Acta Scientific Computer Sciences

Review Article Volume 3 Issue 10

Application of Malware Agent Based Datasets to Deep Learning Networks: A Review

Charles O Ugwunna1*, Moses O Onyesolu1 and ChukwuNonso H Nwokoye2

1Nnamdi Azikiwe University, Awka, Nigeria
Nigerian Correctional Service, Awka, Nigeria

*Corresponding Author: ChukwuNonso H Nwokoye, Nigerian Correctional Service, Awka, Nigeria, Awka, Nigeria.

Received: July 21, 2021; Published: September 14, 2021


  Implanting malware into servers or endpoints in a network has become a huge possibility in institutions that use the World Wide Web to accomplish tasks. Thanks to the penetration of connectivity due to the Internet and mobile devices into personal life, work, and entertainment, as well as the increasing number of smartphones and diverse IT applications. So is the application of both machine learning (ML) and deep learning (DL) models to a variety of programs used in big data analytics and data mining. On the other hand, the agent-based model (ABM) is a computer model for simulating the activities and interactions of autonomous agents. However, we enumerated some challenges of traditional models that gave rise to the use of ABM. With the observation that both ABM and ML/DL have yet to be applied to epidemics in communication networks, we reviewed the literature surrounding the aforementioned subjects. This is to provide the appropriate background for malware spread forecasts and the development of transitional methods from ABM to DL networks during representations of epidemic theory.

Keywords: Machine Learning; Deep Learning; Malware


  1. Aldweesh A., et al. “Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues”. Knowledge-Based Systems 189 (2020).
  2. Nwokoye C H., et al. “Evaluating degrees of differential infections on sensor networks’ features using the SEjIjR-V epidemic model”. Egyptian Computer Science Journal3 (2020).
  3. Crowdstrike Global Threat Report (2020).
  4. Rhode M., et al. “Early-stage malware prediction using recurrent neural networks”. Computer and Security 77 (2018): 578-594.
  5. Xiaofeng L., et al. “ASSCA: API-based Sequence and Statistics features combined malware detection Architecture”. Procedia Computer Science 129 (2018): 248-256.
  6. Batista F K., et al. “A new individual-based model to simulate malware propagation in wireless sensor networks”. Mathematics 8 (2020): 1-23.
  7. Hong Guo., et al. “Impact of Network Structure on Malware Propagation: A Growth Curve Perspective”. Journal of Management Information Systems1 (2016): 296-325.
  8. Davis J J and Clark A J. “Data preprocessing for anomaly based network intrusion detection: A review”. Computers and Security6 (2011): 353-375.
  9. Wehle HD. “Machine Learning, Deep Learning and AI: What’s the Difference?” (2017).
  10. Nwokoye C and Umeh I. “Analytic-agent cyber dynamical systems analysis and design method for modeling spatio-temporal factors of malware propagation in wireless sensor networks”. MethodsX 5 (2018): 1373-1398.
  11. Peng S., et al. “Modeling the dynamics of worm propagation using two-dimensional cellular automata in smartphones”. Journal of Computer and System Sciences 79 (2013): 586-595.
  12. Pellis L., et al. “Eight challenges for network epidemic models”. Epidemics 10 (2015): 58-62.
  13. Cunniffea NJ., et al. “Thirteen challenges in modelling plant diseases”. Epidemics 10 (2015): 6-10.
  14. Roberts M Andreasen., et al. “Nine challenges for deterministic epidemic models”. Epidemics 10 (2015): 49-53.
  15. Kotenko I. “Agent-based modeling and simulation of cyber-warfare between malefactors and security agents in internet”. Paper presented at the 19th European Conference on Modelling and Simulation, Riga, Latvia, Germany (2015).
  16. Kotenko I. “Agent-based modeling and simulation of network infrastructure cyber-attacks and cooperative defense mechanisms”. Discrete Event Simulations56 (2019): 34-67.
  17. Pan J and Fung CC. “An agent-based model to simulate coordinated response to malware outbreak within an organization”. International Journal of Information and Computer Security2 (2012): 115-131.
  18. Niazi M and Hussain A. “Agent-based tools for modeling and simulation of self-organization in peer-to-peer, ad hoc, and other complex networks”. IEEE Communications Magazine3 (2009): 166-173.
  19. Wasti BK. “Usability of multi-agent simulators in simulation of wireless networks”. (Master’s thesis. The University of Oulu, US) (2014).
  20. Mojahedi E and Azgomi MA. “Modeling the propagation of topology-aware P2P worms considering temporal parameters”. Peer-to-Peer Networking and Applications1 (2015): 171-180.
  21. Shone N., et al. “A Deep Learning Approach to Network Intrusion Detection”. IEEE Transactions on Emerging Topics in Computational Intelligence (2017): 1-10.
  22. Chawla S. “Deep Learning based Intrusion Detection System for Internet of Things”. Master Thesis, University of Washington (2017).
  23. Hijazi A., et al. “A Deep Learning Approach for Intrusion Detection System in Industry Network” (2018).
  24. Kang J., et al. “Long short-term memory-based Malware classification method for information security”. Computers and Electrical Engineering 77 (2019): 366-375.
  25. Fang X., et al. “A deep learning framework for predicting cyberattacks rates”. EURASIP Journal on Information Security1 (2019): 1-11.
  26. Thamilarasu G and Chawla S. “Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things”. Sensors 1 (2019): 1-19.
  27. Thence Ren., et al. (2019).
  28. Boukhalfa A., et al. “LSTM deep learning method for network intrusion detection system”. International Journal of Electrical and Computer Engineering3 (2020): 3315-3322.
  29. Almseidin M., et al. “Evaluation of Machine Learning Algorithms for Intrusion Detection System” (2020).
  30. Wuke L., et al. “Application of Deep Extreme Learning Machine in Network Intrusion Detection Systems”. IAENG International Journal of Computer Science2 (2020): 136-143.
  31. Kim K. “Intrusion Detection System Using Deep Learning and Its Application to Wi-Fi Network”. IEICE Transactions on Information and Systems7 (2020): 1433-1447.
  32. Bediako P K. “Long Short-Term Memory Recurrent Neural Network for detecting DDoS flooding attacks within TensorFlow Implementation framework”. Master’s Thesis submitted to Lulea University of Technology (2017).
  33. Batista F K., et al. “A new individual-based model to simulate malware propagation in wireless sensor networks”. Mathematics 8 (2020): 1-23.
  34. Bose A and Shin K. “Agent-based modeling of malware dynamics in heterogeneous environments”. Security and Communication Networks 6 (2013): 1576-1589.
  35. Hosseini S., et al. “Agent-based simulation of the dynamics of malware propagation in scale-free networks”. Simulation7 (2016): 709-722.
  36. Mwangi K E., et al. “Modelling malware propagation on the internet of things using an agent based approach on complex networks”. Jordanian Journal of Computers and Information Technology (JJCIT)1 (2020).


Citation: Charles O Ugwunna., et al. “Application of Malware Agent Based Datasets to Deep Learning Networks: A Review". Acta Scientific Computer Sciences 3.10 (2021): 37-44.


Copyright: © 2021 ChukwuNonso H Nwokoye., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Acceptance rate35%
Acceptance to publication20-30 days

Indexed In

News and Events

  • Certification for Review
    Acta Scientific certifies the Editors/reviewers for their review done towards the assigned articles of the respective journals.
  • Submission Timeline for Upcoming Issue
    The last date for submission of articles for regular Issues is May 25, 2022.
  • Publication Certificate
    Authors will be issued a "Publication Certificate" as a mark of appreciation for publishing their work.
  • Best Article of the Issue
    The Editors will elect one Best Article after each issue release. The authors of this article will be provided with a certificate of “Best Article of the Issue”.
  • Welcoming Article Submission
    Acta Scientific delightfully welcomes active researchers for submission of articles towards the upcoming issue of respective journals.
  • Contact US