An overview of cyber security data science from a perspective of machine learning
                     An Overview of 
Cyber  Security 
Data Science  from 
a Perspective of  
Machine Learning
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Introducti
on
Machine learning tasks in 
cyber  security
Today  Supervised learning
Discussio Unsupervised learning
n Neural networks and 
deep  learning
Conclusion and future 
work
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Introduction
The information and communication technology (ICT) 
sector has  advanced significantly over the past fifty 
years and is now  pervasive and tightly intertwined with 
our contemporary society.
As a result, the security policymakers have recently 
shown a great  deal of worry over the protection of ICT 
applications and systems  from cyber-attacks.
Cyber security is currently a term used to describe the 
process of  defending ICT systems from multiple cyber 
threats or attacks.
The analysis of various cyber-attacks and the 
development of  defense techniques that preserve 
several qualities described as  below are the main 
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01 02
. .
Information access and 
Integrity is a quality that 
disclosure  to unauthorized 
helps to  stop any 
parties, systems, or  entities 
unauthorized changes to  or 
are prevented by the  
deletions of data.
confidentiality attribute.
03.
A property called availability 
is  used to guarantee 
prompt and  dependable 
access to data assets  and 
systems for a designated  
entity.
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The word "cyber security" refers to a range of 
situations,  including commercial and mobile 
computers, and can be  broken down into a 
number of standard categories.
These include information security, which primarily 
focuses on  the security and privacy of pertinent 
data, application security,  which considers keeping 
software and devices free of risks or  cyber-threats, 
network security, which primarily focuses on  
protecting a computer system from cyber attackers 
or  intruders, and operational security, which also 
includes the  procedures for handling and protecting 
data assets.
Network security devices and computer security 
systems with  a firewall, antivirus programme, or 
intrusion detection system  make up typical cyber 
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Machine learning 
tasks  in cyber 
secMuacrhiintey learning (ML) is sometimes regarded as a 
subset of  "Artificial Intelligence," and it is strongly 
related to data science,  data mining, and 
computational statistics.
It focuses on teaching computers to recognize patterns 
from data.  Machine learning models, which could be 
crucial in the field of  cyber security, often consist of a 
collection of rules, techniques, or  intricate "transfer 
functions" that can be used to uncover  interesting data 
patterns or to recognize or anticipate behavior.
Here, we'll go through various approaches for handling 
machine  learning problems and how they relate to 
cyber security issues  (Assistance, 2022).
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Supervised 
learWnheinn spgecified goals are established to achieve from a 
particular  set of inputs, or when using a task-driven 
approach, supervised  learning is carried out.
Regression and classification methods are the most 
widely used  supervised learning techniques in the field 
of machine learning.  These methods are frequently 
used to categorize or forecast the  future of a specific 
security issue.
For instance, classification methods can be utilized in 
the cyber  security field to forecast denial-of-service 
attacks (yes, no), or to  recognize various classes of 
malicious activities like scanning and  spoofing.
The well-known classification methods are ZeroR, 
OneR, Navies  Bayes, Decision Tree, K-nearest 
neighbors, Support Vector  Machines, Adaptive 
Boosting, and Logistic Regression.
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Unsupervised 
learFnindiinng pgatterns, frameworks, or knowledge in 
unlabeled data, or  using a data-driven strategy, are the 
main objective in  unsupervised learning problems.
Malware, a form of cyber-attack, hides itself in some 
ways,  changing its behavior constantly and 
autonomously to evade  detection.
Unsupervised learning methods like clustering can be 
used to  extract hidden structures and patterns from 
datasets to find clues  to such complex attacks.
Similar to this, clustering approaches can be helpful in 
locating  anomalies, finding and removing rules breaches, 
and noisy  examples in data. The well-liked hierarchical 
clustering techniques  employed in numerous application 
domains include single linkage  or complete linkages, K-
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Neural networks and 
deep  learning
Deep learning is a type of machine learning, a subset of 
artificial  intelligence that takes cues from biological neural 
The most widely used neural network algorithm is back  
networpksro speaegna t iinon t,h aen hdu amrtainfi cbiaral inne. ural networks (ANN) are 
extensively  employed in deep learning (Aversano et al., 
2021).
It executes learning on an input layer, one or more 
hidden layers,  and an output layer of a multi-layer feed-
forward neural network.  Deep learning performs better 
as the volume of security data  increases, which is the 
primary distinction between it and  traditional machine 
learning.
Typically, deep learning algorithms work best with vast 
amounts  of data, whereas machine learning 
techniques work well with  smaller datasets.
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Conclusion and 
future  work
The implementation of a strong framework that 
allows data-  driven decision making is the most 
To make such a framework capable of minimizing these 
crucial tapsrokb floerm as  s manadr to cffyebreinr g s aeuctuormitya tseyds taenmd  intelligent 
security services,  enhanced data analytics based on 
(Assistanmcea,c h2i0n2e1 l)e.arning approaches  must be taken into 
account.
As a result, developing a data-driven security model for 
a specific  security issue as well as related empirical 
evaluation to gauge the  model's efficacy and efficiency 
and determine its suitability for  use in actual 
application domains may be future works.
Further in order to develop a professional research prop
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reserved osal or  dissertation in cyber security applications kindly 
get in touch with  PhD assistance for a best and 
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standard service.
Reference
s
Alhayani, B., Jasim Mohammed, H., Zeghaiton Chaloob, I. & Saleh Ahmed, J. 2021. 
WITHDRAWN:  Effectiveness of artificial intelligence techniques against cyber security risks 
apply of IT industry. Materials  Today: Proceedings.
Assistance, P. 2021. Scope And Significance Of Data Science In 
Cybersecurity.  Assistance, P. 2022. The Contribution of Machine 
Learning in Cyber security.
Aversano, L., Bernardi, M.L., Cimitile, M. & Pecori, R. 2021. A systematic review on Deep 
Learning approaches  for IoT security. Computer Science Review. (40). pp. 100389.
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