Exploring Artificial Intelligence for Network Security: A Case Study of Malware Defence

Li Peng; Tuyatsetseg Badarch

doi:doi:10.11648/j.ajcst.20220502.22

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Exploring Artificial Intelligence for Network Security: A Case Study of Malware Defence

Li Peng, Tuyatsetseg Badarch

Published in American Journal of Computer Science and Technology (Volume 5, Issue 2)

Received: 5 April 2022 Accepted: 11 May 2022 Published: 24 May 2022

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Abstract

AI has many applications in network security. Network security is one of the most challenging situations. The paper carries out AI based network security analysis and prevention ways of the deep learning models in the network security. We focus on some specific AI applications including voice supervision of public network, malicious code monitoring, smartphone intrusion monitoring, HTTP security monitoring, mobile phone malicious APK code monitoring are bringing the solutions for network security. We studied there are powerful methods such as mobile phone malicious APR code monitoring employed Artificial Neural Network (ANN) model which detects and mitigates predictable and unpredictable DDoS attacks (TCP, UDP, and ICMP protocols). HTTP is running over TCP, then the web server can face many TCP-related attacks, therefore, we have an experiment of HTTP security monitoring, mobile phone malicious APK code monitoring. This paper presents a potential security threats from malicious uses of AI, and proposes ways to better prevent, and mitigate these threats. When planning HTTP service protection, we present it is important to keep in mind that the attack surface is much broader than just the HTTP protocol. We suggest promising areas for further research that could expand the AI based solutions for development of cloud computing-related technologies, and the combination of cloud computing and deep learning technology in the security area.

Published in	American Journal of Computer Science and Technology (Volume 5, Issue 2) This article belongs to the Special Issue Advances in Computer Science and Future Technology
DOI	10.11648/j.ajcst.20220502.22
Page(s)	108-114
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Network Security, AI Network Security, Deep Learning

References

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APA Style

Li Peng, Tuyatsetseg Badarch. (2022). Exploring Artificial Intelligence for Network Security: A Case Study of Malware Defence. American Journal of Computer Science and Technology, 5(2), 108-114. https://doi.org/10.11648/j.ajcst.20220502.22

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ACS Style

Li Peng; Tuyatsetseg Badarch. Exploring Artificial Intelligence for Network Security: A Case Study of Malware Defence. Am. J. Comput. Sci. Technol. 2022, 5(2), 108-114. doi: 10.11648/j.ajcst.20220502.22

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AMA Style

Li Peng, Tuyatsetseg Badarch. Exploring Artificial Intelligence for Network Security: A Case Study of Malware Defence. Am J Comput Sci Technol. 2022;5(2):108-114. doi: 10.11648/j.ajcst.20220502.22

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@article{10.11648/j.ajcst.20220502.22,
  author = {Li Peng and Tuyatsetseg Badarch},
  title = {Exploring Artificial Intelligence for Network Security: A Case Study of Malware Defence},
  journal = {American Journal of Computer Science and Technology},
  volume = {5},
  number = {2},
  pages = {108-114},
  doi = {10.11648/j.ajcst.20220502.22},
  url = {https://doi.org/10.11648/j.ajcst.20220502.22},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20220502.22},
  abstract = {AI has many applications in network security. Network security is one of the most challenging situations. The paper carries out AI based network security analysis and prevention ways of the deep learning models in the network security. We focus on some specific AI applications including voice supervision of public network, malicious code monitoring, smartphone intrusion monitoring, HTTP security monitoring, mobile phone malicious APK code monitoring are bringing the solutions for network security. We studied there are powerful methods such as mobile phone malicious APR code monitoring employed Artificial Neural Network (ANN) model which detects and mitigates predictable and unpredictable DDoS attacks (TCP, UDP, and ICMP protocols). HTTP is running over TCP, then the web server can face many TCP-related attacks, therefore, we have an experiment of HTTP security monitoring, mobile phone malicious APK code monitoring. This paper presents a potential security threats from malicious uses of AI, and proposes ways to better prevent, and mitigate these threats. When planning HTTP service protection, we present it is important to keep in mind that the attack surface is much broader than just the HTTP protocol. We suggest promising areas for further research that could expand the AI based solutions for development of cloud computing-related technologies, and the combination of cloud computing and deep learning technology in the security area.},
 year = {2022}
}

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TY  - JOUR
T1  - Exploring Artificial Intelligence for Network Security: A Case Study of Malware Defence
AU  - Li Peng
AU  - Tuyatsetseg Badarch
Y1  - 2022/05/24
PY  - 2022
N1  - https://doi.org/10.11648/j.ajcst.20220502.22
DO  - 10.11648/j.ajcst.20220502.22
T2  - American Journal of Computer Science and Technology
JF  - American Journal of Computer Science and Technology
JO  - American Journal of Computer Science and Technology
SP  - 108
EP  - 114
PB  - Science Publishing Group
SN  - 2640-012X
UR  - https://doi.org/10.11648/j.ajcst.20220502.22
AB  - AI has many applications in network security. Network security is one of the most challenging situations. The paper carries out AI based network security analysis and prevention ways of the deep learning models in the network security. We focus on some specific AI applications including voice supervision of public network, malicious code monitoring, smartphone intrusion monitoring, HTTP security monitoring, mobile phone malicious APK code monitoring are bringing the solutions for network security. We studied there are powerful methods such as mobile phone malicious APR code monitoring employed Artificial Neural Network (ANN) model which detects and mitigates predictable and unpredictable DDoS attacks (TCP, UDP, and ICMP protocols). HTTP is running over TCP, then the web server can face many TCP-related attacks, therefore, we have an experiment of HTTP security monitoring, mobile phone malicious APK code monitoring. This paper presents a potential security threats from malicious uses of AI, and proposes ways to better prevent, and mitigate these threats. When planning HTTP service protection, we present it is important to keep in mind that the attack surface is much broader than just the HTTP protocol. We suggest promising areas for further research that could expand the AI based solutions for development of cloud computing-related technologies, and the combination of cloud computing and deep learning technology in the security area.
VL  - 5
IS  - 2
ER  -

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Author Information

Li Peng

School of Information Technology and Design, Mongolian National University, Ulaanbaatar, Mongolia
Tuyatsetseg Badarch

School of Information Technology and Design, Mongolian National University, Ulaanbaatar, Mongolia

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Plain Text BibTeX RIS

APA Style

Li Peng, Tuyatsetseg Badarch. (2022). Exploring Artificial Intelligence for Network Security: A Case Study of Malware Defence. American Journal of Computer Science and Technology, 5(2), 108-114. https://doi.org/10.11648/j.ajcst.20220502.22

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ACS Style

Li Peng; Tuyatsetseg Badarch. Exploring Artificial Intelligence for Network Security: A Case Study of Malware Defence. Am. J. Comput. Sci. Technol. 2022, 5(2), 108-114. doi: 10.11648/j.ajcst.20220502.22

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AMA Style

Li Peng, Tuyatsetseg Badarch. Exploring Artificial Intelligence for Network Security: A Case Study of Malware Defence. Am J Comput Sci Technol. 2022;5(2):108-114. doi: 10.11648/j.ajcst.20220502.22

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@article{10.11648/j.ajcst.20220502.22,
  author = {Li Peng and Tuyatsetseg Badarch},
  title = {Exploring Artificial Intelligence for Network Security: A Case Study of Malware Defence},
  journal = {American Journal of Computer Science and Technology},
  volume = {5},
  number = {2},
  pages = {108-114},
  doi = {10.11648/j.ajcst.20220502.22},
  url = {https://doi.org/10.11648/j.ajcst.20220502.22},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20220502.22},
  abstract = {AI has many applications in network security. Network security is one of the most challenging situations. The paper carries out AI based network security analysis and prevention ways of the deep learning models in the network security. We focus on some specific AI applications including voice supervision of public network, malicious code monitoring, smartphone intrusion monitoring, HTTP security monitoring, mobile phone malicious APK code monitoring are bringing the solutions for network security. We studied there are powerful methods such as mobile phone malicious APR code monitoring employed Artificial Neural Network (ANN) model which detects and mitigates predictable and unpredictable DDoS attacks (TCP, UDP, and ICMP protocols). HTTP is running over TCP, then the web server can face many TCP-related attacks, therefore, we have an experiment of HTTP security monitoring, mobile phone malicious APK code monitoring. This paper presents a potential security threats from malicious uses of AI, and proposes ways to better prevent, and mitigate these threats. When planning HTTP service protection, we present it is important to keep in mind that the attack surface is much broader than just the HTTP protocol. We suggest promising areas for further research that could expand the AI based solutions for development of cloud computing-related technologies, and the combination of cloud computing and deep learning technology in the security area.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Exploring Artificial Intelligence for Network Security: A Case Study of Malware Defence
AU  - Li Peng
AU  - Tuyatsetseg Badarch
Y1  - 2022/05/24
PY  - 2022
N1  - https://doi.org/10.11648/j.ajcst.20220502.22
DO  - 10.11648/j.ajcst.20220502.22
T2  - American Journal of Computer Science and Technology
JF  - American Journal of Computer Science and Technology
JO  - American Journal of Computer Science and Technology
SP  - 108
EP  - 114
PB  - Science Publishing Group
SN  - 2640-012X
UR  - https://doi.org/10.11648/j.ajcst.20220502.22
AB  - AI has many applications in network security. Network security is one of the most challenging situations. The paper carries out AI based network security analysis and prevention ways of the deep learning models in the network security. We focus on some specific AI applications including voice supervision of public network, malicious code monitoring, smartphone intrusion monitoring, HTTP security monitoring, mobile phone malicious APK code monitoring are bringing the solutions for network security. We studied there are powerful methods such as mobile phone malicious APR code monitoring employed Artificial Neural Network (ANN) model which detects and mitigates predictable and unpredictable DDoS attacks (TCP, UDP, and ICMP protocols). HTTP is running over TCP, then the web server can face many TCP-related attacks, therefore, we have an experiment of HTTP security monitoring, mobile phone malicious APK code monitoring. This paper presents a potential security threats from malicious uses of AI, and proposes ways to better prevent, and mitigate these threats. When planning HTTP service protection, we present it is important to keep in mind that the attack surface is much broader than just the HTTP protocol. We suggest promising areas for further research that could expand the AI based solutions for development of cloud computing-related technologies, and the combination of cloud computing and deep learning technology in the security area.
VL  - 5
IS  - 2
ER  -

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