Skip to content
Research Article Open access CC BY 4.0

Determining the Efficacy of Machine Learning Strategies in Quelling Cyber Security Threats: Evidence from Selected Literatures

Chandra Shikhi Kodete, Bharadwaj Thuraka, Vikram Pasupuleti, Saiteja Malisetty

Asian Journal of Research in Computer Science · pp. 24–33 · Published 13 Jul 2024

10.9734/ajrcos/2024/v17i7487

Abstract

The alarming security threats in the internet world continually raise critical concerns among individuals, organizations and governments alike. The sophistication of cyber-attacks makes it imperative for a paradigm shift from traditional approaches and measures for quelling the attacks to modern sophisticated, digital and strategic ones, such as those involving machine learning and other technologies of artificial intelligence (AI). This study is aimed at examining machine learning (ML) strategies for effective cyber security. ML involves using algorithms and statistical models to enable computers learn from and make decisions or predictions based on data. The study relied on secondary data, which were subjected to a systematic review. The results of its thematic and qualitative analyses prove that majority of the literatures allude to the fact that the maximal performance abilities and tactics of the ML constitute its strategies for quelling cyber security. These include its: early detection of threats that are tackled before they cause damages; ability to analyze huge quantity of data quickly and accurately; and processing of datasets in real-time. The study argues that the noted abilities and tactics constitute ML strategies for quelling cyber security, regardless of its challenges like data quality, security vulnerabilities and possible incidences of bias. The study concludes that ML can indeed be used to detect and respond to threats in real-time, ascertain patterns of malicious behavior, and improve on internet security, which thereby prove it to be a viable tool for quelling cyber security.

Machine learning strategies quell cyber security artificial intelligence

References (41)

  1. 1 Azeez NA, Isekwene CC. Machine learning in cyber security operations. University of Ibadan Journal of Science and Logistics in ICT Research. 2024;11(2):57-70.
  2. 2 Njoku DO, Iwuchukwu VC, Jibiri JE, Ikwuazom CT, Ofoegbu CI, Nwokoma FO. Machine learning approach for fraud detection system in financial institution: A web base application. International Journal of Engineering Research and Development. 2024;20(4):01-121.
  3. 3 Okusi O. Cyber security techniques for detecting and preventing cross-site scripting attacks. World Journal of Innovation and Modern Technology. 2024;8(2):71-89. DOI: 10.56201/wjimt.v8.no2.2024
  4. 4 Bulama L, Shrivastava M. Framework & techniques to improve cyber security in Nigeria. Journal of Data Acquisition and Processing. 2023;38(2).
  5. 5 AL-Hawamleh AM. Predictions of cybersecurity experts on future cyber-attacks and related cybersecurity measures. International Journal of Advanced Computer Science and Applications. 2023;14(2). [DOI]
  6. 6 Chitimira H, Ncube P. The regulation and use of artificial intelligence and 5 Gtechnology to combat cybercrime and financialcrime in South African Banks. PER/ PELJ. 2021;(24). DOI:http://dx.doi.org/10.17159/1727-3781/2021/v24i0a10742 [DOI]
  7. 7 Vidya PM. Cyber security – trends and challenges. International Journal of Computer Science and Mobile Computing. 2014;3(2):586-590.
  8. 8 Snehi M, Bhandari A. Vulnerability retrospection of security solutions for software-defined cyber-physical system against DDoS and IoT-DDoS attacks. Computer Sci Rev; 2021. Available:https://doi.org/10.1016/j.cosrev.2021.100371 [DOI]
  9. 9 Gkioulos V, Chowdhury N. Cyber security training for critical infrastructure protection: A literature review. Computer Sci Rev. 2021;40:100361. Available:https://doi.org/10.1016/J.COSREV.2021.100361 [DOI]
  10. 10 Mohammad G, Reza S. Software vulnerability analysis and discovery using machine-learning and data-mining techniques. ACM Computer Survey (CSUR). 2017;50(4). Available:https://doi. org/10. 1145/3092566 [DOI]
  11. 11 Okusi OA. An analysis of cross-site scripting and its preventive techniques. M.Sc. Cyber Security: CSCT Masters Project. Department of Computer Science and Creative Technologies, University of West England (UWE), Bristol; 2023.
  12. 12 Chen X, Li M, Jiang YU, Sun Y. A comparison of machine learning algorithms for detecting XSS attacks. Artificial Intelligence and Security. 2019;11635:214-224. [DOI]
  13. 13 Marashdih AW, Zaaba ZF, Suwais K, Mohd NA. Web application security: An investigation on static analysis with other algorithms to detect cross site scripting. Procedia Computer Science. 2019;161:1173–1181. [DOI]
  14. 14 Anderson B. 3 dangerous cross-site scripting attacks of the last decade. ReadWrite. 2020;1. Available:https://readwrite.com/3-dangerous-cross-site-scripting-attacks-of-the-last-decade
  15. 15 Rodríguez GE, Torres JG, Flores P, Benavides DE. Cross-site scripting (XSS) attacks and mitigation:A survey. Computer Networks. 2020;166:106960. Available: https://doi.org/10.1016/J. COMNET.2019.106960 [DOI]
  16. 16 Chaudhary P, Gupta BB, Gupta S. Cross-Site Scripting (XSS) worms in online social network (OSN): Taxonomy and defensive mechanisms. 3rd International Conference on Computing for Sustainable Global Development (INDIACom). 2016;2131–2136.
  17. 17 Mohammad SM. Artificial intelligence in information technology. International Journal of Innovations in Engineering Research and Technology. 2020;7(6). [DOI]
  18. 18 Jhaveri RH, Revathi A, Ramana K, Raut R, Dhanaraj RK. A review on machine learning strategies for real-worldengineering applications. Hindawi Mobile Information Systems. 2022;26. Available:https://doi.org/10.1155/2022/183507 [DOI]
  19. 19 Alamleh A, Almatarneh S, Samara G, Rasmi M. Machine learning-based detection of smartphone malware: Challenges and solutions. Mesopotamian Journal of Cybersecurity. 2023;134– 157. DOI: https://doi.org/10.58496/MJCS/2023/017 [DOI]
  20. 20 Medida VNVSN, Mathew RJ. DeepGuard: Detecting and preventing DoSattacks using deep learning. Dogo Rangsang Research Journal (UGC Care Group I Journal). 2023;13(6).
  21. 21 Motiwala A. Cyber security in Ghana: Evaluating readiness for the future. AnnaarInternational Peacekeeping Training Centre Brief. 2017;(1):1-4.
  22. 22 Adewole K. An inquiry into the awareness level of cyber security policy and measures in Nigeria. International Journal of Science and Advanced Technology. 2011;1(7):91-96.
  23. 23 Zhang Z, Ning H, Shi F, Farha F, Xu Y, Xu J, Zhang F, Choo KKR. Artificial intelligence in cyber security: Research advances, challenges, and opportunities. ArtifIntell Rev. 2021;55(2):1029–1053. Available: https://doi.org/10.1007/S10462-021-09976-0 [DOI]
  24. 24 Apruzzese G, Laskov P, de Oca EM, Mallouli W, Rapa LB, Grammatopoulos AV, Franco FD. (2023, March). The role of machine learning in cybersecurity. Digit.Threat.: Res. Pract. 2023;4(1). Available: https://doi.org/10.1145/3545574 [DOI]
  25. 25 Bharadiya JP. Machine learning in cybersecurity: Techniques and challenges. European Journal of Technology. 2023; 7(2):1–14. [DOI]
  26. 26 Thapliyal V, Thapliyal P. machine learning for cybersecurity: Threat detection, prevention, and response. Darpan International Research Analysis. 2024;12(1):1-7. Available: https://dirajournal.com [DOI]
  27. 27 Jian-Hua L. Cyber security meets machine learning. In cyber security meets machine learning. Springer Singapore; 2021. Available: https://doi.org/10.1007/978-981-33-6726-5 [DOI]
  28. 28 Kaur J, Garg U, Bathla G. Detection of cross site scripting (XSS) attacks using machine learning techniques: A review.Artificial Intelligence Review; 2024. Available: https://doi.org/10.1007/s10462-023-10433-3 [DOI]
  29. 29 Wusu GE, Alaka H, Yusuf W, Mporas I, Toriola-Coker L, Oseghale R. A machine learning approach for predicting critical factors determining adoption of offsite construction in Nigeria. Smart and Sustainable Built Environment(ahead-of-print); 2022. [DOI]
  30. 30 Dixit P, Silakari S. Deep learning algorithms for cyber security applications: A technological and status review. Computer Sci. Rev. 2021;39:100317. Available:https://doi.org/10.1016/J. COSREV.2020.100317 [DOI]
  31. 31 Luo C, Tan Z, Min G, Gan J, Shi W, Tian Z. A novel web attack detection system for internet of things via ensemble classification. IEEE Trans. Industry Inf. 2021;17(8):5810–5818. Available:https://doi.org/10.1109/ TII.2020.3038761 [DOI]
  32. 32 Onan A, Tocoglu MA. A term weighted neural language model and stacked bidirectional LSTMbased framework for sarcasm identification. IEEE Access. 2021;9:7701–7722. Available:https://doi.org/10.1109/ACCESS.2021.30497 34 [DOI]
  33. 33 Onan A. Consensus clustering-based undersampling approach to imbalanced learning. Sci Program; 2019. Available: https:// doi. org/ 10. 1155/ 2019/ 59010 87 [DOI]
  34. 34 Pavan KP, Jaya T, Rajendran V. SI-BBA—a novel phishing website detectionbased on swarm intelligence with deep learning. Mater Today; 2021. Available:https://doi.org/10.1016/J.MATPR.2021.07.178 [DOI]
  35. 35 Tariq I, Sindhu MA, Abbasi RA, Khattak AS, Maqbool O, Siddiqui GF. Resolving cross-sitescripting attacks through genetic algorithm and reinforcement learning. ExpSyst App. 2021;168:114386. Available:https://doi.org/10.1016/J.ESWA.2020.114386 [DOI]
  36. 36 Kaur S, Singh M. Hybrid intrusion detection and signature generation using deep recurrent neural networks. Neural Computer App. 2019;32(12):7859–7877. Available: https://doi.org/ 10.1007/S00521-019-04187-9 [DOI]
  37. 37 Zhou Y, Wang P. An ensemble learning approach for XSS attack detection with domain knowledge and threat intelligence. Computer Security. 2019;82:261–269. Available:https://doi.org/10.1016/J.COSE.2018.12.016 [DOI]
  38. 38 Ozkan-Okay M, Akin E, Aslan Ö, Kosunalp S, Iliev T, Stoyanov I, Beloev I. A comprehensive survey: Evaluating the efficiency of artificial intelligence and machine learning techniques on cyber security solutions. IEEE Access. 2024;12: 12229-12256. DOI: 10.1109/ACCESS.2024.3355547 [DOI]
  39. 39 Polito C, Pupillo L. Artificial intelligence and cybersecurity. Intereconomics. 2024;59(1):10-13 (JEL: L38, L51, Z18) DOI: 10.2478/ie-2024-0004. [DOI]
  40. 40 Bulama L, Shirivastata M. The role of information & communication technology towards protection of lives and property in Northern Nigeria: A focus on Maiduguri Bornostate. International Interdisciplinary Research Journal. 2022; 14(1):1-9.
  41. 41 Thakkar A, Lohiya R. A survey on intrusion detection system: Feature selection, model, performance measures, application perspective, challenges, and future research directions. Artificial Intell Rev. 2021;55(1):453–563. Available: https://doi.org/10.1007/S10462-021-10037-9 [DOI]

Cited by 12

Ethical AI Governance, Financing, and Human Well-Being in the 21st Century

Adedotun Christopher Adeniyi, Oluwatobiloba Okusi · African Journal of Humanities and Contemporary Education Research · 2024

Greenhouse Gas Emissions and the Challenges of Environmental Sustainability

Ivie Ibuemi Otasowie, Vikram Pasupuleti, Adeleke A. Adeoye · African Journal of Environmental Sciences and Renewable Energy · 2024

Strategies for mitigating cybersecurity challenges to fund management in the digitalized real estate industry

Martins Awofadeju, Beryl Fonkem, Omolola Akinola · Magna Scientia Advanced Research and Reviews · 2024

Achieving Housing Affordability in the U.S. through Sustained Use of AI and Robotic Process Automation for Prefabricated Modular Construction

Akintayo Philips Akinola, Bharadwaj Thuraka, Similoluwa Blossom Adesuwa Okpeseyi · African Journal of Advances in Science and Technology Research · 2024

Risk Mitigation Approach to Cyber Threat using AI-Driven Models for the Evolving Threat Landscape

Olanrewaju, J., Togunde, M. O., Akande, O. · British Journal of Computer, Networking and Information Technology · 2025

Impact of AI on Architecture: An Exploratory Thematic Analysis

Vikram Pasupuleti, Chandra Shikhi Kodete, Bharadwaj Thuraka · African Journal of Advances in Science and Technology Research · 2024

Leveraging Artificial Intelligence (AI) for the Maintenance of Science Laboratory Equipment

Amusan Odunayo · African Journal of Advances in Science and Technology Research · 2024

Evaluating the Capacity of Machine Learning Models to Quell Cybersecurity Threats

Abiodun Kazeem Oniyide, Kenneth O. Ogirri, Chinonso Francis Nkeoma Ubbaonu · International Journal of Scientific Research in Science and Technology · 2024

Artificial Intelligence for Cybersecurity: A State of the Art

Abdullah Al Siam, Md Maruf Hassan, Touhid Bhuiyan · 2025 IEEE 4th International Conference on AI in Cybersecurity (ICAIC) · 2025

Leveraging Cost-Effective AI and Smart Technologies for Rapid Infrastructural Development in USA

Philips Akinola · African Journal of Advances in Science and Technology Research · 2024

Showing 10 of 12 known citations — external sources report more than can currently be individually listed.

Related research

Article metrics

Real usage data collected on this platform.

0

Page views

0

PDF downloads

0

Outbound clicks

12

Citations

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

No views recorded yet.

Traffic sources

Referring site, by host.

No traffic recorded yet.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.