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.

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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  -