ability to determine what are the critical resources for accomplishing a given task within a mission • Determine tasks that have limited mapping choices Ability to control how a task is mapped to physical and logical resources • Policy-based resource allocation algorithms Ability to manage a task in a network under a threat and reassign it dynamically to a different set of resources • Adaptively change set of mapping policies π USC Information Sciences Institute Yolanda Gil 14 Research Challenges (II) Ability to prioritize the use of uncompromised resources based on criticality of mission tasks Ability to detect intrusion and deception • Generate n mappings for a given workflow (with π and random assignments), compare results through provenance records Ability to proactively detect high-level patterns of deception • Orchestrate simulated tasks mirroring real missions and observe Ability to predict the practical impact on the mission of deception activities in specific (L and P) resources • Can workflow be mapped given the mission requirements Ability to measure mission accomplishment based on level of trust on task outcome when network is compromised USC Information Sciences Institute Yolanda Gil 15 Our Prior Work: From Workflow Templates to Executable Workflows [Kumar et al 09] Workflow template Executable workflow for for image processing 2560x2400 pixels USC Information Sciences Institute Yolanda Gil 16 Our Prior Work: Wings/Pegasus/Condor
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