dr. bhavani thuraisingham the university of texas at dallas (utd) february 2014 assured cloud...
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Dr. Bhavani ThuraisinghamThe University of Texas at Dallas (UTD)
February 2014
Assured Cloud Computing for Assured Information
Sharing
Outline• Objectives• Assured Information Sharing• Layered Framework for a Secure Cloud• Cloud-based Assured Information Sharing• Cloud-based Secure Social Networking• Other Topics
– Secure Hybrid Cloud – Cloud Monitoring– Cloud for Malware Detection– Cloud for Secure Big Data
• Education• Directions• Related Books
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, Dr. Maribel Fernandez, Kings College, U of
London (EOARD)– Dr. Barbara Carminati; Dr. Elena Ferrari, U of Insubria (EOARD)
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 and related applications
Information OperInformation Operaations Across Infospheres: tions Across Infospheres: Assured Information SharingAssured Information Sharing
Scientific/Technical ApproachConduct 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
ObjectivesDevelop a Framework for Secure and Timely Data Sharing
across InfospheresInvestigate Access Control and Usage Control policies for
Secure Data SharingDevelop innovative techniques for extracting information
from trustworthy, semi-trustworthy and untrustworthy partners
ComponentData/Policy for Agency A
Data/Policy for Coalition
Publish Data/Policy
ComponentData/Policy for Agency C
ComponentData/Policy for Agency B
Publish Data/Policy
Publish Data/Policy
Our Approach • Policy-based Information Sharing
• Integrate the Medicaid claims data and mine the data;• Enforce policies and determine how much information has
been lost (Trustworthy partners); • 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 analytics 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
04/19/23 8
Layered Framework for Assured Cloud Computing
Applications
Hadoop/MapReduc/Storage
HIVE/SPARQL/Query
XEN/Linux/VMM
Secure Virtual Network Monitor
PoliciesXACML
RDF
Risks/Costs
QoSResource 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 HiveSystem 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
ServerBackend
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 1 Agency 2 Agency n
…
RDF-Based Policy Engine
Policies
Ontologies
Rules
In RDF
JENA RDF EngineRDF Documents
Inference Engine/Rules Processore.g., Pellet
Interface to the Semantic WebTechnologyBy UTDallas
RDF-based Policy Engine on the Cloud
Policy Transformation
Layer
Result Query
DB
DB
RDF
Policy Parser Layer
Regular Expression-Query
Translator Data Controller Provenance Controller
. . . RDF
XML
Policy / Graph Transformation Rules
Access Control/ Redaction Policy (Traditional Mechanism)
User Interface Layer
High Level Specification Policy
Translator
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 1 Agency 2 Agency n
…
RDF Data and Policies
SPARQL Query
Result
Architecture
Policy Engine
Provenance
Agency 1
Agency 2 Agency n
User Interface Layer
Connection Interface
RDF GraphPolicy Request
RDF Graph: ModelRDF Query: SPARQL
RDBMS Connection: DB
Connection: Cloud
Connection: Text
Cloud-based Store
Local
Access Control
Combined
Redaction
Policy n-2
Policy n-1 Access Control
Combined
Redaction
Policy n
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 uri2WHERE P
Develop and Scale PoliciesAgency 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
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
Other Example PoliciesAgency 1 shares a resource with Agency 2 provided
Agency 2 does not share with Agency 3Agency 1 shares a resource with Agency 2 depending
on the content of the resource or until a certain timeAgency 1 shares a resource R with agency 2 provided
Agency 2 does not infer sensitive data S from R (inference problem)
Agency 1 shares a resource with Agency 2 provided Agency 2 shares the resource only with those in its organizational (or social) network
Analyzing and Securing Social Networks in the Cloud
AnalyticsLocation Mining from Online Social NetworksPredicting Threats from Social Network Data, Sentiment AnalysisCloud Platform for implementation
Security and PrivacyPreventing the Inference of Private Attributes (liberal or conservative; gay or straight)Access Control in Social NetworksCloud Platform for implementation
Security Policies for On-Line Social Networks (OSN)
• Security Policies ate Expressed in SWRL (Semantic Web Rules Language) examples
Security Policy Enforcement
• A reference monitor evaluates the requests.• Admin request for access control could be evaluated by
rule rewriting– Example: Assume Bob submits the following admin request
– Rewrite as the following rule
Framework ArchitectureSocial Network
Application
Reference Monitor
Semantic Web Reasoning
Engine
Access request Access Decision
Policy Store
Modified Access request
Policy Retrieval
Reasoning Result
SN Knowledge BaseKnowledge Base Queries
Secure Social Networking in the Cloud with Twitter-Storm
User Interface Layer
Fine-grained Access Control with Hive
SPARQL Query Optimizer for Secure RDF Data
Processing
Relational Data RDF Data
Social Network 1 Social Network 2 Social Network N
…
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 cloudMultiple VMsDe-mapping (isolate) each VM memory from physical memory
Hypervisor
OS
Applications
integrityforensics
Cloud-based Malware Detection
Benign
Buffer
Feature extraction and selection using
Cloud
Training & Model update
Unknown executable
Feature extraction
Classify
ClassMalware
Remove Keep
Stream of known malware or benign executables
Ensemble of Classification
models
Cloud-based Malware Detection• Binary feature extraction involves
– Enumerating binary n-grams from the binaries and selecting the best n-grams based on information gain
– For a training data with 3,500 executables, number of distinct 6-grams can exceed 200 millions
– In a single machine, this may take hours, depending on available computing resources – not acceptable for training from a stream of binaries
– We use Cloud to overcome this bottleneck
• 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 are being 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.
Big Data and the Cloud0 Big Data describes large and complex data that cannot be managed by traditional data
management tools
0 From Petabytes to Zettabytes to Exabytes of data
0 Need tools for capture, storage, search, sharing, analysis, visualization of big data.
0 Examples include
- Web logs, RFID and surveillance data, sensor networks, social network data (graphs), text and multimedia, data pertaining to astronomy, atmospheric science, genomics, biogeochemical, biological fields, video archives
0 Big Data Technologies
0 Hadoop/MapReduce Platform, HIVE Platform, Twitter Storm Platform, Google Apps Engine, Amazon EC2 Cloud, Offerings from Oracle and IBM for Big Data Management, Other: Cassandra, Mahut, PigLatin, - - - -
0 Cloud Computing is emerging a critical tool for Big Data Management
0 Critical to maintain Security and Privacy for Big Data
Security and Privacy for Big Data0 Secure Storage and Infrastructure
0 How can technologies such as Hadoop and MapReduce be Secured
0 Secure Data Management
0 Techniques for Secure Query Processing
0 Examples: Securing HIVE, Cassandra
0 Big Data for Security
0 Analysis of Security Data (e.g., Malware analysis)
0 Regulations, Compliance Governance
0 What are the regulations for storing, retaining, managing, transferring and analyzing Big Data
0 Are the corporations compliance with the regulations
0 Privacy of the individuals have to be maintained not just for raw data but also for data integration and analytics
0 Roles and Responsibilities must be clearly defined
Security and Privacy for Big Data0 Regulations Stifling Innovation?
0Major Concern is too many regulations will stifle Innovation0Corporations must take advantage of the Big Data technologies to improve business0But this could infringe on individual privacy0Regulations may also interfere with Privacy – example retaining the data 0Challenge: How can one carry out Analytics and still maintain Privacy?
0 National Science F Workshop Planned for Spring 2014 at the University of Texas at Dallas
Education on Secure Cloud Computing and Related Technologies
• Secure Cloud Computing– 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
• Analyzing and Securing Social Networks• Big Data Analytics and Security
Directions• Secure VMM and VNM
– Designing Secure XEN VMM – Developing automated techniques for VMM
introspection– Determine a secure network infrastructure for the cloud
• Integrate Secure Storage Algorithms into Hadoop• Identity Management in the Cloud• Secure cloud-based Big Data Management/Social
Networking
Related Books• Developing and Securing the Cloud, CRC Press (Taylor and
Francis), November 2013 (Thuraisingham)• Secure Data Provenance and Inference Control with
Semantic Web, CRC Press 2014, In Print (Cadenhead, Kantarcioglu, Khadilkar, Thuraisingham)
• Analyzing and Securing Social Media, CRC Press, 2014, In preparation (Abrol, Heatherly, Khan, Kantarcioglu, Khadilkar, Thuraisingham)
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