Predicting Cyber Threats From Twitter Using Codeless LSTM Knime Model

Authors

  • Inass A. Husien Computer Science Department The Libyan Academy, Misurata,Misurata
  • Farij O. Ehtiba Computer Science Department The Libyan Academy, Misurata,
  • Haitham Saleh Ben Abdelmula Computer Networks Department,College of Computer Technology, Zawia
  • Hend Abdelgader Eissa Computer Technologies Department,College of Computer Technology Tripoli

DOI:

https://doi.org/10.51984/sucp.v3i3.3748

Keywords:

Cyber Threats, CodelessApproach, Knime, LSTM, Twitter

Abstract

Cybersecurity threats pose significant risks in the increasingly interconnected digital world. Traditional security measures struggle to keep pace with modern cyberattacks, necessitating innovative approaches for proactive threat detection. This paper explores a codeless approach using Long Short-Term Memory (LSTM) model within the Knime analytics platform to predict emerging cyber threats from Twitter data to avoid the complexity and hassle of writing and debugging code. Based on the implementation results, the proposed model achieved accuracy on the prediction around 74%.

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Published

2024-12-28

Issue

Vol. 3 No. 3 (2024): Sebha University Conference Proceedings, Vol 3, No. 3. 2024: Proceedings of the Sixth Conference for Engineering Sciences and Technology (CEST-6)

Section

Confrence Proceeding

License

Copyright (c) 2024 Sebha University Conference Proceedings

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

Predicting Cyber Threats From Twitter Using Codeless LSTM Knime Model. (2024). Sebha University Conference Proceedings, 3(3), 108-113. https://doi.org/10.51984/sucp.v3i3.3748