Dr. Bhavani Thuraisingham The University of Texas at Dallas (UTD) February 2013 Cloud Computing for Assured Information Sharing

Team Members Sponsor: Air Force Office of Scientific Research The University of Texas at Dallas – Dr. Murat Kantarcioglu; Dr. Latifur Khan; Dr. Kevin Hamlen; Dr. Zhiqiang Lin, Dr. Kamil Sarac Sub-contractors – Prof. Elisa Bertino (Purdue) – Ms. Anita Miller, Late Dr. Bob Johnson (North Texas Fusion Center) Collaborators – Late Dr. Steve Barker, Kings College, U of London (EOARD) – Dr. Barbara Carminati; Dr. Elena Ferrari, U of Insubria (EOARD)

Outline Objectives Assured Information Sharing Layered Framework Our Research Education Acknowledgement: – Research Funded by Air Force Office of Scientific Research – Education funded by the National Science Foundation

Objectives Cloud computing is an example of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet. Users need not have knowledge of, expertise in, or control over the technology infrastructure in the "cloud" that supports them. Our research on Cloud Computing is based on Hadoop, MapReduce, Xen Apache Hadoop is a Java software framework that supports data intensive distributed applications under a free license. It enables applications to work with thousands of nodes and petabytes of data. Hadoop was inspired by Google's MapReduce and Google File System (GFS) papers. XEN is a Virtual Machine Monitor developed at the University of Cambridge, England Our goal is to build a secure cloud infrastructure for assured information sharing applications

Information Oper a tions Across Infospheres: Assured Information Sharing Scientific/Technical Approach Conduct experiments as to how much information is lost as a result of enforcing security policies in the case of trustworthy partners Develop more sophisticated policies based on role-based and usage control based access control models Develop techniques based on game theoretical strategies to handle partners who are semi-trustworthy Develop data mining techniques to carry out defensive and offensive information operations Accomplishments Developed an experimental system for determining information loss due to security policy enforcement Developed a strategy for applying game theory for semi- trustworthy partners; simulation results Developed data mining techniques for conducting defensive operations for untrustworthy partners Challenges Handling dynamically changing trust levels; Scalability Objectives Develop a Framework for Secure and Timely Data Sharing across Infospheres Investigate Access Control and Usage Control policies for Secure Data Sharing Develop innovative techniques for extracting information from trustworthy, semi-trustworthy and untrustworthy partners Budget FY06-8: AFOSR $300K, State Match. $150K Component Data/Policy for Agency A Data/Policy for Coalition Publish Data/Policy Component Data/Policy for Agency C Component Data/Policy for Agency B Publish Data/Policy

Our Approach Integrate the Medicaid claims data and mine the data; next enforce policies and determine how much information has been lost (Trustworthy partners); Prototype system; Application of Semantic web technologies Apply game theory and probing to extract information from semi-trustworthy partners Conduct Active Defence and determine the actions of an untrustworthy partner –Defend ourselves from our partners using data mining techniques –Conduct active defence – find our what our partners are doing by monitoring them so that we can defend our selves from dynamic situations

Coalition Policy Enforcement Prototype Dr. Mamoun Awad (postdoc) and students

3/19/20168 Layered Framework for Assured Cloud Computing User Interface Hadoop/MapReduc/Storage HIVE/SPARQL/Query XEN/Linux/VMM Secure Virtual Network Monitor Policies XACML Risks/ Costs QoS Resource Allocation Cloud Monitors Figure2. Layered Framework for Assured Cloud

Secure Query Processing with Hadoop/MapReduce We have studied clouds based on Hadoop Query rewriting and optimization techniques designed and implemented for two types of data (i) Relational data: Secure query processing with HIVE (ii) RDF data: Secure query processing with SPARQL Demonstrated with XACML policies Joint demonstration with Kings College and University of Insubria – First demo (2011): Each party submits their data and policies – Our cloud will manage the data and policies – Second demo (2012): Multiple clouds

Fine-grained Access Control with Hive System Architecture  Table/View definition and loading,  Users can create tables as well as load data into tables. Further, they can also upload XACML policies for the table they are creating. Users can also create XACML policies for tables/views.  Users can define views only if they have permissions for all tables specified in the query used to create the view. They can also either specify or create XACML policies for the views they are defining.  CollaborateCom 2010

Server Backend SPARQL Query Optimizer for Secure RDF Data Processing Web Interface Data Preprocessor N-Triples Converter Prefix Generator Predicate Based Splitter Predicate Object Based Splitter MapReduce Framework Parser Query Validator & Rewriter XACML PDP Plan Generator Plan Executor Query Rewriter By Policy New Data Query Answer To build an efficient storage mechanism using Hadoop for large amounts of data (e.g. a billion triples); build an efficient query mechanism for data stored in Hadoop; Integrate with Jena Developed a query optimizer and query rewriting techniques for RDF Data with XACML policies and implemented on top of JENA IEEE Transactions on Knowledge and Data Engineering, 2011

Demonstration: Concept of Operation User Interface Layer Fine-grained Access Control with Hive SPARQL Query Optimizer for Secure RDF Data Processing Relational Data RDF Data Agency 1Agency 2Agency n …

RDF-Based Policy Engine Policies Ontologies Rules In RDF JENA RDF Engine RDF Documents Inference Engine/ Rules Processor e.g., Pellet Interface to the Semantic Web Technology By UTDallas

RDF-based Policy Engine on the Cloud A testbed for evaluating different policy sets over different data representation. Also supporting provenance as directed graph and viewing policy outcomes graphically  Determine how access is granted to a resource as well as how a document is shared  User specify policy: e.g., Access Control, Redaction, Released Policy  Parse a high-level policy to a low-level representation  Support Graph operations and visualization. Policy executed as graph operations  Execute policies as SPARQL queries over large RDF graphs on Hadoop  Support for policies over Traditional data and its provenance  IFIP Data and Applications Security, 2010, ACM SACMAT 2011

Integration with Assured Information Sharing: User Interface Layer RDF Data Preprocessor Policy Translation and Transformation Layer MapReduce Framework for Query Processing Hadoop HDFS Agency 1Agency 2Agency n … RDF Data and Policies SPARQL Query Result

Architecture

Key Feature 1: Policy Reciprocity  Agency 1 wishes to share its resources if Agency 2 also shares its resources with it  Use our Combined policies  Allow agents to define policies based on reciprocity and mutual interest amongst cooperating agencies SPARQL query: SELECT B FROM NAMED uri1 FROM NAMED uri2 WHERE P

Key Feature 2: Develop and Scale Policies  Agency 1 wishes to extend its existing policies with support for constructing policies at a finer granularity.  The Policy engine – Policy interface that should be implemented by all policies – Add newer types of policies as needed

Key Feature 3: Justification of Resources  Agency 1 asks Agency 2 for a justification of resource R2 Policy engine – Allows agents to define policies over provenance – Agency 2 can provide the provenance to Agency 1 But protect it by using access control or redaction policies

Key Feature 4: Development Testbed  Policy framework provides three configurations – A standalone version for development and testing; – A version backed by a relational database – A cloud-based version achieves high availability and scalability while maintaining low setup and operation costs

Secure Storage and Query Processing in a Hybrid Cloud The use of hybrid clouds is an emerging trend in cloud computing – Ability to exploit public resources for high throughput – Yet, better able to control costs and data privacy Several key challenges – Data Design: how to store data in a hybrid cloud? Solution must account for data representation used (unencrypted/encrypted), public cloud monetary costs and query workload characteristics – Query Processing: how to execute a query over a hybrid cloud? Solution must provide query rewrite rules that ensure the correctness of a generated query plan over the hybrid cloud

Hypervisor integrity and forensics in the Cloud Cloud integrity & forensics Hardware Layer Virtualization Layer (Xen, vSphere) Linux Solaris XP MacOS  Secure control flow of hypervisor code  Integrity via in-lined reference monitor  Forensics data extraction in the cloud  Multiple VMs  De-mapping (isolate) each VM memory from physical memory Hypervisor OS Applications integrity forensics

Cloud-based Malware Detection Benign Buffer Feature extraction and selection using Cloud Training & Model update Unknown executable Feature extraction Classif y Class Malware Remove Keep Stream of known malware or benign executables Ensemble of Classification models A Cloud Map-reduce framework is used to extract and select features from each chunk A 10-node cloud cluster is 10 times faster than a single node Very effective in a dynamic framework, where malware characteristics change rapidly

Identity Management Considerations in a Cloud Trust model that handles – (i) Various trust relationships, (ii) access control policies based on roles and attributes, iii) real-time provisioning, (iv) authorization, and (v) auditing and accountability. Several technologies have to be examined to develop the trust model – Service-oriented technologies; standards such as SAML and XACML; and identity management technologies such as OpenID. Does one size fit all? – Can we develop a trust model that will be applicable to all types of clouds such as private clouds, public clouds and hybrid clouds Identity architecture has to be integrated into the cloud architecture.

Education NSF Capacity Building Grant on Assured Cloud Computing – Introduce cloud computing into several cyber security courses Completed courses – Data and Applications Security – Data Storage – Digital Forensics – Secure Web Services – Computer and Information Security – Capstone Course One course that covers all aspects of assured cloud computing – Week long course to be given at Texas Southern University

Directions Secure VMM (Virtual Machine Monitor) and VNM (Virtual Network Monitor) – Exploring XEN VMM and examining security issues – Developing automated techniques for VMM introspection – Will examine VMM issues January 2012 Integrate Secure Storage Algorithms into Hadoop Identity Management Social network systems on the Cloud (e.g., Use Storm framework)