Abstract
Cloned journals refer to deceptive or counterfeit scientific journals that imitate genuine scholarly publications with the intention of misleading scholars into submitting their works. As early career researchers fall prey to these hijacked/cloned journals, research in this direction is important. In this paper, we present “PRAY—Published Research Attestation sYstem”, to validate the research papers published in a journal. We develop a novel random number generator to generate a journal secret key (jsk) based on satellite beacon signals. HMAC of ISSN number with manuscript DOI using keyed jsk; attestation code (ACODE) is generated. This attestation code is used to verify and attest the validity of the genuine publication. The system is tested and validated through verification in Scopus database.
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References
University Grand Commission UGC CARE—List of cloned journals Group I. Available https://ugccare.unipune.ac.in/Apps1/User/Web/CloneJournals Accessed 5 Aug 2023.
University Grand Commission UGC CARE—List of cloned journals Group II. Available: https://ugccare.unipune.ac.in/Apps1/User/Web/CloneJournalsGroupII. Accessed 5 Aug 2023.
University Grand Commission UGC CARE—List of cloned journals Group I. Available: https://ugccare.unipune.ac.in/Apps1/User/Web/CloneJournalsNew. Accessed 5 Aug 2023.
Asim Z, Sorooshian S. Clone journals: a threat to medical research. Sao Paulo Med J. 2019;137(6):550–1. https://doi.org/10.1590/1516-3180.2018.0370160919.
Jalalian M, Dadkhah M. The full story of 90 hijacked journals from August 2011 to June 2015. Geogr Pannon. 2015. https://doi.org/10.5937/GeoPan1502073J.
Dadkhah M, Maliszewski T, Teixeira da Silva JA. Hijacked journals, hijacked web-sites, journal phishing, misleading metrics, and predatory publishing: actual and potential threats to academic integrity and publishing ethics. Forensic Sci Med Pathol. 2016;12(3):353–62. https://doi.org/10.1007/s12024-016-9785-x.
Dadkhah M, Borchardt G. Hijacked journals: an emerging challenge for scholarly publishing. Aesthetic Surg J. 2016;36(6):739–41. https://doi.org/10.1093/asj/sjw026.
Moussa S. A ‘Trojan horse’ in the reference lists: Citations to a hijacked journal in SSCI-indexed marketing journals. J Acad Librariansh. 2021;47(5):102388. https://doi.org/10.1016/j.acalib.2021.102388002E.
Dadkhah M, Maliszewski T, Jazi MD. Characteristics of hijacked journals and predatory publishers: our observations in the academic world. Trends Pharmacol Sci. 2016;37(6):415–8. https://doi.org/10.1016/j.tips.2016.04.002.
Asadi A, Rahbar N, Asadi M, Asadi F, Khalili Paji K. Online-based approaches to identify real journals and publishers from hijacked ones. Sci Eng Ethics. 2017;23(1):305–8. https://doi.org/10.1007/s11948-015-9747-9.
Singhal S, Kalra BS. Publication ethics: Role and responsibility of authors. Indian J Gastroenterol. 2021;40(1):65–71. https://doi.org/10.1007/s12664-020-01129-5.
Negahdary M. Simultaneous submission of a manuscript to more than one journal: challenges and solutions. Publ Res Q. 2017;33(2):188–91. https://doi.org/10.1007/s12109-017-9507-6.
Sivakumar E, Chawla P, Ganesan G (2022) Challenges in scholarly communication and scientific publication—present scenario and opportunities through blockchain solutions. In: The 5th International Conference on Future Networks and Distributed Systems, p. 496–503. doi: https://doi.org/10.1145/3508072.3508174.
Khurana P, Ganesan G, Kumar, G, Sharma K (2023) A bibliometric analysis to unveil the impact of digital object identifiers (DOI) on bibliometric indicators in Proceedings of Third International Conference on Computing, Communications, and Cyber-Security. Lecture Notes in Networks and Systems, vol. 421, p. 859–869. doi: https://doi.org/10.1007/978-981-19-1142-2_67.
Khurana P, Ganesan G, Kumar G, Sharma K. A comparative analysis of unified informetrics with scopus and web of science. J Scientometr Res. 2022;11(2):146–54. https://doi.org/10.5530/jscires.11.2.16.
Harahap MK, Khairina N. Copyright protection of scientific works using digital watermarking by embedding DOI QR code. J Comput Netw Archit High Perform Comput. 2021;3(2):234–40. https://doi.org/10.47709/cnahpc.v3i2.1064.
Gorraiz J, Melero-Fuentes D, Gumpenberger C, Valderrama-Zurián JC. Availability of digital object identifiers (DOIs) in web of science and scopus. J Informet. 2016;10(1):98–109. https://doi.org/10.1016/j.joi.2015.11.008.
Echeverría P, López-Vallejo M. High performance FPGA-oriented Mersenne Twister Uniform Random Number Generator. J Sign Process Syst. 2013;71:105–9. https://doi.org/10.1007/s11265-012-0684-4.
Thomas DB, Luk W. High quality uniform random number generation using LUT Optimised State-transition Matrices. J VLSI Sign Process Syst Sign Image Video Technol. 2007;47:77–92. https://doi.org/10.1007/s11265-006-0014-9.
Bassham LE, et al. SP 800–22 Rev. 1a. A statistical test suite for random and pseudorandom number generators for cryptographic applications. Gaithersburg: National Institute of Standards & Technology; 2010.
National Institute of Standards and Technology. “FIPS-46: Data Encryption Standard (DES).” Revised as FIPS 46–1:1988, FIPS 46–2:1993, FIPS 46–3:1999. 1979. Available: http://csrc.nist.gov/publications/fips/fips46-3/fips46-3.pdf. Accessed 27 Aug 2023.
National Institute of Standards and Technology. “FIPS-197: Advanced Encryption Standard (AES)”. 2001. Available: https://csrc.nist.gov/pubs/fips/197/final Accessed on 27 Aug 2023.
Diffie W, Hellman M. New directions in cryptography. IEEE Trans Inf Theory. 1976;22(6):644–54. https://doi.org/10.1109/TIT.1976.1055638.
Rivest RL, Shamir A, Adleman L. A method of obtaining digital signatures and public-key cryptosystems. Commun ACM. 1978;21(2):120–6.
Elgamal T. A public key cryptosystem and a signature scheme based on discrete logarithms. IEEE Trans Inf Theory. 1985;31(4):469–72. https://doi.org/10.1109/TIT.1985.1057074.
Rabin MO. Digitalized signatures and public-key functions as intractable as factorization. 1979. Available: https://dspace.mit.edu/handle/1721.1/149499. Accessed on 27 Aug 2023.
Rivest R. The MD5 message-digest algorithm. 1992. Available: https://www.rfc-editor.org/rfc/rfc1321. Accessed on 27 Aug 2023.
National Institute of Standards and Technology. FIPS 180–1: Secure Hash Standard, April 1995. Available: https://csrc.nist.gov/pubs/fips/180-1/final. Accessed on 27 Aug 2023.
National Institute of Standards and Technology. FIPS 180–2 Secure Hash Standard. 2002. Available: https://csrc.nist.gov/pubs/fips/180-2/upd1/final. Accessed on 27 Aug 2023
National Institute of Standards and Technology. FIPS 202 Secure Hash Standard. 2015. Available: https://csrc.nist.gov/pubs/fips/202/final. Accessed on 27 Aug 2023].
Aumasson J-P, Meier W, Phan RC-W, Henzen L. The hash function BLAKE. New York: Springer; 2015.
National Institute of Standards and Technology. FIPS 198–1: The Keyed-Hash Message Authentication Code (HMAC). 2008. Available: https://csrc.nist.gov/pubs/fips/198-1/final [Accessed on 27 Aug 2023
Berlekamp ER. Algebraic coding theory. World Science. 1968.
Andoohgin SM, Jazi MD, Borchardt G, et al. Detecting hijacked journals by using classification algorithms. Sci Eng Ethics. 2018;24:655–68. https://doi.org/10.1007/s11948-017-9914-2.
Butnaru A, Mylonas A, Pitropakis N. Towards lightweight URL-based phishing detection. Future Internet. 2021;13(6):154. https://doi.org/10.3390/fi13060154.
El-Rashidy MA. A smart model for web phishing detection based on new proposed feature selection technique. Menoufia J Electron Eng. 2021;30(1):97–104. https://doi.org/10.21608/mjeer.2021.146286.
Wang W, Zhang F, Luo X, Zhang S. PDRCNN: precise phishing detection with recurrent convolutional neural networks. Secur Commun Netw. 2019;2019:2595794. https://doi.org/10.1155/2019/2595794.
Al-Ahmadi S. A deep learning technique for web phishing detection combined URL features and visual similarity. Int J Comput Netw Commun (IJCNC) 12(5). 2020. Available at SSRN: https://ssrn.com/abstract=3716033.
Alqahtani H, Alotaibi SS, Alrayes FS, Al-Turaiki I, Alissa KA, Aziz ASA, Maray M, Al DM. Evolutionary algorithm with deep auto encoder network based website phishing detection and classification. Appl Sci. 2022;12(15):7441. https://doi.org/10.3390/app12157441.
Zhu E, Ju Y, Chen Z, Liu F, Fang X. DTOF-ANN: an artificial neural network phishing detection model based on decision tree and optimal features. Appl Soft Comput. 2020;95:106505. https://doi.org/10.1016/j.asoc.2020.106505.
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This article is part of the topical collection “Intelligent Systems” guest edited by Geetha Ganesan, Lalit Garg, Renu Dhir, Vijay Kumar and Manik Sharma.
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Sivakumar, E., Singh, K.J., Chawla, P. et al. PRAY So You Don’t Become Prey. SN COMPUT. SCI. 5, 324 (2024). https://doi.org/10.1007/s42979-024-02644-4
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DOI: https://doi.org/10.1007/s42979-024-02644-4