PredictingCriticalCloudComputingSecurityIssuesusingArtificialNeuralNetworkANNsAlgorithmsinBankingOrganizations.pdf

ITEE, 6 (2) pp. 40-45, APR 2017

ITEE Journal Information Technology & Electrical Engineering

ISSN: – 2306-708X

Volume 6, Issue 2 April 2017

Predicting Critical Cloud Computing Security Issues using Artificial Neural

Network (ANNs) Algorithms in Banking Organizations

Abdelrafe Elzamly1, Burairah Hussin 2, Samy S. Abu Naser3, Tadahiro Shibutani4, and Mohamed Doheir5

1Department of Computer Science, Al-Aqsa University, Gaza, Palestine 2 ,5 Information & Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM), Malaysia

3Department Information Technology, Al-Azhar University, Gaza, Palestine 4Institute of Advanced Sciences, Yokohama National University, Yokohama, Japan

E-mail: [email protected]

ABSTRACT The aim of this study is to predict critical cloud computing security issues by using Artificial Neural Network (ANNs) algorithms.

However, we proposed the Levenberg–Marquardt based Back Propagation (LMBP) Algorithms to predict the performance for

cloud security level. Also LMBP algorithms can be used to estimate the performance of accuracy in predicting cloud security

level. ANNs are more efficiently used for improving performance and learning neural membership functions. Furthermore, we

used the cloud Delphi technique for data gathering and analysis it in this study. In this study, the samples of 40 panelists were

selected from inside and outside Malaysian banking organizations based on their experienced in banking cloud computing.

However, we have indicated that the LMBP is nonlinear optimization models which used to measure accuracy of the prediction

model, the Mean Square Error (MSE) are measured to determine the performance. The performance is goodness, if the MSE is

small as shown in Table 1. This work has been conducted on groups of cloud banking developers and IT managers. As future

work, we intend to combine another optimal technique with ANNs algorithms to predict and mitigate critical security cloud issues.

Though, positive prediction of critical cloud security issues is going to surge the probability of cloud banking success rate.

Keywords: Cloud banking organization, Cloud Computing, Cloud Security Issues, , Artificial Neural Network, Levenberg Marquardt Algorithm,

Back Propagation Algorithm,. 1. NTRODUCTION

Although much research and progress in the area of

cloud computing project, a lot of cloud computing projects

have a very high failure rate particularly when it is related to

the banking area. However, several serious cloud security

issues like data protection and integrity, quality of

services(QoS), Portability and Interoperability, and mobility

need to be controlled and mitigated before cloud computing

able to apply adoptive widely [1]. In addition, cloud

computing has several advantages but cloud computing in

banking organizations is suffering from a lot of cloud

security issues. The aim of cloud risk management is

identification and evaluation of cloud security issues at an

early stage to predict the cloud computing security level [2].

Today, cloud computing risk management became a mutual

practice amongst leading banking organization success. In

the increasing effort to improve development processes and

security; new studies have led to cloud computing risk area.

Risk management aids software project manager and team

to do improved decisions to mitigate cloud-computing risks.

The objective of this study is predicting performance for

cloud computing security issues using Levenberg–

Marquardt based Back Propagation (LMBP) algorithms.

2. LITERATURE REVIEW

Cloud computing risk management consists of

computing processes, methods and techniques that are

useful to mitigate cloud computing risk failure. Security

risk management is increasingly becoming significant

in a diversity of areas linked to information technology

(IT), for example: telecommunications, banking

information systems, cloud computing[3]. Moreover,

the cloud banking model is a resource management

modeling founded on economic philosophies. Its

function like commercial banks in loan and deposit

business [4]. Cloud security is a general subject and any

grouping of policies, controls, and technologies to

safeguard data, services and infrastructure from

conceivable attacks. Additionally, current researches

focused on providing security technologies, instead of

business features such as services stability, availability

and continuity [5]. This study is going to predict the

critical cloud issues in Malaysian banking

organizations. Actually, they presented the conceptual

framework for cloud security banking that involved

components for example security, legal, privacy,

compliance and regulatory issues of banking [6]. As

stated by previous studies we split the framework

modeling cloud computing to five phases as mobility

and banking application, Cloud Deployment Models

(CDM), cloud risk management models (CRMM),

Cloud Service Models (CSM), and cloud security model

(CSM) as follows: Firstly, mobility related to the

possibility of moving and taking place in diverse

locations and through multiple times using any kind of

portable devices like smart phones, Personal Digital

Assistants (PDAs) and wireless laptops. Nonetheless,

mobile banking related to any operation that linked to

banking services like balance check, payments and

receiving banking SMS via a mobile device, and

account transactions [7]. Secondly, CSM depend on

ITEE, 6 (2) pp. 40-45, APR 2017

ITEE Journal Information Technology & Electrical Engineering

ISSN: – 2306-708X

Volume 6, Issue 2 April 2017

some state of the art of web technologies like

Application Programming Interface (API), Web

Services, Web 2.0, and etc. [8]. Also, CSM is split into

four categories that are offered from a cloud provider:

Software as a Service (SaaS), Platform as a Service

(PaaS), Banking Process as a Service (BPaaS), and

Infrastructure as a Service (IaaS). Thirdly, CDM can be

split into four dissimilar types: Public cloud is made

obtainable to the general public or a huge industry group

and are possessed by a third party selling cloud services

[9]. Private Cloud is functioned and possessed by a

single organization or company that focuses on

controlling the mechanism of virtualizing resources and

automating services those are used and tailored by many

lines of business and essential groups [4]. Community

cloud falls among public and private clouds with regard

to the target set of consumers [10]. Community cloud,

this model is used by a specific group of community

within an organization that has the same worry,

objectives or security necessities [11]. Hybrid cloud

uses both public and private cloud methods, where it

smears the strategic notions of the services of public

cloud with the basis of the private cloud. Fourthly,

Cloud Risk Management (CRM): in Cloud computing,

risk required to be taken into consideration in all phases

of interactions and investigated at every service stage in

relation to the possessions that should be protected [12].

Besides, there are diverse types of risks that bank

management should be protected against. For numerous

banks, the main risk is credit risk but there are several

other risks that supervising authorities must notify

banks about connected criteria and require them to

follow [13]. There are eight phases for effective cloud

risk management like Cloud Risk Planning Phase

(CRPL), Cloud Risk Analysis (CRA) phase, Cloud Risk

Identification(CRI) phase, Cloud Risk

Prioritization(CRP) phase, Cloud Risk

Evaluation(CRE) phase, Cloud Risk Treatment(CRT)

phase includes four strategies for responding to cloud

risks: cloud risk mitigation, cloud risk avoidance, cloud

risk transfer, cloud risk elimination, cloud risk

acceptance, Cloud Risk Controlling(CRC) phase, and

Cloud Risk Communication & Documentation (CRCD)

phase. Finally, Cloud Security Issues Models (CSIM):

cloud security is a very common topic and any grouping

of policies, technologies, and controls to protect data,

infrastructure and services from possible attacks or

achieving business objectives all the security domains

should work in an effective manner [14].

3. CLOUD SECURITY ISSUES

Though, classification of critical security issues in

cloud banking is needed to be highlighted in this section

[15]: 3rd Party (Providers) and Policies Security Issues:

Lack of standards, Service Level Agreement (SLAs),

Governance, Legally and policy, Dependency, Lack of

transparency, Cloud service provider viability,

Malicious insiders, Regulatory compliance &

requirements, Shared technology issues, Unknown risk

profile, Trusted cloud, Abuse cloud computing;

Application and program (software) security issues:

Authentication, Authorization, Insecure Interfaces

API’s, Availability and Mobility, Portability and

Interoperability; Data and Information Security Issues:

Privacy, Confidentiality, Data Protection, Data

Limitations and Segregation, Data integrity and

scavenging, Data Location, Data Loss/Leakage,

Detection and Recovery, Hijacking of Account or

Service & Traffic; Security Control & Network Issues:

Information flow Controlling, Intrinsic Constrains of

Wireless Network, Network Access Schemes,

Bandwidth, Anonymity and Network Traffic Analysis,

Network Security, Virtual Network Protection, Limited

control, Distributed Denial of Service (DDoS),

Heterogeneity in Mobile cloud Devices, Platform

Reliability and Latency; Security and Service

Management Issues: Session Management,

Identity/Access Management, Quality of Service (QoS),

IT organizational changes; Physical Infrastructure

Security Issues: Flexibility Infrastructure, Single Point

to Attack and Failure, High-value cyber-attack targets,

the multi-tenancy, Scalability, Cost.

4. EMPIRICAL STRATEGY

The Delphi technique use to collect data as

qualified informants, so we focused on two cloud

developers groups and cloud IT managers in banking

organizations. In this regard the Delphi study is

modified to three phases like identifying, analyzing, and

evaluating as described in Figure 1. The data are

collected by secondary data and Delphi study. In current

study, the population samples of forty panelists were

chosen from inside and outside Malaysian banking

organizations according to their experienced in cloud

banking. Actually, we measure the probability of

occurrence according to a 10 scales (1= “very low

probability of occurrence risk” and 10 = “very high

probability of occurrence risk”), and the brutality of the

cloud security issues described on a 10 scales (1= “very

low influence risk” and 10 = “very high impact risk”.

Actually, we used Delphi techniques for data gathering

and analysis it in this study. However, we will begin a

list of cloud security issues based on secondary data,

experienced of cloud managers and cloud developers.

The Delphi method is collected data and aggregated of

cloud security issues. In fact, we divided the phases of

cloud Delphi technique into three phases such as

identifying, analyzing, and evaluating. However, we

illustrate the concept of Delphi technique for identifying

and classifying cloud security issues in Figure 1 as

follows:

Cloud Delphi Technique

Phase 1: Identifying  Collected data and aggregated of cloud

security issues.

 Select the experts from both inside and outside

the banking organization.

 Divide panelist to two groups cloud

ITEE, 6 (2) pp. 40-45, APR 2017

ITEE Journal Information Technology & Electrical Engineering

ISSN: – 2306-708X

Volume 6, Issue 2 April 2017

Figure 1: Illustrates the steps of cloud Delphi study of

collecting data [16]

5. METHODOLOGY (MATERIALS &

METHODS)

However, the data gathered for this study to be used

in the modelling is getting from the managers and cloud

developers in banking organizations. We propose

Artificial Neural Network (ANNs) for predicting cloud

security issues in banking organizations. In order to

manage and predict performance of cloud computing

security level, we can use artificial neural networks

methods. In order to establish the intelligent approaches,

first we need to model the relationship between cloud

computing issues. In addition, artificial neural networks

modelling are used as nonlinear statistical data model to

predict cloud-computing issues. Of course, IT managers

and cloud developers must use practical approaches,

methods, and tools to predict cloud security issues in

banking organization. Indeed, the back-propagation

algorithm is used in layered feed- forward ANNs where

the artificial neurons are structured in layers, and lead

their signals “forward”, and then the errors are

transmitted backwards. The neural network gets input

from input layers and yields the output to the output

layer and the processing can be done in hidden layers.

There must be only one input and output layer, however,

there may be an arbitrary number of hidden layers [17-

19]. Additionally, the BP algorithm should minimize

these errors, till the ANN learns the training data.

Typically the training initiates with random weights,

and the learning objective is to modify them so that the

error is reduced [17-19]. The design of procedures for

predicting cloud security issues using Levenberg-

Marquardt (LM) Based Back Propagation (BP)

Algorithm as follows:

1. Collect and prepare the data for cloud security issues based on Cloud Delphi Technique.

2. Assign an estimated probability of occurrence and severity of cloud security issues based on

security models.

3. Build a network analysis 4. Train the network: It generates the neural

network from a Cloud Delphi dataset with

known output data cases.

5. Test the network: A trained neural networks are used to test how well it does at prediction of

known and new output values.

6. Predict cloud security issues based models by using artificial neural networks for evaluating

the performance impact of CSI. A trained neural

network is used to predict unknown output

value.

6. RESULTS AND DISCUSSION

Indeed, we used the Levenberg–Marquardt based

Back Propagation (LMBP) Algorithms, as nonlinear

optimization to predict the performance. So we illustrate

the mean square error and Regression (R) values for the

Training, Validation and Testing as in Table 1.

Table 1 Illustrates the MSE and Regression values for the

three types Types Samples Training

data

(input)%

MSE R

Training 28 70% 4.94160×e-7 9.95213×e-1

Validation 6 15% 8.75807×e-6 9.79262×e-1

Testing 6 15% 1.95378×e-5 9.49600×e-1

Table 1 shows that the overall Mean Square Error

which measure the average squared errors between the

output data and targets data and Regression (R) which

measure correlation between the actual outputs data and

targets data for training, validation and testing samples.

The accuracy of prediction is observed, when the values of

R are closest to 1. Hence, if the dataset was trained by using

(LMBP) Algorithms, the performance obtained was in 3 epochs with 10 hidden neurons yields. The results indicated

that the LMBP algorithms are very efficiently for testing and

training networks. Although, a two-layered feed forward

network hidden neurons and networks are trained using

LMBP Algorithms as shown in Figure 2.

ITEE, 6 (2) pp. 40-45, APR 2017

ITEE Journal Information Technology & Electrical Engineering

ISSN: – 2306-708X

Volume 6, Issue 2 April 2017

Figure 2 architecture and algorithms and progress of ANN

system

Figure 3 Performance of LMBP Algorithm (MSE vs.

Epochs)

Figure 4 error histogram with 20 bins based LMBP

Indeed, it is trained to measure the performance of networks

by using LMBP algorithms in Matlab R2013b.

Furthermore, we estimated the best validation performance

0.0000087581 at epoch 3 in Figure 3 and the error histogram

with 20 bins is illustrated in Figure 4. Therefore, regression

R values are measured the correlation between outputs and

targets. Hence, the results in the regression analysis plot are

perfect correlation between the outputs and targets as in

Figure 5. In addition, the one mean a close relation between

outputs and targets, zero a random relationship. LMBP is

nonlinear optimal models which used to measure accuracy

of the prediction model, the Mean Square Error (MSE) are

measured to determine the performance. The performance is

goodness, if the MSE is small.

Figure 5 Regression Analysis Plot – Levenber g-Marquardt

Backpropagation Algorithm

7. CONCLUSIONS

The concern of the study is to predict critical cloud

computing security issues using Artificial Neural

Network (ANNs) algorithms. However, we presented

the Levenberg–Marquardt based Back Propagation

(BP) Algorithms to predict the performance for cloud

security level. Also LMBP algorithm is applied to

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

10-10

10-5

Best Validation Performance is 8.7581e-06 at epoch 3

d E

rro

r (m

)

5 Epochs

Train

Validation

Test

Best

Error Histogram with 20 Bins

Ins

tan

Errors = Targets – Outputs

-0.0

049

-0.0

0418

-0.0

0345

-0.0

0272

-0.0

0127

-0.0

0055

0.0

00181

0.0

00907

0.0

01632

0.0

02358

0.0

03084

0.0

0381

0.0

04536

0.0

05262

0.0

05988

0.0

06714

0.0

0744

0.0

08166

0.0

08892

Training

Validation

Test

Zero Error

0.65 0.66 0.67 0.68 0.690.65

0.655

0.66

0.665

0.67

0.675

0.68

0.685

0.69

Target

tpu

t ~

= 0

.97

arg

+ 0

.02

Training: R=0.99521

Data

Fit

Y = T

0.65 0.66 0.67 0.68 0.690.65

0.655

0.66

0.665

0.67

0.675

0.68

0.685

0.69

Target

tpu

t ~

= 1

.2*T

arg

+ –

0.1

Validation: R=0.97926

Data

Fit

Y = T

0.65 0.66 0.67 0.68 0.690.65

0.655

0.66

0.665

0.67

0.675

0.68

0.685

0.69

Target

tpu

t ~

= 1

.3*T

arg

+ –

0.2

Test: R=0.9496

Data

Fit

Y = T

0.65 0.66 0.67 0.68 0.690.65

0.655

0.66

0.665

0.67

0.675

0.68

0.685

0.69

Target

tpu

t ~

= 1

.1*T

arg

+ –

0.0

All: R=0.9596

Data

Fit

Y = T

ITEE, 6 (2) pp. 40-45, APR 2017

ITEE Journal Information Technology & Electrical Engineering

ISSN: – 2306-708X

Volume 6, Issue 2 April 2017

estimate and test the performance of accuracy for

predicting cloud security level. ANNs are more

efficiently used for improving performance and learning

neural membership functions. Indeed, the performance

of cloud security is analyzed by using LMBP to give the

best performance in the predicting models.

Furthermore, we used the cloud Delphi technique for

data gathering and analyzing it in this study. In this

study, the samples of 40 panelists were selected from

inside and outside Malaysian banking organizations based on their experienced in banking cloud computing.

However, we have indicated that the LMBP is nonlinear

optimal models which used to measure accuracy of the

prediction model and to reduce the error between the

actual outputs and targets for training process, the Mean

Square Error(MSE) are measured to determine the

performance. The performance is goodness, if the MSE

is small as shown in Table 1. As future work, we intend to use another optimal technique with Artificial Neural

Network algorithms to predict and mitigate critical

security cloud issues.

8. Acknowledgements

This work is organized by the Welfare Association in

Palestine; financially supported by the Arab Monetary

Fund, and Bank of Palestine under the program name

(Academic Fellowship Program Zamalah). The authors

also would like to thank Al-Aqsa University, Gaza,

Palestine and Faculty of Information & Communication

Technology, Universiti Teknikal Malaysia Melaka

(UTeM), Malaysia.

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Authors’ information

Abdelrafe Elzamly, He got a Ph.D.

in Information and Communication

Technology from the Technical

University Malaysia Melaka (UTeM)

in 2016 with a record of about 20

publications. He received his Master

degree in Computer Information

Systems from the University of Banking and Financial

Sciences in 2006. He received his B.Sc. degree in Computer

from Al-Aqsa University, Gaza in 1999. He is currently

working as Assistant Professor in Al-Aqsa University as a

full time. Also, from 1999 to 2007 he worked as a part time

lecturer at the Islamic University in Gaza. Between 2010 and

2012 he worked as a Manager in the Mustafa Center for

Studies and Scientific Research in Gaza. His research

interests are in risk management, software and information

systems engineering, cloud computing security, and data

mining.

Burairah Hussin, He received his

Ph.D. degree in Management

Science-Condition Monitoring

Modelling, from the University of

Salford, UK in 2007. Before that, he

received a M.Sc. degree in

Numerical Analysis and

Programming from the University of Dundee, UK in 1998

and a B.Sc. degree in Computer Science from the University

of Technology Malaysia in 1996. He currently works as a

Professor at the Technical University Malaysia Melaka

(UTeM). He also worked as the Dean at the Faculty of

Information and Communication Technology, Technical

University of Malaysia Melaka (UTeM). His research

interests are in data analysis, data mining, maintenance

modelling, artificial intelligence, risk management,

numerical analysis, and computer network advising and

development.

Samy Abu Naser, He got a Ph.D. in

Computer Science from North

Dakota State University, USA in

1993. He received his M.Sc. Degree

in Computer Science from Western

Kentucky University, USA in 1989.

He received his B.Sc. Degree in

Computer Science from Western

Kentucky University, USA in 1987. He is currently working

as a professor in Al-Azhar University, he worked as the

Dean of the Faculty of Engineering and Information

Technology in AL-Azhar University, he worked as Deputy

Vice President for Planning & Quality Assurance, and he

worked as a deputy dean of the Faculty of Engineering and

Information Technology in Al- Azhar University. His

research interests are in data mining, artificial intelligent,

and risk management.

Tadahiro Shibutani, He received

the Ph.D. degree in mechanical

engineering from Kyoto

University, Kyoto, Japan, in 2000.

He was a Visiting Scholar with the

Center of Advanced Life Cycle

Engineering, University of

Maryland, in 2007. He is currently Associate Professor of

Center for Creation of Symbiosis Society with Risk with

Yokohama National University, Yokohama, Japan. His

research interests include physics of failure, health

monitoring, and risk management for engineering systems.

Mohamed Doheir, He is currently a

PhD candidate in Health Care

Management in University Technical

Malaysia Malaka (UTeM). He

received his M. Sc. degree in Internet

working Technology from University

Technical Malaysia Malaka (UTeM) in

2012. He received his B.Sc. Degree in Educational

Computer Science from Al Aqsa University- Gaza, Palestine

in 2006. His research interests are in Health care, Cloud

Computing and Network Simulation.