Contrasting Computational Frameworks for Intrusion Detection: A Methodological Synthesis

Authors

  • Nancy Thomas
  • R. Gunasunadari

Keywords:

N\A

Abstract

This research analyses and compares various machine learning techniques for the purposes of intrusion detection. With the increasing sophistication of cyberthreats, selecting the optimal intrusion detection system (IDS) is crucial to network security. This article examines various modelling techniques including Decision Trees, Random Forest, Support Vector Machines (SVM), and Deep Learning models, using a benchmark dataset including Random Forest, Decision Trees, Support Vector Machines (SVM), and Deep Learning approaches. In the analyzed study, a set of performance measures is calculated using accuracy, precision, recall, and F1 score. Their results are graphically demonstrated and successfully communicated.

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References

Tavallaee, M., et al. "A detailed analysis of the KDD Cup 99 dataset." International Conference on Computational Intelligence for Security and Defense Applications, 2009.

García-Teodoro, P., et al. "Anomaly-based network intrusion detection: Techniques, systems and challenges." Computers & Security, 2009.

Hodo, E., et al. "Threat analysis of IoT networks using deep learning models." IEEE Transactions on Cybernetics, 2017.

Li, C., et al. "Machine learning techniques for cybersecurity intrusion detection: A review." Journal of Information Security and Applications, 2020.

Buczak, A. L., & Guven, E. "A survey of data mining and machine learning methods for cyber security intrusion detection." IEEE Communications Surveys & Tutorials, 2016.

Sommer, R., & Paxson, V. "Outside the closed world: On using machine learning for network intrusion detection." IEEE Symposium on Security and Privacy, 2010.

Vinayakumar, R., et al. "Deep learning approaches for intrusion detection systems: A survey." Computer Communications, 2019.

Ahmed, M., et al. "A survey of network anomaly detection techniques." Journal of Network and Computer Applications, 2016.

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Published

2025-05-15

How to Cite

1.
Thomas N, R. Gunasunadari RG. Contrasting Computational Frameworks for Intrusion Detection: A Methodological Synthesis. J Neonatal Surg [Internet]. 2025May15 [cited 2025Sep.21];14(24S):269-72. Available from: https://jneonatalsurg.com/index.php/jns/article/view/5923

Issue

Vol. 14 No. 24S (2025): Journal of Neonatal Surgery

Section

Original Article

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