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Showing 2 results for Key Recovery
Iman Mirzaali Mazandarani, Dr Nasour Bagheri, Dr Sadegh Sadeghi, Volume 12, Issue 1 (9-2023)
Abstract
With the increasing and widespread application of deep learning and neural networks across various scientific domains and the notable successes achieved, deep neural networks were employed for differential cryptanalysis in 2019. This marked the initiation of growing interest in this research domain. While most existing works primarily focus on enhancing and deploying neural distinguishers, limited studies have delved into the intrinsic principles and learned characteristics of these neural distinguishers. In this study, our focus will be on analyzing block ciphers such as Speck, Simon, and Simeck using deep learning. We will explore and compare the factors and components that contribute to better performance. Additionally, by detailing attacks and comparing results, we aim to address the question of whether neural networks and deep learning can effectively serve as tools for block cipher cryptanalysis or not.
Javad Alizadeh, Seyyed Hadi Noorani Asl, Volume 12, Issue 2 (2-2024)
Abstract
The Internet of Drones (IoD) refers to the use of unmanned aerial vehicles (UAVs) connected to the Internet. This concept is a specific application of IoT. The IoD may offer opportunities, but it also poses security vulnerabilities. It is necessary to use authentication and key agreement protocols in drone communications to prevent these vulnerabilities. In 2020, Alladi et al presented an authentication and key agreement protocol based on physical unclonable functions called SecAutUAV. They analyzed the security of their scheme through both formal and informal methods. In this paper, we demonstrate the vulnerability of the SecAuthUAV protocol to a key recovery attack. An adversary can obtain a session key between a drone and a ground station by intercepting and analyzing the session data. In addition, we present a secret value recovery attack with complexity  , which is lower than the complexity of brute force attacks. An adversary could spoof and track the drone by using these values. In order to improve the security and efficiency of SecAuthUAV, we present a new version and compare it to the original. We utilize both the informal method and formal-based ProVerif to analyze the
security of the latest protocol. To compare the efficiency of the new protocol and SecAuthUAV, we counted their number of operators and functions. The new protocol is more secure and efficient than SecAutUAV.
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