Improving Against Cyber-attack by Hackers in our tertiary institutions-Artificial Intelligence approach

Abstract

The rate at which hackers have devised means of increasing threats is becoming unbearable. Classified information is no longer confidential in financial institutions and academic environments. Government agencies and election organizers are no exception in these nefarious activities. The resultant effect is a decline in the number of subscribers that patronize the affected telecommunication companies. The threats could be minimized or prevented by introducing a smart means of checking to eliminate the threats to improve the security management in the higher institution or multimedia environment using intelligent agents. The methodology used to understand the nature of threats and types of threats observed in multimedia could be outlined as follows: Designing a smart rule base that will detect and reduce threats in multimedia, training Artificial Neural Network (ANN) in the multimedia rule base to increase the efficiency of the rules. The next stage is to design a SIMULINK model for improving security management in the higher institution using Artificial Intelligence (Neuro-fuzzy software). The results obtained are highest without Neuro-Fuzzy Controller with percentage threat level recorded as is 83% while that when Neuro-fuzzy is incorporated in the system is 81.18%. This clearly shows that there is an obvious percentage reduction in the number of threats when Neuro-fuzzy is incorporated into the system. The positive performance index was 1.82%.
KEYWORDS Improving, Cyber-attack, Hackers, Artificial Intelligence, Tertiary Institutions

Full text

American Journal of Engineering Research (AJER)
2022
American Journal of Engineering Research (AJER)
e-ISSN: 2320-0847 p-ISSN : 2320-0936
Volume-11, Issue-01, pp-194-199
www.ajer.org
Research Paper Open Access
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Improving Against Cyber-attack by Hackers in our
Tertiary institutions: Artificial Intelligence Approach
1Bakare Kazeem, Department of Electrical and Electronic Engineering, Enugu State University of
Science and Technology, (ESUT), Nigeria
2Ngang Bassey Ngang, Department of Electronics and ComputerEngineering, Veritas University,
Abuja, Nigeria.
ABSTRACT :
The rate at which hackers have devised means of increasing threats is becoming unbearable. Classified
information is no longer confidential in financial institutions and academic environments. Government agencies
and election organizers are no exception in these nefarious activities. The resultant effect is a decline in the
number of subscribers that patronize the affected telecommunication companies. The threats could be minimized
or prevented by introducing a smart means of checking to eliminate the threats to improve the security
management in the higher institution or multimedia environment using intelligent agents. The methodology used
to understand the nature of threats and types of threats observed in multimedia could be outlined as follows:
Designing a smart rule base that will detect and reduce threats in multimedia, training Artificial Neural
Network (ANN) in the multimedia rule base to increase the efficiency of the rules. The next stage is to design a
SIMULINK model for improving security management in the higher institution using Artificial Intelligence
(Neuro-fuzzy software). The results obtained are highest without Neuro-Fuzzy Controller with percentage threat
level recorded as is 83% while that when Neuro-fuzzy is incorporated in the system is 81.18%. This clearly
shows that there is an obvious percentage reduction in the number of threats when Neuro-fuzzy is incorporated
into the system. The positive performance index was 1.82%.
KEYWORDS Improving, Cyber-attack, Hackers, Artificial Intelligence, Tertiary Institutions.
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Date of Submission: 16-01-2022 Date of acceptance: 31-01-2022
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I. INTRODUCTION
Information security system, in the public or private sector, is of paramount importance to all national
security objectives. Such institutions provide the ability to collect, manage, and share valuable information
between multiple organizations that can build large e-commerce businesses and often participate in private
domain relationships [1]. Information shared between multiple domains can come in a variety of forms
including text, audio, video, and images that can increase the complexity of security and privacy management
[2]. Major security challenges include the integration of various security policies of cooperating organizations in
collaborative efforts to protect information and use shared information to detect and respond to any emerging
threats [3]. In addition, confidentiality of data is often a major problem [4]. In addition, some data analysts and
mining tools have suggested that cybercriminals can use it to extract sensitive information from a private and
confidential multimedia system and detect patterns and functions that reflect potential threats to infrastructure
[5]. Thus, two key challenges in the development of multimedia-based flexibility strategies for managing threats
and data mining and information security [6]. This study will use blockchain technology to improve security
management in the multimedia environment. Wireless ad networks represent live streaming systems that are
fully distributed without infrastructure [7]. Significant disruption occurs when multiple transfers occur with
links to the same or different codes, thus leading to additional problems and issues such as delay, jitter, limited
bandwidth, packet loss (packet loss), etc., which also affects service level (QoS) [8].Over the past few years,
wireless networks have attracted a lot of research across the network and social functioning [9]. Recently,
multimedia programs on ad hoc wireless have become increasingly common, but even largely the effects of
delays and packet loss challenges [10]. Low multimedia transmission quality caused by packet delay and loss of
voice traffic, for example, is still one of the major technical barriers to the voice communication system. Due to

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the growing popularity of wireless ad networks, QoS support for multimedia transmission has become an
important requirement because it is closely related to service satisfaction. [11] Introducing Security and Privacy
in the computer cloud where he analyzed the key security issues enveloped the market today with security
measures, in order to provide the server and business providers with the best possible solution. In the same
article, [12] introduced Data Privacy on cloud computing, which was a relatively new homomorphic encryption
system based on numbers. The encryption system can be used primarily to protect logical data from a cloud
computing. The proposed system uses a clear text space ring and a single encryption key to remove encryption,
that is, a standard encryption system. In order to understand the basics of distributed computerization and to
distance information from the cloud, a number of assets have been advised [13 [. In [15] the authors provide a
comprehensive insight into the basic concepts of distributed computation [16]. of threats identified in our area,
Designing a smart legal framework that will detect and reduce threats to multimedia, training ANN in the legal
framework designed to improve format efficiency, design a SIMULINK model for enhancing security
optimization in a multimedia environment using Neuro-fuzzy controller. Many tools are now in use for
improving the performance of the power electronics in the industry to enhance increased productivity; some of
these are, artificial intelligence, fuzzy logic, neural networks, hybrid networks, etc [17]. They have been recently
recognized as the important tools to improve the output performance of machines in the industrial sectors.
Utilization of these intelligent control with adaptiveness seems to yield a promising research area in the
development, implementation, and control of electrical drives.In the study conducted by [18], he affirmed that
fuzzy controller is an effective means of regulating system frequency; when combined alongside other control
devices would yield good results; hence being used for complementary functions with Artificial neural network
(ANN) our design would be effective and efficient. The work done in [19] does recommend that for any
successful design the methodology involved in the work must be followed step-by-step and adhered to the
specific objectives of the study; which has to do with the measurement of the collected data, classification, and
tabulation.Investment that will pay considerable dividends over the course of its operating life is a
comprehensive power monitoring system. Even though increased energy prices have become a larger influence
on the balance sheet, many facilities do not take advantage of opportunities to better manage these expenses.
Those without monitoring systems likely have no understanding of their energy usage; those with them may not
be using their systems to the fullest potential.
Because the quality of energy supplied can adversely affect its operation, oftentimes leading to loss or
degradation of equipment, product, revenue, and reputation, plant managers must weigh the advantages of
implementing a monitoring program.
The second section of this paper shows three methods for monitoring systems of solar plants. The third
section discusses communication and monitoring system for wind turbines, and finally the conclusion is
discussed in the fourth section.
II. METHODOLOGY
The procedure adopted in achieving the goal of this task is the step-by-step approach to the specific
objectives of the work; this entails collecting and weighing the threat levels using trending method; classifying the
threats and types, using the time series method approach from the service provider’s records in the database of the
company.
This involves characterizing the types of threats observed in the affected area, designing a rule base
that will detect and minimize threats in the multimedia environment, training Artificial Neural Network (ANN)
in the multimedia rule base to enhance the efficiency of the stated rules; finally designing a SIMULINK model
for improving the security system in a multimedia environment using Neuro-Fuzzy approach. Multimedia is
simply multiple forms of media integrated together. An example of multimedia is a web page with animation.
The basic types can be described as follows: Text, Graphics, Audio, Animation, Video, and Graphics Objects
(see: Computer graphics and visualization, etc. The threats can come from any source and has to be reported.
ii
2.1 Classifying the types of threats observed in multimedia
Table: 1 Characterized threats in a Multimedia environment
TYPES OF THREATS IN
MULTIMEDIA
% OF THREATS IN
MULTIMEDIA NETWORK
TIME OF THREAT
DATE OF
THREAT
DAYS OF
THREAT
Copy right
80%
1pm
6/11/2018
1
Hacking in to ones data or data
leakage
82%
2am
7/8/2019
2
Explotation of internate conection
70%
3pm
8/6/2019
3
Corruption of data
60%
1am
4/4/2019
4
Bonnets
60%
7am
5/10/2018
5
Distributed denial-of-service
(ddos)
75%
4PM
6/6/2018
6

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Malware
83%
2AM
7/10/2019
7
2.2 Designing a multimedia rule base that will detect and reduce threats in multimedia
Fig 1 designed multimedia Fuzzy inference system that will detect and reduce threats in multimedia
Fig 1 shows designed multimedia Fuzzy inference system that will detect and reduce threats in multimedia;with
two inputs of threats and multimedia and an output of result.
Fig 2 designed multimedia rule base that will detect and reduce threats in multimedia
Fig 2 shows designed multimedia rule base that will detect and reduce threats in multimedia. The rules are three
in number as shown in fig 2.

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2.3 Training ANN in the multimedia rule base to enhance the efficacy of the rules
Fig 3 trained ANN in the multimedia rule base to enhance the efficacy of the rules
Fig 3 shows trained ANN in the multimedia rule base to enhance the efficacy of the rules.
2.4 Designing a SIMULINK model for improving security management in a multimedia environment
using Neuro-fuzzy controller
Fig 4 designed SIMULINK model forimproving security management in a multimedia environment using
Neuro-fuzzy controller.
Fig 4 shows designed SIMULINK model forimproving security management in a multimedia environment using
Neuro-fuzzy controller. The results obtained after simulation are as shown in fig 5.
III. RESULTS AND DISCUSSION
Figure 1 shows a multimedia designed using Fuzzy Inference System that can detect and reduce threats in
multimedia. Figure 1 has both threats and multimedia as inputs and the result as output.
0.2 0.3 0.4 0.5 0.6 0.7 0.8
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
0.7
W(i,1)
W(i,2)
IMPROVING SECURITY MANAGEMENT IN A MULTIMEDIA ENVIRONMENT USING NEURO-FUZZY CONTROLLER
x{1}
Input 1
rulebasem
In1
Out1
In1Out1
In1
Out1
In1Out1
Subsystem5
In1
COPY RIGHT
HACKING INTO ONES DATA
EXPLOTATION OF INTERNATE
CORRUPTION OF DATA
BONNET
DISTRIBUTED DENIAL OF SERVICE
MALWARE
NO OF DAYS
In1Out1
In1
Out1
Subsystem2
In1
Out1
In1Out1
In1Out1
Subsystem13
In1Out1
In1
Out1
In1
Out1
In1
Out1
In1Out1
In1
In2
Out1
In1Out1
Neural Network
x{1} y{1}
NO OF DAYS
1
2
3
4
5
6
7
MALWARE1
81.18
In1
Out1
Out2
Out3
Out4
Out5
Out6
Out7
HACKING INTO ONES DATA
80.21
EXPLOTATION OF INTERNATE
68.47
DISTRIBUTED DENIAL OF SERVICE
73.36
CORRUPTION DATA
58.69
COPY RIGHT
78.25
Bernoulli Binary
Generator
Bernoulli
Binary
BONNET
58.69
Add
AWGN
Channel
AWGN
1

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Figure 2 shows the basics of multimedia rule designed to detect and reduce threats in multimedia.
Three rules are involved as displayed in figure 3. It shows ANN trained in the basics of multimedia rule to
improve the efficiency of the rules.
Figure 4 is a SIMULINK designed model for enhancing security management in a multimedia
environment using Neuro-fuzzy control. The results obtained after simulation are as shown in Figure 5. Figure 5
shows a comparison between a common threat and a Neuro-fuzzy threat in improving security management in a
multimedia environment. The average prevalence rate is 83% without a controller, when the controller is
included in the system we have 81.18%. With these results obtained, it shows that the percentage reduction in
the number of threats when Neuro-fuzzy is introduced into the system is 1.82%. Table 1 shows the types of
threats found in the multimedia; while Table 2 compares the common threat and the Neuro-fuzzy threat to
improving security management in a multimedia environment.
Table: 2 comparison between the conventional threat and Neuro-fuzzy threat in improving security
management in a multimedia environment
TIME (s)
CONVENTIONAL THREAT IN
IMPROVING SECURITY
MANAGEMENT IN A MULTIMEDIA
ENVIRONMENT
NEURO –FUZZY THREAT IN
IMPROVING SECURITY
MANAGEMENT IN A MULTIMEDIA
ENVIRONMENT
1
83
81.18
2
75
73.36
3
60
58.6
4
80
78.25
5
82
80.21
6
70
68.47
7
60
58.69
Fig 5 comparing conventional threat and Neuro-fuzzy threat in improving security management in a multimedia
environment. Fig 5 shows comparing conventional threat and Neuro-fuzzy threat in improving security
management
IV. CONCLUSION
The high rate of increase in the number of threats experienced in multimedia has led to its reduction in
the number of its subscribers. This number of threats observed in the multimedia that has drastically led to the
reduction of its subscribers is eradicated by an introduction of improving security management in a multimedia
environment using neuro-fuzzy controller. To achieve this, it is done in this manner, characterizing types of
threats observed in multimedia, designing a multimedia rule base that will detect and reduce threats in
multimedia, training ANN in the multimedia rule base to enhance the efficacy of the rules and designing a
SIMULINK model forimproving security management in a multimedia environment using Neuro-fuzzy
controller. The results obtained are the highest conventional percentage of threat is 83% while that when Neuro-
fuzzy is incorporated in the system is 81.18%. With these results obtained, it shows that the percentage
reduction in the number of threats when Neuro- fuzzy is incorporated in the system is 1.82%..
1 2 3 4 5 6 7
55
60
65
70
75
80
85
threat (%)
Time(s)
Conventional threat in improving security management in a multimedia environment
Neuro –fuzzy t hreat in improving security management in a multimedia environment

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