Neural cryptography: vulnerabilities and attack strategies

Lubjana Beshaj; Gaurav Tyagi

doi:10.1117/12.3013669

6 June 2024 Neural cryptography: vulnerabilities and attack strategies

Lubjana Beshaj, Gaurav Tyagi

Proceedings Volume 13058, Disruptive Technologies in Information Sciences VIII; 130580S (2024) https://doi.org/10.1117/12.3013669
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

A number of research papers has been published using the architecture of adversarial neural networks to prove that communication between two neural net based on synchronized input can be achieved, and without knowledge of this synchronized information these systems can not be breached. In this paper we will try to evaluate these adversarial neural net architectures when a third party gain access to partial secret key, or a noisy secret key, or has knowledge about loss function, or loss values itself, or activation functions used during training of encryption layers. We explore the cryptanalysis side of it in which we will focus on vulnerabilities a neural-net based cryptography network can face. This can be used in future to improve the current neural net based cryptography architectures. In this paper we show that while the encryption key is necessary to decrypt the messages in neural network domain, the adversarial neural networks can occasionally decrypt messages or raise a concern which will require further training.

Conference Presentation

Citation Download Citation

Lubjana Beshaj and Gaurav Tyagi "Neural cryptography: vulnerabilities and attack strategies", Proc. SPIE 13058, Disruptive Technologies in Information Sciences VIII, 130580S (6 June 2024); https://doi.org/10.1117/12.3013669

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES + PRESENTATION

DOWNLOAD PAPER SAVE TO MY LIBRARY

WATCH
PRESENTATION

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Neural networks

Artificial neural networks

Cryptography

Cryptanalysis

Symmetric key encryption

Network security

RELATED CONTENT

Research on short-term power load forecasting based on LSTM
Proceedings of SPIE (May 23 2022)

Speaker identification for the improvement of the security communication between...
Proceedings of SPIE (May 02 2017)

Link between adaptive feed-forward layered networks and discriminant analysis
Proceedings of SPIE (August 19 1993)

Online location of a break in water distribution systems
Proceedings of SPIE (August 18 2003)

Position-invariant target detection by a neural net
Proceedings of SPIE (August 01 1990)

Characteristic recognition system with artificial neural network
Proceedings of SPIE (October 03 1996)

Dark formation detection using recurrent neural networks and SAR data
Proceedings of SPIE (September 29 2006)

Subscribe to Digital Library

Receive Erratum Email Alert

Keywords/Phrases

Search In:

Publication Years