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IEEE Paper Template in A4 (V1) Security and Privacy Issues in Cloud and Fog Domain Beulah Moses XXXXXXXXXX Masters in IT – Networking XXXXXXXXXX ABSTRACT – XXXXXXXXXXwords- What ?(50 words). We are...

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IEEE Paper Template in A4 (V1)
Security and Privacy Issues in Cloud and Fog Domain
Masters in IT – Networking
ABSTRACT – XXXXXXXXXXwords- What ?(50 words). We are discussing the issues in both the cloud and the fog domain. The issues in the cloud domain include ……The issues in the fog domain include…..The research assignment focusses on a, b , c and d. Why(Importance)
Keywords— cloud, fog, ….(10 marks)
1. INTRODUCTION (15 marks)
Two to three paragraphs.(1 page)
Section 1. Background . …Section 2. …Section 3
2. Background/Literature Review(4-5 pages) (40 marks)
2.1 Cloud Domain
2.1.1 Hidden Channel Attack
1 paragraph with diagrams where possible
2.1.2 Data Plane Attack
2.1.3 Control Plane Attack
2.2 Fog Domain
2.2.1 Authentication and Trust Issues
3. ISSUES/ SOLUTIONS(2- 4 pages)(10 marks)
3.1 Abc
3.2 Def
3.3 Xyz
4. Future Research (5 marks)
5. Advantages/ Disadvantages (5 marks)
6. Conclusion (5 marks)
A. Page Layout
Short paper is. Your paper must use a page size co
esponding to A4 which is 210mm (8.27") wide and 297mm (11.69") long. The margins must be set as follows:
· Top = 19mm (0.75")
· Bottom = 43mm (1.69")
· Left = Right = 14.32mm (0.56")
Your paper must be in two column format with a space of 4.22mm (0.17") between columns.
All paragraphs must be indented. All paragraphs must be justified, i.e. both left-justified and right-justified.
B. Text Font of Entire Document
The entire document should be in Times New Roman or Times font. Type 3 fonts must not be used. Other font types may be used if needed for special purposes.
Recommended font sizes are shown in Table 1.
C. Title and Author Details
Title must be in 24 pt Regular font. Author name must be in 11 pt Regular font. Author affiliation must be in 10 pt Italic. Email address must be in 9 pt Courier Regular font.
Font Sizes for Papers
    Font Size
    Appearance (in Time New Roman or Times)
    table caption (in Small Caps),
figure caption,
eference item
    reference item (partial)
    author email address (in Courier),
cell in a table
    abstract body
    abstract heading (also in Bold)
    level-1 heading (in Small Caps),
    level-2 heading,
level-3 heading,
author affiliation
    author name
All title and author details must be in single-column format and must be centered.
Every word in a title must be capitalized except for short minor words such as “a”, “an”, “and”, “as”, “at”, “by”, “for”, “from”, “if”, “in”, “into”, “on”, “or”, “of”, “the”, “to”, “with”.
Author details must not show any professional title (e.g. Managing Director), any academic title (e.g. Dr.) or any membership of any professional organization (e.g. Senior Member IEEE).
To avoid confusion, the family name must be written as the last part of each author name (e.g. John A.K. Smith).
D. Content
The abstract should give a clear indication of the objectives, scope, results and conclusion of your work. It is recommended to have at least five references.
E. Figures and Tables
One figure and one table can be included in your short paper. Figures and tables must be centered in the column. Large figures and tables may span across both columns. Any table or figure that takes up more than 1 column width must be positioned either at the top or at the bottom of the page.
Graphics may be full color. All colors will be retained on the PDF. Graphics must not use stipple fill patterns because they may not be reproduced properly. Please use only SOLID FILL colors which contrast well both on screen and on a black-and-white hardcopy, as shown in Fig. 1.
Fig. 1 A sample line graph using colors which contrast well both on screen and on a black-and-white hardcopy
Original version of this template was provided by courtesy of Causal Productions (”. Most of the formatting instructions in this document have been compiled by Causal Productions from the IEEE LaTeX style files.
List and number all bibliographical references in 9-point Times, single-spaced, at the end of your paper. When referenced in the text, enclose the citation number in square
ackets, for example [1]. Where appropriate, include the name(s) of editors of referenced books. The template will number citations consecutively within
ackets [1]. The sentence punctuation follows the
acket [2]. Refer simply to the reference number, as in [3]—do not use “Ref. [3]” or “reference [3]” except at the beginning of a sentence: “Reference [3] was the first . . .”
Number footnotes separately in superscripts. Place the actual footnote at the bottom of the column in which it was cited. Do not put footnotes in the reference list. Use letters for table footnotes.
Unless there are six authors or more give all authors’ names; do not use “et al.”. Papers that have not been published, even if they have been submitted for publication, should be cited as “unpublished” [4]. Papers that have been accepted for publication should be cited as “in press” [5]. Capitalize only the first word in a paper title, except for proper nouns and element symbols.
For papers published in translation journals, please give the English citation first, followed by the original foreign-language citation [6].
[1] G. Eason, B. Noble, and I. N. Sneddon, “On certain integrals of Lipschitz-Hankel type involving products of Bessel functions,” Phil. Trans. Roy. Soc. London, vol. A247, pp. 529–551, April XXXXXXXXXXreferences)
[2] J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73.
[3] I. S. Jacobs and C. P. Bean, “Fine particles, thin films and exchange anisotropy,” in Magnetism, vol. III, G. T. Rado and H. Suhl, Eds. New York: Academic, 1963, pp. 271–350.
[4] K. Elissa, “Title of paper if known,” unpublished.
[5] R. Nicole, “Title of paper with only first word capitalized,” J. Name Stand. A
ev., in press.
[6] Y. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, “Electron spectroscopy studies on magneto-optical media and plastic substrate interface,” IEEE Transl. J. Magn. Japan, vol. 2, pp. 740–741, August 1987 [Digests 9th Annual Conf. Magnetics Japan, p. 301, 1982].

Length: 6000 words Maximum
Choose one of the topics given below and submit a research project, which should not exceed 6000 words.
Topics to select from but not limited to:
• IoT Security and Privacy Regime/challenges in all domains of IoT
• IoT Smart City
• IoT Mining Industry
• IoT Health Care
• IoT Independent Living of Older Generation
• IoT for Reducing Food Wastage in Australia
• IoT in Finance Industry and Security Challenges
•  Business Intelligence in IoT
• Future of Fog Domain  
• IoT and Data Analytics
• Big Data challenges in IoT and Cloud
• Machine Learning for Intelligent Decision Making in IoT
• Blockchains/ Common Cu
ency for IoT (eg IoTA) Challenges
If you want to choose other IoT related topics please talk to your lecturer before deciding.
 Please use the template from the Student resource folder.
1. Analyse the Task -Milestone 1
Analyse what is expected of you. This includes careful reading of the assignment task as specified in the Subject Outline. The executive summary of the research project to be written as an Abstract. 
For Analysis:
  i. What is the purpose of this report?
  ii. What is the topic you have chosen for your report? (The topic can be one specified as above, but you can come up with new ideas and topics of your choice in IoT)
  iii. What is the expected format of the report? (Refer to the format on Interact2)
  iv. Come up with a Problem Statement based on your topic, note it down and discuss it with your lecturer.
  v. Write a small executive summary as an abstract
2. Introduce the Problem or Challenges- Milestone2
  i. Write the Introduction to the problem and domain.
   ii. Structure of the report including which section covers what?
3. Do the Research- Literature Review –Milestone 3
This is the main part of the research project and could take more time. (Referencing should be in APA6 format)
 i. Review of Literature based on past and cu
ent work in your topic (Download from conference proceedings, journal articles - use Primo search to access more articles and journals - Minimum 10 journals or articles)
 ii. Structure in chronological order and start summarising.
 iii. Based on the literature, list the requirements for your Project, then choose one or more requirements to work with.
 iv. Now you can modify your Problem Statement based on the requirements chosen for your project.
 v. Write the cu
ent and past methodologies used to solve your problem, the requirements and Literature Review
4. Write your Report - Final Report - Milestone 4
Start writing your draft, as you do your research project. (Put it all together)
 i. Write an executive summary of the report (Purpose, Scope, Method, Results, Recommendation and Conclusion)
 ii. Write a Review of Literature based on past and cu
ent work in your topic (no more than 3 pages) 
 iii. Briefly explain each methodology used in the past and present (5 or more methodology)
 iv. Compare the above methodologies from your perspective on the efficiency, simplicity, extension into other applications, time saving when used, cost saving when used, feasibility, connectivity, commercialisation issues, etc.
 v. Explain the methodology which best suits your application/problem based on various factors with proper justifications. This methodology can be one of the above methodologies or a combination of more than one.
 vi. Include tables and graphs to support your justification and argument.
vii. Discuss your research findings with a conclusion.
viii. Referencing should be in APA6 format.
 1. Abstract -10 marks
2. Introduction- 15 marks
3. Literature Review 40 marks
4. Issues/ Challenges/ Solutions- 10 marks
5. Future Research - 5 marks
6. Advantages and disadvantages- 5 marks
7. Conclusion - 5 marks
8. References and Presentation- 10 marks
Answered Same Day Apr 20, 2020 ITC560 Charles Sturt University


Ahmedali answered on Apr 28 2020
153 Votes
Big Data challenges in IoT and Cloud
Executive Summary
Big Data, IoT, and Cloud computing are most advanced technologies that are now becoming inseparable. The IoT produced data form the part of big data, which is processed by Big Data technologies while analysis of this data is ca
ied out by cloud-based applications. This amalgamation of technologies also result into increase of challenges as each technologies has its own set of challenges such that when they are put together, the systems have to face a large number of challenges. These challenges are required to be addressed if companies have to make most effective use of these technologies to
ing profitability in the organization.
This paper addresses this need by exploring the challenges faced by the three technologies. The paper first explores the fundamentals of each technologies including what they are, how they are structured and where they are used. Further, it explores the challenges of each technologies. In the next section, the challenges discovered are collated, critically analysed and the solutions are suggested. The paper analyses the key feature of Big data that is obtained from IoT devices and explores possible solutions for it. As the cu
ent research explores more of the challenges and leaves less scope for identification of the solutions, a future research is recommended for the same such that each of the challenges identified can be studied in depth and solutions can be discovered.
Executive Summary    1
Introduction    2
Literature Review    3
Cloud Computing technology    3
Cloud computing models    4
Development process    4
Challenges in Cloud Computing    5
Measures to overcome challenges in Cloud Computing    9
IoT Technologies    9
IoT Architecture    10
Challenges of IoT    11
Big Data Technologies    13
Issues and Challenges in Big Data    13
Big Data, IoT, and Cloud Computing Challenges and Solutions    15
Heterogeneity    15
Future research    17
Conclusions    18
References    19
IT industry cu
ently in the phase of transition in which the data is growing at an exponential rate and the people and organizations are getting more data centric. The world has entered the Big data era and the data is getting more precise with the increase of capabilities of the technology. Big data provides many opportunities for the organization but at the same time also present some challenges.
The collection of huge data can be obtained from various internet sources in the modern world and with the use of IoT; it has become possible to extract more data from industrial and very human sources. Because of the emergence of IoT, the expanse of data has become even more and growing towards infinity.
To be able to extract the value from the big data, the technology needs to process the data and analyse the same in the timely manner. The results of the analysis can affect the business decisions. Cloud computing is used to keep the big data stored. Cloud is the distributed system of computing that provides resources for storage, computing and networking of big data. The adoption of cloud computing has some major benefits for users such as reduced cost of operations effective management, higher availability, and rapid elasticity of data.
Organizations working to gain more profitability from the use of Big data lead to wide talks about the field of data science by researchers. Data science comes with a variety of techniques that can be used for extracting data and developing insights from the data. Big Data analytics can be used for analysis of the converged data that is obtained from IoT devices and from the cloud computing storage units.
However, big data analytics is faced with several challenges that could be related to big data usage, cloud-computing challenges or IoT as the three technologies merged to produce a complete system of data extraction and analysis. While one technology has specific challenges, when the three technologies are used in a unified manner, the challenges not only add but new challenges also emerge because of the combination. This research would explore the challenges that are faced by the Big Data technology systems operating in Cloud under the IoT environment.
Literature Review
Big data is huge in size, velocity and variety and thus, requires a platform that is capable of storing this large amount of data along with the defined categories. Thus, cloud computing comes as the most suitable choice for the storage of big data. Big data is not directly usage and needs to be filtered and refined before it can be put to an actual business use. Big data analytics is used for this and it lies inside the cloud system platform. Before understanding how the Big Data technologies over cloud and IoT work, the three technologies have to be explored individually to understand how they work and what challenges they face.
Cloud Computing technology
Cloud computing is academically defined as "A computing paradigm where the boundaries of cloud computing are determined economic rationale and not by technical limits". As per NIST, cloud computing uses a shared pool of a variety of different computing resources. Before cloud computing emerged, companies used to develop applications in their own technology infrastructure but with the advent of cloud computing, cloud infrastructure could be used for developing applications at much lower costs than traditional development that needed an organization to establish a complete infrastructure for the development (PETRI, 2010). There are three types of deployment models that can be used for the deployment of cloud computing and these include IaaS, PaaS, and SaaS, each of them are discussed hereafter.
Cloud computing models
Figure 1: Cloud Computing Models (Glas & Andres, 2010)
IaaS: In this cloud computing model, the infrastructure can be put on the cloud using virtualization technology such that hardware components like data centres, networks, and the company can use servers over cloud (Eval-Source, 2014).
PaaS: This architecture can be used for implementing multiple applications on the cloud. This model easy to integrate and deploy. Platform As a Service (PaaS) model is used for the development of software applications over the web.
SaaS: In this deployment model of cloud computing, clients are provided with the license for using cloud based applications and the payment for the same is managed using pay-as-you-go model. is one such model that is based on SaaS deployment. There are some major benefits of SaaS such as fast deployment, better user adoption, and less support requirement (Glas & Andres, 2010).
Development process
The development process of cloud computing application involves four major steps that include identification of opportunities, suitability assessment, requirement gathering and implementation.
Opportunities Identification: The organization is explored to understand what applications are already used in the organization and the utilizes, databases or website that are with the organization. Using this knowledge, gaps can be identified on what additional applications would be required for the implementation over the cloud. This would need an understanding of the applications purpose, security needs, growth opportunities, and the environment in which the application would be deployed (Gupta & Rathore, 2012).
Suitability Assessment: Before an organization decides to implement any cloud-based application, the suitability of the needed application to cloud environment needed to be explored. This would include exploration of the potential risks that the application can face in a cloud environment. In case, the risks are too high, the application may not be installed over the cloud (Carlson, et al., 2012).
Requirement Gathering: The stakeholder requirements for the development of the cloud solution is then gathered so that the developed application has the required features and other criteria related to functionality, performance, security, compliance, manageability, and standards.
Implementation: Cloud can be implemented using specific deployment model as per the needs of the organization from private cloud, virtualization, third party platform, and hy
id model involving in-house as well as virtualized resources (Babcock, 2011).
Challenges in Cloud Computing
Perceived risks related to cloud computing prevent its adoption and thus, it is important to study risks and challenges that are faced by cloud computing models in business. There can be five major sources of risks in cloud computing environment and these include uses, service provided, network provider, enterprise, and the environment (Catteddu & Hogben, 2010).
A Risk Identification Matrix can be used for understanding how the risks generated in each of these areas can affect an organization and how easy is it to mitigate their impacts. The matrix shown below can be used for creating the risk profile of a cloud based network. This risk profile can be modified as the workload in the IT systems increase (Gadia, 2011).
Figure 1 Risk Identification Matrix (Ristić, 2014)
The resulting Risks can include issues related to availability, integrity and confidentiality. The risk profile of the cloud based business contains details of risk types, their origin, and risk levels (Schotman, Shahim, & Mitwalli, 2013). Different types of risks faced in cloud computing based businesses include:

User risk: Many organizations today have their employees using their own devices for the office use such as in the BYOD scheme. The organization has a limited control over these privately owned devices and hence, the risks are higher. For instance, in case such a device gets lost, the person able to access the device would be able to access the data (Buchhloz, Dunlop, & Ross, 2012). These users also use certain common business applications such as Microsoft Excel and Private Email service that can further add to security risk for the organization. Controls needed for protection of the data that is transfe
ed through these devices is difficult to achieve in privately owned devices (Oracle Corporation , 2007).
Enterprises risk: Private cloud is more secure than a public cloud but if the data centres used are not of industrial grade, the risks only increase and more comprehensive security processes are then required to mitigate these risks s (Engine Yard, Inc., 2014). The enterprise identity access management (IAM) is one area that creates more risks for cloud computing environment and thus, need proper governance mechanism. For this, different sources of risks have to be assessed and mitigation measures have to be made (Wipro Council for Industrial Research, 2013).
Network provider risk: Networks can exist between the cloud service provider and the company, and between the user and the cloud service provider. Communication on the network with enterprise can be controlled by an organization but the communication that happens between the users, the cloud service provider is difficult to control, and thus, this network poses a risk of facing attacks like Man-in-the-Middle. Although, encryptions are used over SSL, mitigating such risks is still difficult and attackers can still
eak into the network affecting its integrity and confidentiality of users. Although encryptions are used such as in SSL for mitigating such risks, the network can still be
oken into by attackers leading to bad impacts on confidentiality and integrity (Mkrtchyan, 2010).
Cloud provider risk: Cloud service providers can also cause problems that are difficult to predict. Service Level Agreements can help in this case largely for recovery of losses but it cannot help prevent the damage. If the service provider maintains a redundant server, lost data can be retrieved but in case of disaster, it may not help if disaster recovery is not (Chen, Longstaff, & Carley, 2004).
Environment risk: Risks that are caused by natural disasters were the environmental risks that can affect the data on cloud. For instance, in 2011, Tsunami in Japan caused severe damage to Fukushima nuclear energy plant that caused a major power outage (Hathaway, et al., 2012). Hu
icane Sandy had disrupted major services in many areas of US in 2012 (AKIYAMA, SATO, NAITO, NAOI, & KATSUTA, 2012).
Key concerns that act as ba
iers to cloud Computing are data availability issues, data security concerns, legal or compliance issues and loss of control over data (Shinde & Chavan, 2013). In a mitigation plan for overcoming ba
iers to Cloud Computing, each of these inhibitors had to be explored and their potential risk has to be studied....

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