Honeyword Generation Using a Proposed Discrete Salp Swarm Algorithm
01 Apr 2023-Baghdad Science Journal-Vol. 20, Iss: 2, pp 0357-0357
TL;DR: This study will demonstrate numerous previous honeyword generating strategies, describe the proposed methodology, examine the experimental results, and compare the new honeyword production method to those proposed in previous research.
Abstract: : Honeywords are fake passwords that serve as an accompaniment to the real password, which is called a “sugarword.” The honeyword system is an effective password cracking detection system designed to easily detect password cracking in order to improve the security of hashed passwords. For every user, the password file of the honeyword system will have one real hashed password accompanied by numerous fake hashed passwords. If an intruder steals the password file from the system and successfully cracks the passwords while attempting to log in to users’ accounts, the honeyword system will detect this attempt through the honeychecker. A honeychecker is an auxiliary server that distinguishes the real password from the fake passwords and triggers an alarm if intruder signs in using a honeyword. Many honeyword generation approaches have been proposed by previous research, all with limitations to their honeyword generation processes, limited success in providing all required honeyword features, and susceptibility to many honeyword issues. This work will present a novel honeyword generation method that uses a proposed discrete salp swarm algorithm. The salp swarm algorithm (SSA) is a bio-inspired metaheuristic optimization algorithm that imitates the swarming behavior of salps in their natural environment. SSA has been used to solve a variety of optimization problems. The presented honeyword generation method will improve the generation process, improve honeyword features, and overcome the issues of previous techniques. This study will demonstrate numerous previous honeyword generating strategies, describe the proposed methodology, examine the experimental results, and compare the new honeyword production method to those proposed in previous research.
TL;DR: The qualitative and quantitative results prove the efficiency of SSA and MSSA and demonstrate the merits of the algorithms proposed in solving real-world problems with difficult and unknown search spaces.
TL;DR: An exhaustive and a comprehensive review of the so-called salp swarm algorithm (SSA) and discussions its main characteristics, including its variants, like binary, modifications and multi-objective.
Abstract: This paper completely introduces an exhaustive and a comprehensive review of the so-called salp swarm algorithm (SSA) and discussions its main characteristics. SSA is one of the efficient recent meta-heuristic optimization algorithms, where it has been successfully utilized in a wide range of optimization problems in different fields, such as machine learning, engineering design, wireless networking, image processing, and power energy. This review shows the available literature on SSA, including its variants, like binary, modifications and multi-objective. Followed by its applications, assessment and evaluation, and finally the conclusions, which focus on the current works on SSA, suggest possible future research directions.
TL;DR: Improved salp swarm algorithm (ISSA), a recently created bio-inspired optimization algorithm presented in 2017, is improved to enhance solution accuracy, reliability and convergence speed and a new control parameter, inertia weight, is added.
••01 Jan 2020
TL;DR: The application of SSA in optimizing the Extreme Learning Machine (ELM) is investigated to improve its accuracy and overcome the shortcomings of its conventional training method.
Abstract: Salp Swarm Algorithm (SSA) is a recent metaheuristic inspired by the swarming behavior of salps in oceans. SSA has demonstrated its efficiency in various applications since its proposal. In this chapter, the algorithm, its operators, and some of the remarkable works that utilized this algorithm are presented. Moreover, the application of SSA in optimizing the Extreme Learning Machine (ELM) is investigated to improve its accuracy and overcome the shortcomings of its conventional training method. For verification, the algorithm is tested on 10 benchmark datasets and compared to two other well-known training methods. Comparison results show that SSA based training methods outperforms other methods in terms of accuracy and is very competitive in terms of prediction stability.
TL;DR: An alternative approach is suggested that selects the honeywords from existing user passwords in the system in order to provide realistic honeywords-a perfectly flat honeyword generation method-and also to reduce storage cost of the honeyword scheme.
Abstract: Recently, Juels and Rivest proposed honeywords (decoy passwords) to detect attacks against hashed password databases. For each user account, the legitimate password is stored with several honeywords in order to sense impersonation. If honeywords are selected properly, a cyber-attacker who steals a file of hashed passwords cannot be sure if it is the real password or a honeyword for any account. Moreover, entering with a honeyword to login will trigger an alarm notifying the administrator about a password file breach. At the expense of increasing the storage requirement by 20 times, the authors introduce a simple and effective solution to the detection of password file disclosure events. In this study, we scrutinize the honeyword system and present some remarks to highlight possible weak points. Also, we suggest an alternative approach that selects the honeywords from existing user passwords in the system in order to provide realistic honeywords—a perfectly flat honeyword generation method—and also to reduce storage cost of the honeyword scheme.
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