Big Data (security Issue)

8/29/2015
SECURITY ISSUES ASSOCIATED WITH BIG
DATA IN CLOUD COMPUTING
SECURITY ISSUES ASSOCIATED
WITH BIG DATA IN CLOUD
COMPUTING
Seminar Advance Topics One
Submitted By
Md.Mehedi Hassan
1/26
Supervisor
Sajjad Waheed
Associate Professor
Dept. of ICT,MBSTU

DATA IN CLOUD COMPUTING8/29/2015
Outline
 Introduction
 Big Data
 Why Big Data
 Cloud Computing
 How Big Data is Related with Cloud Computing
 Why Choose Big Data as a Thesis Topic
 Introduction to Hadoop
 MapReduce
 Hadoop Distributed File System(HDFS)
 Application
 Advantages of Big Data
 Alternative of Big Data
 Security Issue of Big Data
 Motivation and Related Work
 Issues and Challenges
 The Proposed Approaches
 Conclusions
2/26

Introduction
 To analyze complex data and to identify patterns it is very important
to securely store, manage and share large amounts of complex data
(big data).
 Big data applications are a great benefit to organizations, business,
companies and many large scale and small scale industries.
 Cloud resources are needed to support big data storage and projects,
and big data is a huge business case for moving to cloud
 The main focus is on security issues in cloud computing that are
associated with big data.
3/26

Big Data
 Big Data is the word used to describe massive volumes of structured
and unstructured data that are so large that it is very difficult to
process this data using traditional databases and software
technologies.
 Big Data Source :
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Big Data
 Volume
 Many factors contribute towards increasing Volume
storing transaction, live streaming and data
collected from sensors etc
 Variety
 Structured: Relational data.
 Semi Structured: XML data.
Unstructured: Word, PDF, Text,
Media Logs
 Velocity
 Big Data Velocity deals with the
pace at which data flows in from
sources and human interaction
5/26

Why Big Data
 Speed, Capacity and Scalability of Cloud Storage
 End Users Can Visualize Data
 Manage Data Better
 Company Can Find New Business Opportunities
 Data Analysis Methods, Capabilities will Evolve
6/26

Cloud Computing
 Cloud Computing is a technology which
depends on sharing of computing
resources than having local servers or
personal devices to handle the
applications.
 In Cloud Computing, the word “Cloud”
means “The Internet”, so Cloud
Computing means a type of computing in
which services are delivered through the
Internet.
7/26

How Big Data is Related with Cloud Computing
 Cloud computing is a powerful technology to perform massive-scale and
complex computing.
 It eliminates the need to maintain expensive computing hardware,
dedicated space, and software
 Big Data need large on-demand compute power and distributed storage to
crunch the 3V data problem and Cloud seamlessly provides this elastic on-
demand
8/26

Why Choose Big Data as a Thesis Topic
 As a software developer I have handle large volume of data for banking
transaction.
 Already observed for time consume to execute data for a particular select
statement or analytical SQL
 System is very slow when all branch are parallel processing.
 This problem over come using Big Data concept
 Already use Facebook,Goole,IBM etc.
 Open source (Hadoop)
 In this case I choose Big Data Topic
9/26

Introduction to Hadoop
10/26
 Hadoop : Apache open source framework written in java that allows
distributed processing of large datasets across clusters of computers using
simple programming models
 Doug Cutting son’s toy
 Hadoop Architecture
Two major layers
 Processing layer :
MapReduce
 Storage layer :
Hadoop Distributed
File System
MapReduce
(Distributed Computation)
HDFS
(Distributed Storage)
YARN Framework Common Utilities

Introduction to Hadoop (cont.)
 How Hadoop works
 Core tasks across a cluster of computers
 Data dividing into directories and files
 Files are then distributed across various cluster nodes
 HDFS, supervises the processing.
 Blocks are replicated.
 Performing sort that takes place between the map and reduce stages.
 Sending the sorted data to a certain computer.
 Advantages
 Low-cost alternative to build bigger servers
 Fault-tolerance and high availability.
 Dynamic clustering
 Automatic data distribution and open source
11/26

MapReduce
 What is MapReduce : A processing technique and a program model for
distributed computing based on java.
 Mapper
 Shuffle
 Reducer
 Java based
 Key Value
12/26

MapReduce (cont.)
13/26

MapReduce Example
14/26

Hadoop Distributed File System(HDFS)
 The HDFS is a distributed, scalable, and portable file-system written in
Java for the Hadoop framework
 Features
 Distributed storage and processing
 Name Node
 Data Node
 Interface in Hadoop
 Streaming access
 Cluster status check
15/26

Hadoop Distributed File System(cont.)
16/26
Name Node
Meta data(Name, replica…)
/home/foo/data, 3…
Client
Blocks
Replication
Write
Meta data Ops
Read
Block Ops
D a t a n o d e s D a t a n o d e s
Rack 1 Rack 2

Application
17/26
Homeland
Security
Smarter
Healthcare
Multi-channel
sales
Telecom
Manufacturing
Traffic Control
Trading
Analytics
Search
Quality

Advantages of Big Data
 Cost reduction
 Faster, better decision making
 New products and services
 Perform risk analysis
18/26

Alternative of Big Data
 Apache Spark (Less security than Hadoop)
 Cluster Map Reduce(Slow and less security than Hadoop)
19/26

Issue and Challenge
 Network level
 Distributed Nodes
 Distributed Data
 Internodes Communication
 Authentication level
 Data Protection
 Administrative Rights for Nodes
 Authentication of Applications and Nodes
 Logging
 Data level
 Confidentiality
 Integrity
 Availability
 Generic types
 Traditional Security Tools
 Use of Different Technologies
20/26

The Proposed Approaches
 File Encryption
 Network Encryption
 Logging
 Software Format and Node Maintenance
 Nodes Authentication
 Rigorous System Testing of Map Reduce Jobs
 Honeypot Nodes
 Layered Framework for Assuring Cloud
 Third Party Secure Data Publication to Cloud
 Access Control
21/26

Conclusions
 I have highlighted the main advantages and application of Big data with
cloud computing .
 Summarized security issues associated with big data in cloud computing .
 Propose cloud environments can be secured for complex business
operations.
 Propose approaches for Big Data security
22/26

Future Works
 To Implement data chaptering algorithm with data security
 Data flow Hadoop to Cloud with confidential security
23/26

Q & A
24/26

DATA IN CLOUD COMPUTING8/29/2015 25/26

References
 Ren, Yulong, and Wen Tang. "A SERVICE INTEGRITY ASSURANCE
FRAMEWORK FOR CLOUD COMPUTING BASED ON
MAPREDUCE."Proceedings of IEEE CCIS2012. Hangzhou: 2012, pp 240 –
244, Oct. 30 2012-Nov. 1 2012
 Hao, Chen, and Ying Qiao. "Research of Cloud Computing based on the
Hadoop platform."Chengdu, China: 2011, pp. 181 – 184, 21-23 Oct 2011.
 N, Gonzalez, Miers C, Redigolo F, Carvalho T, Simplicio M, de Sousa G.T,
and Pourzandi M. "A Quantitative Analysis of Current Security Concerns and
Solutions for Cloud Computing.". Athens:2011., pp 231 – 238, Nov. 29 2011-
Dec. 1 2011
 Hao, Chen, and Ying Qiao. "Research of Cloud Computing based on the
Hadoop platform.".Chengdu, China: 2011, pp. 181 – 184, 21-23 Oct 2011.
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Big Data (security Issue)

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