Efficient Server Management Architecture Based on Cloud Edge

Integration with Advanced Security Enhancements

D. Prasanna, S. Parasuraman, S. R. Magesh, S. Narayana Badri and G. Ragul

Department of Computer Science and Engineering, Mahendra Engineering College, Namakkal, Tamil Nadu, India

Keywords: Server Management, Cloud Edge Integration, Security Enhancement, Cloud Security, AEICS, Cloud

Manufacturing, CMS.

Abstract: This research examines the transition from completely automated to fully data-assisted process control in

production systems. The process is optimized in current concepts through data analysis using a decision

support system after manufacturing is finished. Prescriptive automation endeavors to regulate the process

independently and beforehand by employing a prescriptive analytics methodology. The development of

information technology architecture is a critical component of the overall concept. By conducting expert

interviews and reviewing recent literature, it is feasible to ascertain the components of IT architecture that are

necessary for prescriptive automation. These requirements, which are solution components, are in opposition

to the purpose of a modular architectural concept. Reference architecture is established by analyzing the

requirements and, as a result, the components of the solution that are necessary, provided that the data

processing resources are available. The processing components of this architecture are built upon a

combination of cloud and peripheral computing. This study concentrates on the latest hazards to privacy, data

security, and real-time processing in the cloud manufacturing environment. The objective of the investigation

is to establish a scalable security framework that can ensure secure data transmission, safeguard sensitive

information, and facilitate secure access control in dynamic and distributed manufacturing environments by

capitalizing on the synergy between cloud and edge technologies. This study introduces a novel technique,

Advanced Edge Integrated Cloud Security (AEICS), and employs it to evaluate the efficacy of the proposed

scheme by cross-validating it with the current approach, Cloud Manufacturing System (CMS). The research

will also assess the potential benefits of this integrated security architecture in enhancing the reliability,

efficiency, and resilience of cloud manufacturing systems, as well as provide best practices for its deployment

and implementation. The results section contains a comprehensive demonstration of the efficacy of the

proposed scheme in detail.

1 INTRODUCTION

The Internet has grown exponentially in size and

complexity over the past few decades Zhanyang Xu,

et al. 2020. Nearly half of the world's population has

internet access by year's end 2014. The growth of the

internet, however, exacerbates the network security

vulnerabilities. The security of the Internet of Things

(IoT) will be a top priority since it will encompass all

objects and devices that may connect to the internet.

Threats to human safety can arise from many sources,

from basic home sensors to advanced medical

devices, automobiles, aircraft, and even nuclear

power plants. In 2013, the number of breaches

increased by 62% from 2012. Vijay M., 2024; P.

Velmurugadass, et al., 2021.; Ahmed M Alwakeel,

2021, The Internet edge is where typical security

controls such as firewalls, along with intrusion

detection and prevention systems are deployed. SDN

supports the enforcement of policies dynamically;

fine-grained traffic filtering, and rapid response to

security incidents. This introduces an IoT security

solution based on SDN architectures. The first key

aspect of the proposed security system was to

construct and protect the wireless and wired network

infrastructure. The second was to explore the

possibility of extending the proposed architecture to

support access control systems for Ad-Hoc networks

and network objects (sensors, tablets, smart-phones,

etc.). Then, Chapter 4 describes a proposed network

access control system called PANATIKI based on

Internet of Things devices. Abhay Verma, et al.,

770

Prasanna, D., Parasuraman, S., Magesh, S. R., Badri, S. N. and Ragul, G.

Efﬁcient Server Management Architecture Based on Cloud Edge Integration with Advanced Security Enhancements.

DOI: 10.5220/0013872600004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 1, pages

770-778

ISBN: 978-989-758-777-1

2021; Dulana Rupanetti, et al., 2024, Software

Defined Networks (SDN) is a novel technology

concept for networking that arose from recent

advancements in computer networking. The SDN

controller is a centralized piece of software that

controls the way the network operates as a whole.

With SDN, there is a natural consolidation of network

intelligence and a decoupling of the control and data

planes.

In response to packets or proactively via rules, the

controller has the capacity to add, edit, or remove

flow entries. Also, SDN allows for dynamic policy

enactment, fine-grained traffic filtering, and rapid

response to security events. An SDN architecture-

based Internet of Things security paradigm is present.

In summary, the reported research was successful

towards delivery of the first goal of the proposed

security model, which is to design and secure a

wireless and wired network infrastructure. M. Yasir

Mehmood, et al., 2021, The next step would be to

expand the proposed model to include Ad-Hoc

networks and network object, like sensors, tablets,

smartphones, etc. An inventive method to cloud

manufacturing has taken shape, incorporating the

tenets of “Internet of everything, intelligent leading,

digital/analog driving, shared services, cross-border

integration and universal innovation.” This

methodology also changes how services are provided

in cloud and IoT environments, developing a wider

range of possibilities. With the introduction of new

sensing units, perception technologies, and internet of

things (IoT) infrastructure, intelligent manufacturing

resources, capabilities, and products are being linked

to new networks. Odugu Rama Devi, et al., 2022;

Bhagwati Sharan, et al., 2022; Akhil Pandey, et al.,

2023, These networks include private networks,

sensor networks, software-defined networks, global

positioning systems (GPS), remote sensing, radar,

and quick response (QR) codes. As a result, the

network perimeter of the new cloud manufacturing

system is become more and more open and diffuse.

Existing security architectures rely on information

security technologies to safeguard modern cloud

manufacturing systems, and they are not adequate.

2 RELATED WORKS

Edge computing and security problems in cloud

networks are very widespread now. Sina Ahmadi,

2024 This study concentrates on investigating such

problems and formulating the ideal answers. In this

sense, a thorough literature review has been done.

According to the results, edge computing is connected

to various difficulties like privacy issues, security

breaches, expensive expenses, low efficiency, etc.

Thus, appropriate security policies must be put in

place if we are to solve these problems. Emerging

developments such machine learning, encryption,

artificial intelligence, real-time monitoring, etc. assist

to reduce security concerns by means of technology.

Moreover, via cloud computing they may create a safe

and secure future. It was found that new technologies

and approaches readily allow one to cover the security

consequences of edge computing.

The fast-growing Internet of Things environment

makes solutions for effective data processing and

analysis much sought after. The topic of this paper

is the possible Internet of Things (IoT) usage of hybrid

architectures, cloud computing, and edge computing.

Using extensive search and analysis of industry

publications, conference proceedings, and peer-

reviewed articles, the technique highlighted current

advancements in computing technology for the

Internet of Things (IoT). Although cloud computing

offers more scalability and flexibility, the results

reveal that edge computing excels in reducing latency

and enhancing data privacy by localized processing.

Fog and mist computing is two hybrid systems

aggregating the best aspects of cloud and edge

computing. For Internet of Things (IoT)

deployments, these hybrid systems enhance

bandwidth consumption and provide low-latency,

privacy-sensitive applications. For situations needing

low-latency processing and excellent bandwidth

control, hybrid architectures are found very

successful. These methods satisfy the limitations of

both edge and cloud computing for IoT as they offer a

balanced method of data analysis and resource

management. They also exhibit a tremendous

progressive progress.

Shalin Parikh, et al., 2019, New computing

paradigm known as cloud computing entered the

scene with the arrival of IoT/5G and the data

warehousing and processing now mostly use cloud

computing as their platform. Data storage into the

cloud does, however, provide a unique set of security

issues and problems. Moreover, as every device

creates more data; the traditional cloud computing

paradigm cannot manage problems like excessive

latency, bandwidth limitation, and resource

restriction. New computational paradigms such as

edge and fog computing are being proposed to solve

the issues of the former at the device itself or close by.

Both of these approaches offer compute decisions and

memory storage very adjacent to the device. No

system is flawless notwithstanding their benefits.

Efﬁcient Server Management Architecture Based on Cloud Edge Integration with Advanced Security Enhancements

771

Muktar Yahuza, et al., 2020, One interesting

concept that improves the capacity of cloud

computing is edge computing. Maintaining the

provision of computer services depends on keeping

the surroundings free from security and privacy

violations. Privacy and security concerns make most

people not consider the edge computing environment

as a coherent paradigm. While many have examined

privacy and security concerns with edge computing,

not everyone has considered the necessary criteria. A

system's ability and functions to eliminate specific

security and privacy concerns are reflected in its

security and privacy requirements. This study aims

to help researchers in the future identify promising

research areas by taking a close look at the privacy and

security requirements of the edge computing and the

many technology approaches used to mitigate these

risks. This literature review covers a wide range of

subjects, including: (1) a taxonomy of edge network

attacks and the related technological trend that

mitigates these attacks; (2) a classification of edge

computing security and privacy requirements; (3)

state-of-the-art techniques that combat these threats;

(4) metrics for measuring the effectiveness of these

techniques; (5) potential avenues for future research in

this area; and, finally, research opportunities.

Elahe Fazeldehkordi, et al., 2022, The Internet of

Things (IoT) is a creative idea with enormous uses that

have crept into our daily existence. IoT and linked

devices are fast increasing in count. Problems with

service availability, security, privacy, bandwidth,

network resources, data transmission costs, long

transmission times leading to increased power

consumption of IoT devices, and latency are among

the many issues that develop when transferring the

matching enormous quantities of data produced by

these devices to the cloud. By bringing data

processing and storage closer to end users and IoT

devices, edge computing (EC) avoids these issues.

Edge computing and related computing paradigms are

defined in depth in the outset of the article, along with

their advantages and disadvantages. After that, we

provide possible solutions and countermeasures after

going into detailed on the primary security and privacy

breaches inside the scope of EC-based IoT.

Following this, we provide an EC-based architecture

that is secure for use in Internet of Things applications.

We also provide an edge computing application

scenario for the Internet of Things and compare it to

cloud computing, weighing the pros and cons of each

technology. Concerns regarding the security and

privacy of Internet of Things devices based on EC

have been addressed.

3 METHODOLOGY

Cloud servers often employ security features such as

encryption, access control mechanisms, and

conventional firewalls to safeguard sensitive data

while it is stored and processed on these servers. But

as cloud manufacturing gets more complicated and

dispersed, these systems’ latency and scalability

issues become increasingly notable. The integration

of cloud and edge security is becoming increasingly

important in order to deal with any threats as they

arise. An integrated architecture that smoothly

integrates edge and cloud security is still missing

from many of these systems' security measures.

Security issues, including hacking and illegal access,

become increasingly critical as the system grows in

size. Because of their proximity to the data source,

edge devices require extra safeguards to avoid

security holes in real-time functionality. The demands

of cloud manufacturing systems are always changing,

therefore it's possible that conventional security

methods won't be enough. The necessity for a more

cohesive security architecture that incorporates

elements of both cloud and edge security is, therefore,

growing in importance. Some of the limitations such

as, edge registration has several drawbacks. One

drawback is it requires more local equipment

components. Another is, as more and more "smart"

devices enter edge servers and IoT devices with

powerful built-in PCs, there are fresh opportunities

for mischief makers to bargain for these devices.

Lastly, it would also increase costs because it would

require a more complicated PC with more training

resources.

Cloud and edge security measures will be

integrated to offer low latency for monitoring and

detecting threats in real-time. The solution will use

new encryption technology to prevent unauthorized

access to data traveling from edge devices to the

cloud. Local data processing and filtering on the edge

devices will improve data and cyber breach

prevention before contacting the cloud. We will build

a consolidated platform to monitor and manage

security in both environments. Authentication and

access control will be strengthened with multi-factor

authentication (MFA) and role-based access controls

to ensure that sensitive production data will only be

accessed by authorized users. The cloud and edge

infrastructure will be outfitted with intrusion

detection and prevention systems (IDPS) for

improved detection and prevention of cyber threats.

The strategy will improve our ability to protect

against the latest threats by automatically adjusting

security practices through AI and ML. All of these

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integrated strategies provide an improved, secured,

scalable, and efficient approach to security in a cloud

manufacturing environment. By enhancing the

efficiency of conventional security system design, the

suggested approach known as Advanced Edge

Integrated Cloud Security (AEICS) outperforms the

current one, Cloud Manufacturing System (CMS).

Below are the characteristics of the proposed

architectural design:

• Implementing security measures in both cloud

and edge settings in a distributed manner.

• Cloud and edge node threat detection and

response in real-time.

• The cloud and edge devices' security rules are

seamlessly integrated.

• Secure architecture that can scale to

accommodate various cloud manufacturing

systems.

• Encryption of data and safe transfer of data

from edge devices to the cloud.

• Multi-factor authentication used for managing

identities and access.

At present, there is a set logic in place for

controlling production facilities, and this logic

considers dependencies and correlations using data

that is mainly out of date. With the advent of

digitalization, new avenues have opened up for the

improvement of decision making using complete and

current data. Research on predictive analytics for

production control mostly focuses on broad needs and

difficulties with specific components (like data

processing) or technology (like cloud computing).

Data analysis and optimization, in the form of a

decision support system, occurs after production,

although process data extraction is typically

contemplated. There must still be a human decision-

maker and manual executor for this auxiliary system

to work. It is easy to use for managing devices,

configuring access controls to the devices, and

developing cloud and edge layer security protocols.

The Internet of Things (IOT), which includes sensors

and connected equipment, sends encrypted data to

Cloud and Edge nodes. The design incorporates

authentication protocols to ensure the system cannot

be accessed by unauthorized people and devices. The

data around security is provided by the solutions that

monitor assets in real-time, allowing the quick

identification of vulnerabilities or attacks. Edge

devices are meant to process data at the device, which

improves responsiveness and minimizes latency of

critical security operations. The system is flexible to

support different industrial environments due to the

ability to work with numerous input types and

protocols. Data logs and event histories are collected

for additional discernment into event processing to

ensure security compliance. Finally, input design

purposes adaptive mechanisms in response to the

continuously changing threat landscape to ensure

continual protection to the extent new threats are

introduced. The following figure 1 shows the

architecture diagram and the following figure 2 shows

the system design.

Figure 1: Architecture Diagram.

Alerts in relation to a security incident is sent to

administrators or security personnel in real-time,

enabling immediate verification and action. It will also

generate odes of full and complete security reports

featuring details relating to cloud or edge system

performance, vulnerabilities, and potential threats.

The reports also come with visual dashboards that

capture risk changes in the security posture, trends,

Efﬁcient Server Management Architecture Based on Cloud Edge Integration with Advanced Security Enhancements

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Figure 2: System Design.

and highlight events. The architecture is able to

generate role-specific outputs, allowing multiple

stakeholders to receive information according to their

user role. All activities and security events are logged

in a secured data logger to provide full audit and

compliance. As well, the output is structured to

integrate with third-party security monitoring and

incident management services easily. Security actions

evolve continuously, based on processed output and

feedback from edge devices. The last step in

proactively mitigating future security breaches is the

analysis of output and actionable recommendations

based on the analysis provided by the system once

completed, given that it remains fed on data until

October 2023.

In the micro-services architecture, data encryption,

authentication, threat detection, and other components

work as stand-alone parts and can be scaled with

demand. You are extracting information that is used

by the initializer to make programmatic decisions --

the code is executing multiple protocols, including

TLS/SSL, and the encrypted incoming payload will

help transfer data between the cloud and edge layers

securely. In addition, robust identity management

solutions use token-based authentication protocols

for authentication and access control. Moreover, the

system also utilizes various technologies like Web-

Socket for the real-time monitoring of edge devices,

and for the continuous upload of their data to the

Cloud. You will process incoming data using rule-

based systems for procedural anomaly detection

according to the parameters and machine learning

models for threat detection. Load balancing, fault

tolerance, etc. provide high availability. Also, there is

an API layer to avoid interoperability issues using

which code has been created for multiple IoT devices

and industrial machines. It's the development

lifecycle with continuous security patching and

updating as the ultimate line of defence to keep the

system up to snuff against the latest vulnerabilities.

During the Dataset Design process, you document

not only the design of the business process, but the

relevant tables and fields of a dataset. Read the topic

to get an idea about helpful dataset is the backbone of

policy and procedural governance so you can

understand it state of the migration to be. The Edge

Integrated Cloud Security (AEICS) is a highly

advanced solution that secures the cloud by providing

cutting-edge encryption and access control

mechanisms. It employs multi-factor authentication

(MFA) to provide an extra layer of security and

employs role-based access controls (RBAC) to restrict

who has access to what content for users connecting

to the cloud. Continuous monitoring systems can

monitor for anomalous data access patterns, which can

help detect both benign activity as well as malicious

action, in real-time. This module provides protection

against the external attack as it concerns cloud-native

security protection devices (such as firewalls and

intrusion detection/prevention systems (IDPS)). It will

have encryption of cloud-based data, both in transit

and at rest for end-to-end security. In this module, we

include protection measures for data loss in case of an

incident, such as backup and disaster recovery

strategies. Policies undergo frequent reviews to stay

ahead of emerging cloud security threats. Compliance

with Laws is Maintained Cloud security is aligned

with compliance standards and ensures compliance

with the law and other regulations.

Edge Security aims to protect the sensors,

machines, and Internet of Things (IoT) devices at the

periphery of the manufacturing environment. Each

device is protected by encrypted communication

channels and secure authentication mechanisms,

meaning no data can be intercepted in transit to the

cloud. For device level cyber-attacks, localized

security measures like threat detection and prevention

system can assist in preventing these types of attacks.

This module also includes firmware integrity checks,

which help prevent edge devices from being infected

with malware or from downloading unauthorized

software updates. Real-time monitoring can rapidly

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detect any vulnerabilities or breaches at the edge. opt

for lightweight security protocols for optimal device

performance and less waiting time. Further down the

line, minimize your cloud dependency by letting edge

devices run independently with local security controls.

They are updated and patched regularly to ensure that

the edge devices are protected from continuously

changing threats. This module protects the privacy,

authenticity, and secrecy of cloud manufacturing

system data. It applies strong encryption mechanisms

to the data in transit and at rest to avoid unauthorised

access to important production data. The module also

includes measures for data security, such as

tokenization and data anonymization at the individual

level.

Data access is tightly controlled with the

mechanism of access control and constant monitoring

and auditing of data activity for leakage, illegal

access, etc. We periodically review and enhance our

policies to align with privacy legislation such as

GDPR, CCPA, etc. Data Retention Policies: These

policies are introduced to automatically delete old data

while retaining data that's of utmost necessity. Secure

data exchange protocols should exist between cloud

and edge devices to ensure sensitive data doesn't end

up in the wrong hands. This will also seamlessly

ensure ethical and transparent data processing through

user consent management. The purpose of testing is

to discover mistakes. This approach works because

the purpose of testing is to discover vulnerabilities in

a product or service, including all that exist. Software

testing is the process of stroking a software system to

ensure it meets users’ expectations and criteria without

failing in an undesired way. It is a method to verify

operations of components, subassemblies, assemblies

and the end product.

4 RESULTS AND DISCUSSION

The paper proposed a cloud edge-based service

architecture called Advanced Edge Integrated Cloud

Security (AEICS), that is intended for service

orchestration in practical contexts and the proposed

design enhances service delivery and efficiency by

leveraging the benefits of cloud and edge computing.

The utilization of edge computing architectures in the

development of corporate management systems has

garnered research interest. These investigations

investigate the practicality, objectives, user

requirements, and functional architecture of these

systems. By employing peripheral computing, these

systems aim to reduce latency and enhance data

processing efficiency, thereby generating more

responsive corporate management solutions. Another

area of interest is the deployment of deep learning

within cloud-edge collaborative architectures, as this

approach addresses critical technologies, challenges,

and applications, thereby influencing future research

directions. Federated learning improves privacy and

security by enabling decentralized data processing

through the utilization of distributed systems. Secure

Access Service Edge (SASE) is a cloud service that

combines comprehensive security features with the

capabilities of a wide area network (WAN). SASE

systems intend to reduce complexity, enhance

performance, increase cost efficiency, and provide

universal access by consolidating security and

networking functions at the periphery. Cloud and

peripheral computing introduce numerous security

concerns, including data intrusions, privacy

violations, and access control infractions. In order to

address these concerns, it is necessary to implement

robust security policies that are specifically designed

to address the unique requirements of edge computing

systems. The study collectively underscores the

importance of the developments and factors that must

be considered when developing server management

systems, which include enhanced security features

that integrate cloud and periphery computing. Figure

3 illustrates the data security evaluation outcome of

the proposed scheme, AEICS, which is cross-

validated with the conventional approach, CMS, to

assess the data security of the proposed scheme. Table

1 is a descriptive representation of the

aforementioned.

Table 1: Analysis of Data Security.

Data Size (bps) CMS (%) AEICS (%)

500 85.13 97.57

1000 84.14 97.53

1500 82.49 97.46

2000 83.54 97.51

2500 85.56 97.47

3000 82.47 97.39

3500 84.52 98.66

4000 85.26 97.42

4500 84.45 98.54

5000 83.37 97.39

5500 83.12 98.16

The proposed scheme, AEICS, is cross-validated

with the conventional approach, CMS, to assess the

privacy of cloud servers. The privacy evaluation

outcome is illustrated in Figure 4. Table 2 is a

descriptive representation of the same.

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Figure 3: Data Security.

Table 2: Analysis of Privacy Between Aeics and Cms.

Data Size (bps) CMS (%) AEICS (%)

500 82.64 96.52

1000 81.56 96.71

1500 82.37 96.34

2000 81.66 96.51

2500 82.39 97.09

3000 84.69 96.72

3500 89.52 96.88

4000 88.31 96.95

4500 87.56 97.01

5000 88.09 97.08

5500 85.63 97.14

Figure 4: Privacy Ratio Analysis.

The data processing efficiency evaluation result of

the proposed scheme, AEICS, is shown in Figure 5.

This figure represents a cross-validation of the

proposed scheme with its conventional counterpart,

CMS, in order to evaluate its data processing

efficiency. Table 3 Shows the Analysis of Data

Processing Efficiency.

Table 3: Analysis of Data Processing Efficiency.

Data Size (bps) CMS (%) AEICS (%)

500 79.46 98.39

1000 78.52 98.14

1500 79.59 97.63

2000 78.71 97.79

2500 77.64 98.09

3000 77.75 97.45

3500 77.40 97.45

4000 77.06 97.31

4500 76.71 98.41

5000 76.37 98.12

5500 77.12 98.73

Figure 5: Data Processing Efficiency.

The processing time test result of the proposed

scheme based on AEICS is illustrated in the following

figure, Figure 6. A cross-validation is employed on

this scheme against conventional scheme (CMS) to

check the processing time. The next table 4 presents

a descriptive summary for the same.

Table 4: Data Processing Time Efficiency.

Data Size (bps) CMS (s) AEICS (s)

500 6 1

1000 8 1

1500 11 1

2000 12 2

2500 12 4

3000 13 4

3500 15 4

4000 16 8

4500 17 9

5000 17 9

5500 18 11

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COMMUNICATION, AND COMPUTING TECHNOLOGIES

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Figure 6: Processing Time Efficiency.

Figure 7: Performance Ratio Evaluation.

Table 5: Comparison of Performance Ratio Between Aeics and

Cms.

Data Size (bps) CMS (%) AEICS (%)

500 82.56 97.54

1000 82.47 97.47

1500 83.09 97.63

2000 83.14 97.69

2500 82.76 97.74

3000 83.07 97.80

3500 83.20 97.86

4000 83.29 97.92

4500 83.39 97.99

5000 83.49 98.05

5500 83.59 98.11

We have shown the performance ratio evaluation

result of the proposed scheme AEICS, in the above

figure, Figure 7. The ratio of performance is

measured by applying this scheme where it is cross-

validated with the conventional approach CMS. The

same is descriptively represented in Table 5 above.

5 CONCLUSION AND FUTURE

SCOPE

This research discusses advanced network topologies

with distributed controllers that secure their basis on

software-defines networking (SDN). Also, this

research has a significance in the field of Internet of

Things (IoT) and Ad-Hoc networks. We first

presented a much-needed new design called AEICS,

that is based on balance among a variety of software-

defined network controllers. Second, we proposed a

scalable design supporting multiple SDN domains

There can be various controllers per domain, and

networks can be associated with a domain whether or

not the domain has infrastructure. This is where edge

controllers come in: Controllers that specialize in

allowing data to pass between domains. If a failure

occurs, these edge controllers need to enter a new

distributed interaction to ensure each domain's

independence. We designed an architecture that

utilizes the grid of security concept at each controller,

to protect the full network from attacks. In future

work, we will explore more closely the characteristics

of the extended SDN-Domain, including the possible

applications of other security technologies in the SDN

domain, in addition to the exploration of other

security technologies. We would also like to do more

extensive testing at a bigger scale to fine-tune the

architecture in line with the enhanced AI based

architectural framework. Our team will build and

conduct terrestrial trials to this architecture.

In the forthcoming work, we will develop further

into the features of the expanded SDN domain,

analyze the potential applications of other security

methods within the SDN framework, and investigate

other security methods. In order to enhance our

system design, we plan to conduct additional testing

on a larger scale and leverage the AI based

architectural framework to a greater extent.

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