Honeyword Generation Using a Proposed Discrete Salp Swarm Algorithm





Authentication, Honeyword, Password, Salp algorithm, Swarm algorithm


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.


Download data is not yet available.


Mohammed AA, Abdul-Hassan AK, Mahdi BS. Authentication System Based on Hand Writing Recognition. In: 2019 SCCS 2019 - 2019 2nd Sci. Conf. Comput. Sci; 2019: 138-142. doi:10.1109/SCCS.2019.8852594

Abdulameer SA, Kashmar AH, Shihab AI. A cryptosystem for database security based on TSFS algorithm. Baghdad Sci J. 2020; 17(2): 567-574. doi:10.21123/bsj.2020.17.2.0567

Alaa Kadhim F, Mhaibes HI. A New Initial Authentication Scheme for Kerberos 5 Based on Biometric Data and Virtual Password. ICOASE 2018 - Int Conf Adv Sci Eng. Published online 2018: 280-285. doi:10.1109/ICOASE.2018.8548852

Genç ZA, Kardaş S, Kiraz MS. Examination of a New Defense Mechanism: Honeywords. In: Hancke GP, Damiani E, eds. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 10741. Lecture Notes in Computer Science. Springer International Publishing; 2018: 130-139. doi:10.1007/978-3-319-93524-9_8

Win T, Moe KSM. Protecting private data using improved honey encryption and honeywords generation algorithm. Adv Sci Technol Eng Syst. 2018; 3(5): 311-320. doi:10.25046/aj030537

Chakraborty N, Mondal S. Towards Improving Storage Cost and Security Features of Honeyword Based Approaches. Procedia Comput Sci. 2016; 93(September):799-807. doi:10.1016/j.procs.2016.07.298

Palaniappan S, Parthipan V, Stewart kirubakaran S, Johnson R. Secure User Authentication Using Honeywords. In: Lecture Notes on Data Engineering and Communications Technologies. 31. 2020: 896-903. doi:10.1007/978-3-030-24643-3_105

Wang R, Chen H, Sun J. Phoney: Protecting password hashes with threshold cryptology and honeywords. Int J Embed Syst. 2016; 8(2-3): 146-154. doi:10.1504/IJES.2016.076108

Homayouni SM, Fontes DBMM. Metaheuristic Algorithms. In: Metaheuristics for Maritime Operations. Volume 1. John Wiley & Sons, Inc. 2018; ch2: 21-38. doi:10.1002/9781119483151.ch2

Tezel BT, Mert A. A cooperative system for metaheuristic algorithms. Expert Syst Appl. 2021; 165(May 2020): 113976. doi:10.1016/j.eswa.2020.113976

Malik H, Iqbal A, Joshi P, Agrawal S, Farhad IB. Metaheuristic and Evolutionary Computation: Algorithms and Applications. Part of the Studies in Computational Intelligence book series. Springer Singapore. 916. 2021. doi:10.1007/978-981-15-7571-6

Yasear SA, Ku-Mahamud KR. Taxonomy of memory usage in swarm intelligence-based metaheuristics. Baghdad Sci J. 2019; 16(2): 445-452. doi:10.21123/bsj.2019.16.2(SI)0445

Faeq IF, Duaimi MG, Sadiq Al-Obaidi AT. An efficient artificial fish swarm algorithm with harmony search for scheduling in flexible job-shop problem. J Theor Appl Inf Technol. 2018; 96(8): 2287-2297. http://www.jatit.org/volumes/Vol96No8/18Vol96No8.pdf

Castelli M, Manzoni L, Mariot L, Nobile MS, Tangherloni A. Salp Swarm Optimization: A critical review. Expert Syst Appl. 2021 (November): 116029. doi:10.1016/j.eswa.2021.116029

Faris H, Mirjalili S, Aljarah I, Mafarja M, Heidari AA. Salp swarm algorithm: Theory, literature review, and application in extreme learning machines. Stud Comput Intell. 2020; 811(January): 185-199. doi:10.1007/978-3-030-12127-3_11

Juels A, Rivest RL. Honeywords: Making Password-Cracking Detectable. In: Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security - CCS ’13. ACM Press, 2013: 145-160. doi:10.1145/2508859.2516671

Erguler I. Achieving Flatness: Selecting the Honeywords from Existing User Passwords. IEEE Trans Dependable Secur Comput. 2015; 13(2): 284-295. doi:10.1109/TDSC.2015.2406707

Chakraborty N, Mondal S. On designing a modified-UI based honeyword generation approach for overcoming the existing limitations. Comput Secur. 2017; 66: 155-168. doi:10.1016/j.cose.2017.01.011

Akshima A, Chang D, Goel A, Mishra S, Sanadhya SK. Generation of Secure and Reliable Honeywords, Preventing False Detection. IEEE Trans Dependable Secur Comput. 2018; 5971(c): 1-13. doi:10.1109/TDSC.2018.2824323

Akif OZ, Sabeeh AF, Rodgers GJ, Al-Raweshidy HS. Achieving flatness: Honeywords generation method for passwords based on user behaviours. Int J Adv Comput Sci Appl. 2019; 10(3): 28-37. doi:10.14569/IJACSA.2019.0100305

Lanjulkar Pritee, Ingle Rupali, Lonkar Arti IV. Honeywords : A New Approach For Enhancing Security. Int Res J Eng Technol. 2019; 06(03): 1360-1363. https://www.irjet.net/archives/V6/i3/IRJET-V6I3256.pdf

Veera Babu R, Praneerhasrurhi M. Security Enhancement by Achieving Flatness in Selecting the Honey words from Existing User Passwords. Int J Eng Tech. 2018; 4(2): 743-746. http://www.ijetjournal.org/Volume4/Issue2/IJET-V4I2P115.pdf

Weiwei Jing, Jinku Cui YZ. A Honeyword Generation Method Based on Special Character Distance. Softw Eng Appl. 2019; 08(05): 207-214. doi:10.12677/SEA.2019.85025

Ghare H. Securing System using Honeyword and MAC Address. Int J Res Appl Sci Eng Technol. 2019; 7(5): 2685-2689. doi:10.22214/ijraset.2019.5446

Thakur PV. Honeywords: The New Approach for Password Security. Int J Res Appl Sci Eng Technol. 2019;7(4):2449-2450. doi:10.22214/ijraset.2019.4446

Shinde PD, Patil SH. Secured Password Using Honeyword Encryption. IIoab J. 2018; 9(2): 78-82. https://www.iioab.org/IIOABJ_9.2_78-82.pdf

Guo Y, Zhang Z, Guo Y. Superword: A honeyword system for achieving higher security goals. Comput Secur. 2021; 103: 101689. doi:10.1016/j.cose.2019.101689

Bamane S. Achieving Flatness Using Honeywords Generation Algorithm. Int J Res Appl Sci Eng Technol. 2019; 7(5): 3491-3496. doi:10.22214/ijraset.2019.5572

Kute S, Thite V, Chopade S. Achieving Security using Honeyword. Int J Comput Appl. 2018; 180(49): 43-47. doi:10.5120/ijca2018917333.

Çelik E, Öztürk N, Arya Y. Advancement of the search process of salp swarm algorithm for global optimization problems. Expert Syst Appl. 2021; 182(March). doi:10.1016/j.eswa.2021.115292

Bairathi D, Gopalani D. An improved salp swarm algorithm for complex multi-modal problems. Soft Comput. 2021; 25(15): 10441-10465. doi:10.1007/s00500-021-05757-7

Gupta S, Deep K, Heidari AA, Moayedi H, Chen H. Harmonized Salp Chain-Built Optimization. 37. Springer London; 2021. doi:10.1007/s00366-019-00871-5

Balakrishnan K, Dhanalakshmi R, Khaire UM. Improved salp swarm algorithm based on the levy flight for feature selection. J Supercomput. 2021; 77(11): 12399-12419. doi:10.1007/s11227-021-03773-w

Hegazy AE, Makhlouf MA, El-Tawel GS. Improved salp swarm algorithm for feature selection. J King Saud Univ - Comput Inf Sci. 2020; 32(3): 335-344. doi:10.1016/j.jksuci.2018.06.003

Ouaar F, Boudjemaa R. Modified salp swarm algorithm for global optimisation. Neural Comput Appl. 2021 (July). doi:10.1007/s00521-020-05621-z

Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM. Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Adv Eng Softw. 2017; 114: 163-191. doi:10.1016/j.advengsoft.2017.07.002

Abualigah L, Shehab M, Alshinwan M, Alabool H. Salp swarm algorithm: a comprehensive survey. Neural Comput Appl. 2020; 32(15): 11195-11215. doi:10.1007/s00521-019-04629-4

Slowik A. Swarm Intelligence Algorithms. CRC Press, 1st Ed. 2020. 362P. doi:10.1201/9780429422614