Honeywords Generation Technique based on Meerkat Clan Algorithm and WordNet
DOI:
https://doi.org/10.31185/wjps.269Keywords:
Honeywords system, Meerkat clan algorithm, Password security, and WordNet.Abstract
The efficiency of the Honeywords approach has been proven to be a significant tool for boosting password security. The suggested system utilizes the Meerkat Clan Algorithm (MCA) in conjunction with WordNet to produce honeywords, thereby enhancing the level of password security. The technique of generating honeywords involves data sources from WordNet, which contributes to the improvement of authenticity and diversity in the honeywords. The method encompasses a series of consecutive stages, which include the tokenization of passwords, the formation of alphabet tokens using the Meerkat Clan Algorithm (MCA), the handling of digit tokens, the creation of unique character tokens, and the consolidation of honeywords. The optimization of the performance of the Meerkat Clan Algorithm (MCA) involves the careful selection of parameters. The experimental findings have exhibited noteworthy levels of precision and optimum efficacy, particularly in tasks such as proposing words with similar meanings, forecasting numerical values, and producing distinctive symbols. The attainment of this achievement is facilitated by a confluence of factors, encompassing the caliber of data, the judicious use of algorithms or models, and the ongoing process of iterative improvement to consistently enhance outcomes. In order to achieve the appropriate levels of accuracy and functionality, it is crucial to engage in the process of conducting experiments, thoroughly testing the system, and making necessary improvements. The empirical findings provide confirmation of the effectiveness of the MCA in producing a varied and protected collection of honeywords. This is especially evident in the case of alphabet tokens, which are distinguished by their autonomous creation and strong security characteristics. The analysis of correction rates, specifically in relation to the password "Lion1999*," demonstrates the aforementioned results. This study reveals an average accuracy of honeyword production up to 0.729847632111541. In the same manner, the accuracy of the password "house2000" is determined to be 0.761325846711256. Additionally, when considering a sample of 100 passwords, the mean accuracy of honeyword creation is calculated to be 0.7073897168887518. The findings collectively highlight the effectiveness of the MCA in generating honeywords that possess improved security characteristics.
References
. Thite. M. V, Nighot. M., “Honeyword for security: A review,” International Journal, Vol.6, no.5, 2021.
. Ali. S. M, Mahmood. N. T. and Yousif. S. A., “Meerkat Clan Algorithm for Solving N-Queen Problems,” Iraqi Journal of Science, pp.2082-2089, 2021.
. Chang. D, Goel. A., Mishra. S and Sanadhya. S. K, “Generation of secure and reliable honeywords, preventing false detection,” IEEE Transactions on Dependable and Secure Computing, Vol.16, no.5, pp.757-769, 2018.
. Alshaibi. A, Al-Ani. M, Al-Azzawi. A, Konev. A. and Shelupanov. A, “The comparison of cybersecurity datasets,” Data, Vo.l7, no.2, pp.22, 2022.
. Thanda. Win and Myat Moe. Khin Su “Protecting private data using improved honey encryption and honeywords generation algorithm,” Diss. MERAL Portal, 2018.
. A. Yasser. Yasser, T. Ahmed. Sadiq, and AlHamdani. Wasim “A Proposed Harmony Search Algorithm for Honeyword Generation,” Advances in Human Computer Interaction, 2022.
. AlHamdani. Wasim, T. Ahmed. Sadiq and A. Yasser. Yasser, “Honeyword Generation Using a Proposed Discrete Salp Swarm Algorithm,” Baghdad Science Journal, vol 20, no.2, pp. 125-131, 2023.
. Yu. Fangyi, Martin, and Migual. Vargas, “Targeted honeyword generation with language models,” arXiv preprint arXiv, pp.2208.06946, 2022.
. A. Yasser. Yasser, T. Ahmed. Sadiq, and AlHamdani. Wasim, “A scrutiny of honeyword generation methods: Remarks on strengths and weaknesses points,” Cybernetics and Information Technologies, Vol.22, no.2, pp.3-25, 2022.
. Sailaja. C. V, Reddy. B. T., “Creating secure and dependable honey words to increase password security,” Annals of the Romanian Society for Cell Biology, pp.19588-19594, 2021.
. Kanta. A., Coisel. I, and Scanlon. M, “A survey exploring open source Intelligence for smarter password cracking” Forensic Science International: Digital Investigation, Vol.35, pp.301075, 2020.
. Li. W, Zeng. J, “Leet usage and its effect on password security,” IEEE Transactions on Information Forensics and Security, Vol.16, pp.2130-2143, 2021.
. Priyatno. J., and Bijaksana. M. A, “Clustering synonym sets in english wordNet,” In 2019 7th International Conference on Information and Communication Technology (ICoICT) pp. 1-4(2019). IEEE.
. Cruz-Duarte, J. M. Ortiz-Bayliss, J. C. Amaya, I. and Pillay. N, “Global optimisation through hyper-heuristics: Unfolding population-based metaheuristics,” Applied Sciences, Vol.11, no.12, pp.5620, 2021.
. Saleh. H. A, Sattar. R. A, Saeed. E. M. H and Abdul-Zahra. D. S, “Hybrid features selection method using random forest and meerkat clan algorithm,” TELKOMNIKA (Telecommunication Computing Electronics and Control, Vol20, no.5, pp.1046-1054, 2022.
. Muhsen. A. R., Jumaa. G. G., AL Bakri. N. F. and Sadiq. A. T, “Feature Selection Strategy for Network Intrusion Detection System (NIDS) Using Meerkat Clan Algorithm,” International Journal of Interactive Mobile Technologies, Vol.15, no.16, 2021.
. Chakravarty. P, Cozzi. G, Ozgul. A. and Aminian. k, “A novel biomechanical approach for animal behaviour recognition using accelerometers,” Methods in Ecology and Evolution, Vol.10, no.6, pp.802-814, 2019.
. Mahmood. N, “Solving Capacitated Vehicle Routing Problem Using Meerkat Clan Algorithm,” INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY, Vol.19, no.4, pp.689-694, 2022.
. Sadiq. A. T., Abdullah. H. S., and Ahmed. Z. O, “Solving flexible job shop scheduling problem using meerkat clan algorithm,” Iraqi Journal of Science, pp.754-761, 2018.
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