TARU PUBLICATIONS
Journal of Information and Optimization Sciences cover
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

Powered by:Powered by

Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
submissions@tarupublications.com
Open Access Research Article

Sentinel AI : Pioneering cyber threat detection and mitigation through advanced machine learning

* , , , , ,

* Corresponding author · click or hover a name for details

pp. 957–968Vol. 46Issue 4-AMay 2025DOI: 10.47974/JIOS-1823XML
Received:
16 Oct 2024
Published Online:
31 May 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1823
Pages:
957–968

Abstract

The Covid-19 pandemic has inevitably caused a sharp increase in the use of digital technologies as only manner of safeguarding ourselves. Covid-19 has forced numerous corporations and companies to adapt to remote work life. Owing to the trends towards digitalization of the key sectors of the economy nowadays, the leading enterprises and schools gradually switch to a remote work. The conclusion on the usage of AI and ML in the cybersecurity system is critical to triumph over the cyber threat and avoiding threats to valuable assets in the developed countries. In this article the author introduces a new product called Sentinel AI that implements machine learning for security purposes. The above system uses both supervised and unsupervised learning techniques which includes the deep learning, anomaly detection as well as the behavioral analysis to detect the known as well as the unknown threats. Since Sentinel AI can learn data patterns consistently, better predictions of threats can be done as well as attempts at faking positive results are slimmer, and hence, it is less susceptible to threats. Next-generation technologies along with the examples of their application and the method of improving the cybersecurity based on the anticipating and preventing threats has also been described in this paper.

Keywords

Subject Classifications

Primary 68T20Secondary 68M25

References

[1] I. C. Eian, L. K. Yong, M. Y. X. Li, Y. H. Qi, and F. Z, “Cyber Attacks in the Era of COVID-19 and Possible Solution Domains,” www.preprints.org (Sep. 2020), doi:https://doi.org/10.20944/preprints202009.0630.v1.
[2] Yusuf Yusuf Dayyabu, D. Arumugam, and Suresh Balasingam, “The application of artificial intelligence techniques in credit card fraud detection: a quantitative study,” The application of artificial intelligence techniques in credit card fraud detection: a quantitative study, vol. 389, pp. 07023–07023 (Jan. 2023), doi:https://doi.org/10.1051/e3sconf/202338907023.
[3] C. Obi, V. Akagha, S. Onimisi, A. Chigozie, N. S. Onwusinkwue, and A. Ibrahim, “Comprehensive review on cybersecurity: modern threats and advanced defense strategies,” Computer science & IT research journal, vol. 5, no. 2, pp. 293–310 (Feb. 2024), doi:https://doi.org/10.51594/csitrj.v5i2.758.
[4] S. Kumar, U. Gupta, A. Singh, and A. K. Singh, “Artificial Intelligence,” Journal of Computers Mechanical and Management, vol. 2, no. 3, pp. 31–42 (Aug. 2023), doi:https://doi.org/10.57159/gadl.jcmm.2.3.23064.
[5] M. M. Yamin, B. Katt, and V. Gkioulos, “Cyber ranges and security testbeds: Scenarios, functions, tools and architecture,” Computers & Security, vol. 88, p. 101636 (Jan. 2020), doi:https://doi.org/10.1016/j.cose.2019.101636.
[6] M. A. Khan and K. Salah, “IoT security: Review, blockchain solutions, and open challenges,” Future Generation Computer Systems, vol. 82, no. 3, pp. 395–411 (May 2018), doi:https://doi.org/10.1016/j.future.2017.11.022.
[7] M. Rege, R. Blanch, and K. Mbah, “Machine Learning for Cyber Defense and Attack.https://personales.upv.es/thinkmind/dl/conferences/dataanalytics/data_analytics_2018/data_analytics_2018_5_30_60121.pdf.
[8] M. Chowdhury, A. Rahman, and R. Islam, “Malware Analysis and Detection Using Data Mining and Machine Learning Classification,” Advances in Intelligent Systems and Computing, pp. 266–274 (Oct. 2017 ), doi:https://doi.org/10.1007/978-3-319-67071-3_33.
[9] R. Kaur, D. Gabrijelčič, and T. Klobučar, “Artificial intelligence for cybersecurity: Literature review and future research directions,” Information Fusion, vol. 97, no. 101804, pp. 1–29 (2023), doi:https://doi.org/10.1016/j.inffus.2023.101804.
[10] Mohammed Nasser Al-Mhiqani, Rabiah Ahmad, Warusia Yassin, Aslinda Hassan, Zaheera Zainal Abidin, Nabeel Salih Ali and Karrar Hameed Abdulkareem, “Cyber-Security Incidents: A Review Cases in Cyber-Physical Systems” International Journal of Advanced Computer Science and Applications(ijacsa), 9(1) (2018).
[11] S. Hariharan, A. Velicheti, A. S. Anagha, C. Thomas, and N. Balakrishnan, “Explainable Artificial Intelligence in Cybersecurity: A Brief Review,” IEEE Xplore, Oct. 01 (2021). doi:https://doi.org/10.1109/ISEA-ISAP54304.2021.9689765.
[12] Lallie HS, Shepherd LA, Nurse JRC, Erola A, Epiphaniou G, Maple C, Bellekens X. Cyber security in the age of COVID-19: A timeline and analysis of cyber-crime and cyber-attacks during the pandemic. Comput Secur. 2021 Jun; 105 : 102248. doi: 10.1016/j.cose.2021.102248. Epub 2021 Mar 3. PMID: 36540648; PMCID: PMC9755115. https://pmc.ncbi.nlm.nih.gov/articles/PMC9755115/
[13] R. Mishra, “Cyber Security Threat Detection Model Using Artificial Intelligence Technology,” (Jul. 2023). doi:https://doi.org/10.1109/icecaa58104.2023.10212209.
[14] M. Farouk, R. H. Sakr, and Noha Hikal, “Identifying the most accurate machine learning classification technique to detect network threats,” Neural Computing and Applications, vol. 36, no. 16, pp. 8977–8994 (Mar. 2024), doi:https://doi.org/10.1007/s00521-024-09562-9.
[15] P. Singh, S. K. Borgohain, A. K. Sarkar, J. Kumar, and L. D. Sharma, “Feed-Forward Deep Neural Network (FFDNN)-Based Deep Features for Static Malware Detection,” International Journal of Intelligent Systems, vol. 2023, pp. 1–20 (Feb. 2023), doi:https://doi.org/10.1155/2023/9544481.
[16] J. Li, “Cyber security meets artificial intelligence: a survey,” Frontiers of Information Technology & Electronic Engineering, vol. 19, no. 12, pp. 1462–1474 (Dec. 2018), doi: https://doi.org/10.1631/fitee.1800573.
[17] Diptiban Ghillani, “Deep Learning and Artificial Intelligence Framework to Improve the Cyber Security,” (Sep. 2022), doi: https://doi.org/10.22541/au.166379475.54266021/v1.
[18] S. Islam, M. A. Hayat, and M. F. Hossain, “Artificial intelligence for cybersecurity: impact, limitations and future research directions,” Dec. 27 (2023). doi: https://doi.org/10.5281/zenodo.10448268.
[19] G. Rekha, S. Malik, A. K. Tyagi, and M. M. Nair, “Intrusion Detection in Cyber Security: Role of Machine Learning and Data Mining in Cyber Security,” Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 72–81 (2020), doi: https://doi.org/10.25046/aj050310
[20] Tariq Bishtawi and Reem Alzubi, “Cyber Security of Mobile Applications Using Artificial Intelligence,” (Nov. 2022), doi:https://doi.org/10.1109/eiceeai56378.2022.10050484.
[21] M. Marripudugala, “AI-Powered Fraud Detection in the Financial Services Sector: A Machine Learning Approach,” 2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), pp. 795–799 (Oct. 2024), doi: https://doi.org/10.1109/icssas64001.2024.10760599
[22] K. R. Bhatele, H. Shrivastava, and N. Kumari, “The Role of Artificial Intelligence in Cyber Security,” Countering Cyber Attacks and Preserving the Integrity and Availability of Critical Systems, pp. 170–192 (2019), doi: https://doi.org/10.4018/978-1-5225-8241-0.ch009.
[23] E.-C. University, “Machine Learning in Cybersecurity: How it Works and What Companies Need to Know,” Accredited Online Cyber Security Degree Programs | EC-Council University, Oct. 12 (2023). https://www.eccu.edu/blog/cybersecurity/machine-learning-in-cybersecurity/.
[24] E. Oye, P. Peace, and J. Owen, “The Role of Natural Language Processing in Cybersecurity,” ResearchGate, Dec. 20 (2024). https://www.researchgate.net/publication/387252722_The_Role_of_Natural_Language_Processing_in_Cybersecurity.
[25] H. Liu and B. Lang, “Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey,” Applied Sciences, vol. 9, no. 20, p. 4396 (Oct. 2019), doi:https://doi.org/10.3390/app9204396.
[26] S Senthil Pandi, M.S. Monesh, and B. Lingesh, “A Novel Approach to Detect Face Fraud Detection Using Artificial Intelligence,” pp. 1–6 (Feb. 2024), doi: https://doi.org/10.1109/ic-etite58242.2024.10493594.
[27] M. Kalirane, “Ensemble Learning Methods: Bagging, Boosting and Stacking,” Analytics Vidhya, Jan. 20 (2023). https://www.analyticsvidhya.com/blog/2023/01/ensemble-learning-methods-bagging-boosting-and-stacking/.
[28] Mahmoud Mahfuri, Sameh Ghwanmeh, Rsha Almajed, Waseem Alhasan, Mohammed Salahat, and Jin Hie Lee, “Transforming Cybersecurity in the Digital Era: The Power of AI,” (Feb. 2024), doi: https://doi.org/10.1109/iccr61006.2024.10533072.

Views: 269Downloads: 80Citations: 0