Home⇛QCU The Star: Journal of Science, Engineering, and Information Technology⇛vol. 1 no. 1 (2023)

Investigating the Impact of Training and Testing Ratios on the Performance of an Al-Based Malware Detector using MATLAB

Carlo Angelino M. Romero

Discipline: Computer Engineering

 

Abstract:

This research investigates the impact of the training and testing ratios on the performance of an Al-Based Malware Detector using MATLAB. The experiments through MATLAB have shown that higher training percentage means that a larger portion of dataset for training the model have been used while a lower training percentage shows that a large portion of the dataset reserved for testing the model's performance. The exploration of the influence of training and testing ratios also have been able to determine the performance of an Al-Based Malware Detector. The results give to determining the relationship between training and testing ratios and the effectiveness of the malware detection system.



References:

  1. AlTurjman, F., Salama, R.: An overview about the cyberattacks in grid and like systems. In: AlTurjman, F. (ed.) Smart Grid in loT-Enabled Spaces: The Road to Intelligence in Power, pp. 233-247. CRC Press, Boca Raton (2020). https://doi.org/10.1201/9781003055235-11
  2. Al-Ani Mustafa Majid, Ahmed Jamal Alshaibi, Evgeny Kostyuchenko, Alexander Shelupanov, A review of artificial intelligence based malware detection using deep learning,mMaterials Today: Proceedings, Volume 80, Part 3, 2023, Pages 2678- 2683, ISSN 2214-7853, https://doi.org/10.1016/j.matpr.2021.07.012. (https://
  3. www.sciencedirect.com/science/article/pii/S221478532104858)
  4. Ö. A. Aslan and R. Samet, "A Comprehensive Review on Malware Detection Approaches," in IEEE Access, vol. 8, pp. 6249-6271, 2020, doi: 10.1109/ ACCESS.2019.2963724.
  5. Spafford, Eugene. "The internet worm incident." ESEC'89 (1989): 446-468. [5] Li, Jun, and Shad Stafford. "Detecting smart, self-propagating Internet worms." Communications and Network Security (CNS), 2014 IEEE Conference on. IEEE, 2014. Idika, Nwokedi, and Aditya P. Mathur. "A survey of malware detection techniques." Purdue University 48 (2007).
  6. Yin, Heng, et al. "Panorama: capturing system-wide information flow for malware detection and analysis." Proceedings of the 14th ACM conference on Computer and communications security. ACM, 2007.
  7. Rabia Tahir, "A Study on Malware and Malware Detection Techniques," I.J. Education and Management Engineering, 2018, 2, 20-30 Published Online March 2018 in MECS (http://www.mecs-press.net) DOI: 10.5815/ijeme.2018.02.03
  8. Sun, Baohua, et al. "Multi-modal Sentiment Analysis using Super Characters Method on Low-power CNN Accelerator Device." arXiv preprint arXiv:2001.10179 (2020).
  9. [Y. Liu, X. Li and Z. Xiang, "The Effect of Chatbot-customer Interaction on Consumer Brand Advocacy: Exploring the Role of Chatbots," 2022 IEEE 12th International Conference on Electronics Information and Emergency Communication (ICEIEC), Beijing, China, 2022, pp. 185-190, doi: 10.1109/ICEIEC54567.2022.9835050.

Tools


Copyright © 2025  KITE Digital Educational Solutions |  Exclusively distributed by CE-Logic