Bibliographic Details
| Title: |
Memoryless state-recovery cryptanalysis method for lightweight stream cipher - A5/1. |
| Authors: |
Audumbar, Khedkar Aboli1,2 abolikhedkar@gmail.com, Khot, Uday Pandit3 udaypanditkhot@sfit.ac.in, Hogade, Balaji G.1 balajihogade@ternaengg.ac.in |
| Source: |
International Journal of Electrical & Computer Engineering (2088-8708). Dec2025, Vol. 15 Issue 6, p5453-5465. 13p. |
| Subjects: |
Stream ciphers, Cryptography, Time complexity, GSM communications |
| Abstract: |
Cryptology refers to the discipline concerned with securing communication and data in transit by transforming it into an unintelligible form, thereby preventing interpretation by unauthorized entities. Cryptanalysis is the study and practice of analyzing cryptographic systems with the aim of uncovering their weaknesses, finding vulnerabilities and obtaining unauthorized access to encrypted data. A5/1 is a lightweight stream cipher used to protect GSM communications. There are two memoryless cryptanalysis techniques used for this cipher which are Golic's Guess-and-determine attack and Zhang's Near Collision attack. In this paper a new guessing technique called move guessing technique used to construct linear equation filter along with Golic's guess and determine technique is studied. Two modifications in move guessing technique are proposed for recovery of internal states S0 and S1. Further, a novel algorithm is proposed to select the modification to get minimum time complexity for recovery of internal states S0 and S1. The proposed algorithm gives minimum time complexity of 229.3138 at t = 14 for recovery of S0 state and 243.246 for recovery of S1 at t = 22. [ABSTRACT FROM AUTHOR] |
|
Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Engineering Source |