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)