Automatic Method To Predict And Classify Cyber Hacking Breaches Using Deep Learning

DOI

10.2991/978-94-6463-471-6_114How to use a DOI?

Keywords

Convolution Neural Network (CNN); Recurrent Neural Network (RNN); Breaches; User to Root(U2R); Remote to Local(R2L)

Abstract

Analyzing cyber incident datasets is crucial for enhancing our comprehension of the evolving cyber threat landscape. In present generation many cyber hacking breaches taking place. This Application, Examine diverse cyber-attacks and breaches, analyze the methods employed in these incidents, and explore alternative strategies for prevention. This Application show that rather than by distributing these attacks as because they exhibit autocorrelations, traditional methods for breach prediction and classification, such as rule-based systems and signature-based approaches, have notable limitations. In this application, both prediction and classification of cyber-attacks is done to avoid indeed getting worse in terms of their frequency. This Application will make use of algorithms such as Convolution Neural Network (CNN) and Recurrent Neural Network (RNN) for analyzing our results. In this Application we will be analyzing two types of attacks U2R and R2L attacks.

Copyright

© 2024 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  - K. Ramesh
AU  - T. V. Prasad
AU  - V. Krishna Kanth
AU  - V. Sarveswar
AU  - S. D. Abrar Ali
AU  - M. Vikas Reddy
PY  - 2024
DA  - 2024/07/30
TI  - Automatic Method To Predict And Classify Cyber Hacking Breaches Using Deep Learning
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 1189
EP  - 1200
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_114
DO  - 10.2991/978-94-6463-471-6_114
ID  - Ramesh2024
ER  -