A Survey of Attack Prediction Approaches in Cyber Security
- DOI
- 10.2991/978-94-6463-716-8_75How to use a DOI?
- Keywords
- Cyber-attack prediction; DDOS (Distributed Denial of Services); Discrete Models; Bayesian Networks; Markov Model
- Abstract
In the age of digitization, Cyber-attacks significantly affect the world. Lots of resources and the economy are compromised due to cyberattacks. Predictions of cyber attacks enable us to handle the attack at the appropriate time, which can save money and resources. This paper surveys the different methodologies used to predict cyberattacks. These methodologies are broadly classified into discrete and continuous models. Discrete model examples are the attack graph, Bayesian network, and Markov model, while time series are examples of a continuous model. Other methodologies used to classify and predict the attacks are machine learning, data mining, and deep learning.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Varsha Zokarkar AU - Kirti Mathur PY - 2025 DA - 2025/05/26 TI - A Survey of Attack Prediction Approaches in Cyber Security BT - Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025) PB - Atlantis Press SP - 1003 EP - 1016 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-716-8_75 DO - 10.2991/978-94-6463-716-8_75 ID - Zokarkar2025 ER -