Recent Research Reviews Journal
IRO Journals
Volume 2 — Issue 1 — June 2023

A Comprehensive Review -Application of Bio-inspired Algorithms for Cyber Threat Intelligence Framework

https://doi.org/10.36548/rrrj.2023.1.08
Manas Kumar Yogi
Computer Science and Engineering Department, Pragati Engineering College(A), Surampalem, A.P
Dwarampudi Aiswarya
Computer Science and Engineering Department, Pragati Engineering College(A), Surampalem, A.P
PDF
PDF

How to Cite

Yogi, Manas Kumar, and Dwarampudi Aiswarya. 2023. “A Comprehensive Review -Application of Bio-Inspired Algorithms for Cyber Threat Intelligence Framework”. Recent Research Reviews Journal 2 (1): 101-11. https://doi.org/10.36548/rrrj.2023.1.08.
ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver AMA

Download Citation

Endnote/Zotero/Mendeley (RIS) BibTeX

Keywords

— Cyber
— Threat
— Bio-inspired
— Trust
— Security
— Intelligence
Published: 26-06-2023

Abstract

In most of the modern-day computing systems, security enhancements are a part of security design. The majority of the effort in providing robust security to a system is involved in the identification of cyber threats and how to recover from such cyberattacks. Many researchers have proposed sub-optimal strategies which has been the motivation of this research. This study summarises the research gaps and proposes research direction for mitigating the challenges concerned in that direction. This work reviews the current methodologies to provide a framework which can auto identify cyber threats and to determine how the bio-inspired algorithms can be applied to minimize the effort involved in identification and recovery from cyberattacks. Cyber threat intelligence frameworks serve as crucial elements in providing secure operating environment for the cyber practitioners. The design and development of cyber threat intelligence framework is challenging not only for the cost and effort involved in it but also due to intrinsic dependent entities of cyber security. This study proposes novel principles for bridging the identified research gaps through feature engineering, trust computing base, and bio-inspired based time optimization. There is a lot of research potential in this direction and this study is a sincere and ideal attempt towards the same goal.

References

  1. Apurv Singh Gautam, Yamini Gahlot, and Pooja Kamat. Hacker forum exploit and classification for proactive cyber threat intelligence. In International Conference on Inventive Computation Technologies, pages 279–285. Springer,2019.
  2. Liang Guo, Senhao Wen, Dewei Wang, Shanbiao Wang, Qianxun Wang, and Hualin Liu. Overview of cyber threat intelligence description. In International Conference on Applications and Techniques in Cyber Security and Intelligence, pages 343–350. Springer, 2021.
  3. Mauro Conti, Tooska Dargahi, and Ali Dehghantanha. Cyber threat intelligence: challenges and opportunities. In Cyber Threat Intelligence, pages 1–6. Springer, 2018.
  4. Ajay Modi, Zhibo Sun, Anupam Panwar, Tejas Khairnar, Ziming Zhao, Adam Doupé, Gail-Joon Ahn, and Paul Black. Towards automated threat intelligence fusion. In 2016 IEEE 2nd International Conference on Collaboration and Internet Computing (CIC), pages 408–416. IEEE, 2016.
  5. Asif Karim, Sami Azam, Bharanidharan Shanmugam, Krishnan Kannoorpatti, and Mamoun Alazab. A comprehensive survey for intelligent spam email detection. IEEE Access, 7:168261–168295, 2019.
  6. Asif Karim, Sami Azam, Bharanidharan Shanmugam, and Krishnan Kannoorpatti. Efficient clustering of emails into spam and ham: The foundational study of a comprehensive unsupervised framework. IEEE Access, 8:154759–154788, 2020.
  7. Char Sample, Jennifer Cowley, Tim Watson, and Carsten Maple. Re-thinking threat intelligence. In 2016 International Conference on Cyber Conflict (CyCon US), pages 1–9. IEEE, 2016.
  8. Vasileios Mavroeidis and Siri Bromander. Cyber threat intelligence model: an evaluation of taxonomies, sharing standards, and ontologies within cyber threat intelligence. In 2017 European Intelligence and Security Informatics Conference (EISIC), pages 91–98. IEEE, 2017.
  9. Gartner Inc. Definition: Threat intelligence, May 2013. https://www.gartner.com/en/documents/2487216/definitionthreat-intelligence.
  10. TianyiWang and Kam Pui Chow. Automatic tagging of cyber threat intelligence unstructured data using semantics extraction. In 2019 IEEE International Conference on Intelligence and Security Informatics (ISI), pages 197–199. IEEE, 2019.
  11. Rauf, Usman. "A taxonomy of bio-inspired cyber security approaches: existing techniques and future directions." Arabian Journal for Science and Engineering 43.12 (2018): 6693-6708.
  12. Mthunzi, Siyakha N., et al. "A bio-inspired approach to cyber security." Machine Learning for Computer and Cyber Security. CRC Press, 2019. 75-104.
  13. de Sá, Alan Oliveira, Luiz FR da C. Carmo, and Raphael CS Machado. "Bio-inspired active system identification: a cyber-physical intelligence attack in networked control systems." Mobile Networks and Applications 25.5 (2020): 1944-1957.
  14. Nicolaou, Andreas, Stavros Shiaeles, and Nick Savage. "Mitigating insider threats using bio-inspired models." Applied Sciences 10.15 (2020): 5046.
  15. Balasaraswathi, Veeran Ranganathan, Muthukumarasamy Sugumaran, and Yasir Hamid. "Feature selection techniques for intrusion detection using non-bio-inspired and bio-inspired optimization algorithms." Journal of Communications and Information Networks 2 (2017): 107-119.
  16. Otor, Samera Uga, et al. "An improved bio-inspired based intrusion detection model for a cyberspace." Cogent Engineering 8.1 (2021): 1859667.
  17. Balasaraswathi, M., et al. "Internet of things (Iot) based bio-inspired artificial intelligent technique to combat cybercrimes: a review." Internet of Things in Smart Technologies for Sustainable Urban Development (2020): 141-155.