English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In this paper, we propose and evaluate a method for generating key-dependent feedback configurations (KDFC) for <inline-formula> <tex-math notation="LaTeX">$\sigma $ </tex-math></inline-formula>-LFSRs. <inline-formula> <tex-math notation="LaTeX">$\sigma $ </tex-math></inline-formula>-LFSRs with such configurations can be applied to any stream cipher that uses a word-based LFSR. Here, a configuration generation algorithm uses the secret key(K) and the Initialization Vector (IV) to generate a new feedback configuration after the initialization round. It replaces the older known feedback configuration. The keystream is generated from this new feedback configuration and the FSM part. We have mathematically analysed the feedback configurations generated by this method. As a test case, we have applied this method on SNOW 2.0 and have studied its impact on resistance to algebraic attacks. Besides, as a consequence of resisting algebraic attacks, SNOW 2.0 can also withstand some other attacks like Distinguishing Attack, Fast Correlation Attack, Guess and Determining Attack and Cache Timing Attack. Further, we have also tested the generated keystream for randomness and have briefly described its implementation and the challenges involved in the same.

Key-Dependent Feedback Configuration Matrix of Primitive σ–LFSR and Resistance to Some Known Plaintext Attacks