Cover
Vol. 22 No. 1 (2026)

Published: June 15, 2026

Pages: 254-267

Original Article

Adaptive Multi Objective Chicken Swarm Optimization for Solving Nonlinear Stream Cryptosystem

Mohammed H. Ahmed 1 Asaad N. Hashim 2 Khalid A. Hussein 1
1 Computer Science Department, College of Education, Mustansiriyah University, Baghdad, Iraq
2 Computer Science Department, College of Computer Science and Math, University of Kufa, Najaf, Iraq
History
  • Received: January 31, 2024
  • Revised: March 1, 2024
  • Accepted: June 14, 2024
  • Available Online: June 15, 2026
DOI

https://doi.org/10.37917/ijeee.22.1.24

Abstract

Nonlinear stream ciphers have become a viable alternative to traditional cryptosystems in response to the growing need for secure communication. These ciphers generate a keystream via feedback mechanisms and nonlinear functions, which are then utilized for encryption. Geffe generator system is one of the most keystream generators. Also, these systems have many benefits, like being fast, flexible, and able to create unpredictable and non-repeating keystreams, these systems are susceptible to cryptanalysis attacks, which have the potential to compromise their security. This paper presents the first study of applying chicken swarm optimization (CSO) algorithm in the field of cryptanalysis based on cipher only attack. The standard CSO algorithm and an adaptive multi points CSO (AMPCSO) algorithm are proposed to cryptanalysis nonlinear stream cipher based on Geffe keystream generator. Firstly, the traditional CSO is used to reveal the secret initial values of the Geffe generator. Secondly, an adaptive multi points chicken swarm optimization (AMPCSO) has been proposed to enhance the traditional CSO algorithm to attack Geffe generator systems. The AMPCSO is a new idea to advance the CSO search abilities and improve the foraging behavior of hens and chicks by allowing hens to be influenced by other individuals within the same or different groups and affected by the best individual in the population and enable chicks to learn from four reference points rather than learn from their respective mothers only. Lastly, a new criterion is used to estimate the value of fitness by utilizing a multi-objective fitness function (MOFF), which is grounded on Pareto dominance. The experimental results showed that the CSO and AMPCSO are very effective tools in terms of accuracy, information required, and CPU times when applied to the analysis of nonlinear stream cipher. The AMPCSO required a few characters from ciphertext to attack systems with total LFSRs length up to 59 bits with an appropriate CPU time.

Keywords

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