jist-202606292320260629230538CMV Verlagkhoffmann@cmv-verlag.comCMV VerlagJournal of Information Systems and Telecommunication (JIST) jist2322-143762920261453VitalisOnyekeUdora Nwawelu Bonaventure EkengwuNnaemekaAsiegbuBenjaminEzurikeDumtochukwuOyekaChukwudi ChukwudozieChimdaluOkide6292026162710.66224/jist.48095.14.53.16http://jist.ir/en/Article/48095http://jist.ir/en/Article/Download/48095http://jist.ir/en/Article/Download/48095http://jist.ir/en/Article/Download/48095http://jist.ir/en/Article/Download/48095http://jist.ir/en/Article/Download/48095http://jist.ir/en/Article/Download/48095http://jist.ir/en/Article/Download/48095[1] M. Mamman, Z. M. Hanapi, A. Abdullah and A. Muhammed “An Adaptive Call Admission Control with Bandwidth Reservation for Downlink LTE Networks,” IEEE Access, vol. 5, pp. 10986–10994, 2017, doi: 10.1109/ACCESS.2017.2713451.[2] U. N. Nwawelu and C. I. Ani, “Improving Exp-Rule Scheduler for Real Time Services in LTE Downlink Networks,” vol. 29, no. 9, pp. 7398–7406, 2020.[3] M. A. Akajewole and D. U. Onyishi, “A Reliable Downlink MIMO Algorithm for Mitigating the Effect of User Equipment Mobility in Multi-User MIMO in Fifth-Generation and Beyond Networks” Nigerian Journal of Technology, vol. 43, no. 2, pp.328 – 337, 2024.[4] Navita and Amandeep, “Performance Analysis of OFDMA, MIMO, and SC-FDMA Technology in 4G LTE networks” 6th International Conference – Cloud System and Big Data Engineering, Noida, India, pp. 554-558, 2016, doi: 10.1109/CONFLUENCE.2016.7508181[5] J. Zyren, “Overview of the 3GPP Long Term Evolution Physical Layer,” 2007.[6] C. O. Nnamani, C. L. Anioke, and C. Ikechukwu, “Improved MLWDF Scheduler for LTE Downlink Transmission,” International Journal of Electronics, vol. 103, no. 11, pp. 1857 – 1867, 2016, doi: 10.1080/00207217.2016.1138536.[7] C. Wu, W. Xu, “Key Technologies in 4G/LTE Network. Encyclopedia of Wireless Networks” Springer, Cham, pp. 695 – 698, 2020, DOI: https://doi.org/10.1007/978-3-319-78262-1_86.[8] O. Liberg, M. Sundberg, Y. P. Eric Wang, J. Bergman, J. Sachs, G. Wikstrom, Cellular Internet of Things (Second Edition), Academic Press, ScienceDirect, pp. 155 – 254, 2020[9] G. O. Ugwu, U. N. Nwawelu, M. A. Ahaneku, and C. I. Ani, “Effect of service differentiation on QoS in IEEE 802.11e enhanced distributed channel access: a simulation approach,” Journal of Engineering and Applied Science, vol. 69, no. 1, pp. 1–18, 2022, doi: 10.1186/s44147-021-00055-3.[10] B. A. Forouzan, Data Communications and Networking, McGraw-Hill International Edition, 4th Edition, New York, 2007. [11] M. M. Umar, A. Mohammed, A. Roko, A. Y. Tambuwal and A. Abdulazeez, “QoS-Aware Call Admission Control (QA-CAC) Scheme for LTE Networks,” 15th International Conference on Electronics, Computer and Computation, Abuja, Nigeria, pp. 1 -5, 2019, doi: 10.1109/ICECCO48375.2019.9043228[12] M. M. Umar, A. Mohammed, A. Roko, A. Y. Tambuwal, and A. Abdulazeez, “Enhanced adaptive call admission control scheme with bandwidth reservation for LTE networks,” International Journal of Mobile Computing and Multimedia Communications, vol. 12, no. 1, pp. 23–42, 2021, doi: 10.4018/IJMCMC.2021010102. [13] K. B. Ali, M. S. Obaidat, F. Zarai, L. Kamoun “Markov Model-based Adaptive CAC Scheme for 3GPP LTE Femtocell Networks,” IEEE ICC- Communications Software, Services and Multimedia Applications Symposium, pp. 6924 – 6928, 2015.[14] R. Khdhir, K. Mnif, A. Belghith and L. Kamoun “An Efficient Call Admission Control Scheme for LTE and LTE-A Networks,” International Symposium on Networks, Computers and Communications, Yasmine, Hammamet, Tunisia, pp. 1–5, 2016, doi: 10.1109/ISNCC.7746068[15] E. E. Ekechukwu, O. C. Nosiri, M. I. Ajumuka, “Efficient Call Admission Control Algorithm for Mobility Management in LTE Networks,” International Journal of Networks and Communications, 2022, 12(1), 28-38.[16] M. Maharazu, M. H. Zurina, A. Azizol and M. Abdullah, “Call Admission Control for Real-Time and Non-real-time traffic for Vehicular LTE downlink networks,” Lecture Notes in Electrical Engineering, 425, pp. 46-53, 2018.[17] D. Pratiwi and I. J. Matheus Edward, “Analysis Efficiency Network Performance of 4G LTE in Video Conference Application,” 16th International Conference on Telecommunication Systems, Services, and Applications (TSSA), Lombok, Indonesia, pp. 1 – 6, 2022, doi: 10.1109/TSSA56819.2022.10063921[18] S. Tabbane, “LTE Planning and Dimensioning” ITU PITA Workshop on Mobile Network Planning and Security, Nadi, Fiji Islands, 23-25 October 2019.MeysamBayatElham Fazeli VeisariMohammad JavadTaghipourian6292026283910.66224/jist.49419.14.53.28http://jist.ir/en/Article/49419http://jist.ir/en/Article/Download/49419http://jist.ir/en/Article/Download/49419http://jist.ir/en/Article/Download/49419http://jist.ir/en/Article/Download/49419http://jist.ir/en/Article/Download/49419http://jist.ir/en/Article/Download/49419http://jist.ir/en/Article/Download/494191. A. Zaušková, M. Kubovics, and M. Vanko, "Financial Behaviour of Users of SOLOMO Platforms during the COVID-19 Pandemic," SHS Web of Conferences, Vol. 92, 2021, pp. 03032. https://doi.org/10.1051/shsconf/20219203032.2. A. S. Sethi, V. Bisht, B. S. Bisht, and J. Singh, "MARVELS OF ADVERTISING: TALES OF TRIUMPH OF THE MOST BUZZING ADVERTISING WORDS IN 2022," The Journal of Contemporary Issues in Business and Government, Vol. 29, No. 1, 2023, pp. 679–687. https://forms.gle/1PsASopxVotMHyj96.3. A. T. Rosário, P. R. Lopes, and F. S. Rosário, "The Digital Marketing for Sustainable Development," JHASS, Vol. 2023, DOI: 10.26855/jhass.2023.01.033.4. S. Zhengyi, "Mobile Marketing Strategies in Omnichannel Context.‏ “ http://edoc.bseu.by:8080/handle/edoc/100092.5. R. K. Brathwaite, "Social Media and Tourism Development in Grenada: A Phenomenological Study," Doctoral Dissertation, 2019. http://researchrepository.napier.ac.uk/Output/2005971.6. M. Kubovics and A. Zaušková, "Possibilities for Data Collection and Example of Visualization of Environmental Activities of Businesses in the SOLOMO Environment," Economics Business Organization Research, Vol. 3, No. 2, 2021, pp. 132–154. https://dergipark.org.tr/en/pub/ebor/issue/65701/878673.7. A. Tarabasz, N. Patwa, K. Kkanzadee, A. Chaudhary, N. Jain, S. Basu, and S. Deepadhar, "Factors Affecting Customer Engagement in Shopping Malls through SOLOMO (Social, Local, Mobile) Application," The International Journal of Management, Vol. 6, No. 4, 2017, pp. 23–39.8. M. Bayat, E. Fazeli Visari, and M. J. Taghi Pourian, "A New Approach to Online Marketing in Iran: Presenting a Qualitative Model of SOLOMO Marketing with Projection Technique," Intelligent Marketing Management, Vol. 5, No. 4, 2024, pp. 69–89, DOI: JABM.3.2.15564.35997779559.9. D. Buhalis and M. Foerste, "SoCoMo Marketing for Travel and Tourism: Empowering Co-Creation of Value," Journal of Destination Marketing & Management, Vol. 4, No. 3, 2015, pp. 151–161. https://doi.org/10.1016/j.jdmm.2015.04.001.10. L. Xia, "Countermeasure Research on Transformation of Local Retail Industry Based on SOLOMO Model-Take Wuhan Hundred Group as an Example," Industrial Economy Review, 2017. www.hillpublisher.com/UpFile/202302/20230210182137.11. E. Fazeli Visari, M. J. Taghi Pourian, G. Ghanbarzadeh, and R. Taveli, "Developing a Viral Marketing Model in Online Businesses Using a Mixed Approach," Intelligent Business Management Studies, Vol. 9, No. 36, 2021, pp. 1–37. https://doi.org/10.22054/ims.2021.53590.1763.12. W. Wei, H. Peng, L. Huang, et al., "Interactive Interface Design of Online Travel Products under SOLOMO Mode," Idea & Design, No. 7, 2018, pp. 44–45.13. C. Huo, J. Hameed, M. W. Sadiq, et al., "Tourism, Environment and Hotel Management: An Innovative Perspective to Address Modern Trends in Contemporary Tourism Management [RETRACTED]," Molecular Human Reproduction, Vol. 27, No. 7, 2021, pp. 27. https://doi.org/10.1108/BPMJ-12-2020-0543.14. P. C. Udomraksasup, C. Panyadee, H. Thaveeseng, et al., "Biodiversity-Based Tourism Management for Community Enterprise Groups, Mae Chaem District, Chiang Mai Province," Allied Business Academies, No. 7, 2021, pp. 31–39.15. A. Shirshamsi, V. Mirabi, E. Hassanpour, and M. Ranjbar, "Design and Validation of an Electronic Marketing Model in the Tourism Industry (Case Study: Yazd Province)," Tourism and Development, Vol. 12, No. 2, 2013, pp. 67–85. https://doi.org/10.22034/jtd.2022.321714.2543.16. B. Asgarnejad Nouri, A. Gholipour, and B. Firouzi, "The Role of Using Social Networks and Electronic Customer Relationship Management in Improving the Marketing Performance of Insurance Companies," Iranian Rubber Industry, Vol. 26, No. 105, 2014, pp. 57–74. https://doi.org/10.22034/irm.2022.152945.17. S. Mohammadi, A. Darzian Azizi, and N. Hadian, "Location-based Services as Marketing Promotional Tools to Provide Value-added in E-tourism," International Journal of Digital Content Management, Vol. 2, No. 3, 2021, pp. 189–215. https://doi.org/10.22054/dcm.2021.13683.18. M. Abdolmohammad Sagha, M. Handijanifard, and A. Koushki Jahromi, "Banks and Social Media Marketing: A Research Based on Content Analysis," Smart Business Management Studies, Vol. 9, No. 35, 2021, pp. 35–76. https://sid.ir/paper/1030656/fa.19. D. H. Zhu, Q. He, and Y. P. Chang, "How SOLOMO-Based Product Recommendations Influence Consumers' Acceptance Intention: The Moderating Role of Gender," International Journal of Services Technology and Management, Vol. 27, No. 1–2, 2021, pp. 129–142. https://doi.org/10.1504/IJSTM.2021.113579.20. A. Madleňák, "Geolocation Services and Marketing Communication from a Global Point of View," SHS Web of Conferences, Vol. 92, 2021, pp. 02040. https://doi.org/10.1051/shsconf/20219202040.21. K. Fahmi, M. Sihotang, R. H. Hadinegoro, et al., "Health Care SMEs Products Marketing Strategy: How the Role of Digital Marketing Technology through Social Media?" UJoST-Universal Journal of Science and Technology, Vol. 1, No. 1, 2022, pp. 16–22. https://doi.org/10.11111/ujost.v1i1.55.22. M. Chen and W. Zhang, "WeChat Knowledge Service System of University Library Based on SOLOMO: A Holistic Design Framework," Journal of Information Science, Vol. 46, No. 5, 2020, pp. 616–629. https://doi.org/10.1177/0165551519860045.23. L. G. R. Antunes, R. de Freitas Souza, A. C. Ferreira, et al., "Projective Techniques: In Search of an Alternative to Validity and Reliability," ReMark-Revista Brasileira de Marketing, Vol. 23, No. 3, 2024, pp. 1277–1314.24. S. Mohammadi, A. Darzian Azizi, and N. Hadianfar, "The Effect of Mobile Marketing on Tourists' Behavioral Intentions: An Analysis of the Role of Tourism Destination Brand Equity," Tourism and Development, Vol. 11, No. 2, 2012, pp. 231–247. https://doi.org/10.22034/jtd.2021.288073.2357.Ohood Al MinshidawiAbdol-HosseinVahabie6292026596610.66224/jist.49750.14.53.59http://jist.ir/en/Article/49750http://jist.ir/en/Article/Download/49750http://jist.ir/en/Article/Download/49750http://jist.ir/en/Article/Download/49750http://jist.ir/en/Article/Download/49750http://jist.ir/en/Article/Download/49750http://jist.ir/en/Article/Download/49750http://jist.ir/en/Article/Download/49750[1] A. Nambiar, "Impact of fake news, message and spam spread through social media on people decision making ability," 2022.[2] D. Assenmacher, L. Clever, L. Frischlich, T. Quandt, H. Trautmann, and C. Grimme, "Demystifying social bots: On the intelligence of automated social media actors," Social Media+ Society, vol. 6, no. 3, p. 2056305120939264, 2020.[3] D. Ajiga, P. A. Okeleke, S. O. Folorunsho, and C. Ezeigweneme, "The role of software automation in improving industrial operations and efficiency," International Journal of Engineering Research Updates, vol. 7, no. 1, pp. 22-35, 2024.[4] M. Cai, H. Luo, X. Meng, Y. Cui, and W. Wang, "Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media," Information Processing & Management, vol. 60, no. 2, p. 103197, 2023.[5] E. Ferrara, "Social bot detection in the age of ChatGPT: Challenges and opportunities," First Monday, 2023.[6] L. Madahali, "Social Bots," Iowa State University, 2024. [7] M. Himelein-Wachowiak et al., "Bots and misinformation spread on social media: Implications for COVID-19," Journal of medical Internet research, vol. 23, no. 5, p. e26933, 2021.[8] H. Aghakhani, A. Machiry, S. Nilizadeh, C. Kruegel, and G. Vigna, "Detecting deceptive reviews using generative adversarial networks," in 2018 IEEE security and privacy workshops (SPW), 2018: IEEE, pp. 89-95. [9] S. Cresci, R. Di Pietro, M. Petrocchi, A. Spognardi, and M. Tesconi, "The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race," in Proceedings of the 26th international conference on world wide web companion, 2017, pp. 963-972. [10] A. Bodaghi, S. Goliaei, and M. Salehi, "The number of followings as an influential factor in rumor spreading," Applied Mathematics and Computation, vol. 357, pp. 167-184, 2019.[11] N. S. Alghamdi and J. S. Alowibdi, "Distinguishing Arabic GenAI-generated Tweets and Human Tweets utilizing Machine Learning," Engineering, Technology & Applied Science Research, vol. 14, no. 5, pp. 16720-16726, 2024.[12] H. Alshammari, A. El-Sayed, and K. Elleithy, "AI-Generated Text Detector for Arabic Language Using Encoder-Based Transformer Architecture," Big Data and Cognitive Computing, vol. 8, no. 3, p. 32, 2024. [Online]. Available: https://www.mdpi.com/2504-2289/8/3/32.[13] A. Yousefi Jordehi, M. Hosseini Khasheh Heyran, S. Ahmadnia, S. A. Mirroshandel, and O. Rambow, "Improving Opinion Mining Through Automatic Prompt Construction," Journal of Information Systems and Telecommunication (JIST), vol. 3, no. 47, p. 216, 2024.[14] A. Roustakiani, N. Abdolvand, and S. R. Harandi, "An improved sentiment analysis algorithm based on appraisal theory and fuzzy logic," Journal of Information Systems and Telecommunication (JIST), vol. 88, 2018.[15] H. Solaimannezhad and O. Fatemi, "Representing a Content-based link Prediction Algorithm in Scientific Social Networks," Journal of Information Systems and Telecommunication (JIST), p. 146, 2017.[16] H. Sharifian, N. Tohidi, and C. Dadkhah, "Designing a Semi-Intelligent Crawler for Creating a Persian Question Answering Corpus Called Popfa," Journal of Information Systems and Telecommunication (JIST), 2024.[17] M. E. Khademi, M. Fakhredanesh, and S. M. Hoseini, "Farsi conceptual text summarizer: a new model in continuous vector space," Journal of Information Systems and Telecommunication (JIST), vol. 1, no. 25, p. 23, 2019.[18] E. Alothali, M. Salih, K. Hayawi, and H. Alashwal, "Bot-MGAT: A Transfer Learning Model Based on a Multi-View Graph Attention Network to Detect Social Bots," Applied Sciences, vol. 12, no. 16, p. 8117, 2022. [Online]. Available: https://www.mdpi.com/2076-3417/12/16/8117.[19] E. Alothali, "STREAM-EVOLVING BOT DETECTION FRAMEWORK USING GRAPH-BASED AND FEATURE-BASED APPROACHES FOR IDENTIFYING SOCIAL BOTS ON TWITTER," 2023.[20] S. Najari, M. Salehi, and R. Farahbakhsh, "GANBOT: a GAN-based framework for social bot detection," Social Network Analysis and Mining, vol. 12, no. 1, p. 4, 2021/11/14 2021, doi: 10.1007/s13278-021-00800-9.[21] A. Bhandarkar, M. A. DM, D. Vishwachetan, A. Mushtaq, D. Kadam, and S. Saxena, "Unmasking the AI Hand: A Machine Learning Approach to Deciphering Authorship," in 2024 3rd International Conference for Innovation in Technology (INOCON), 2024: IEEE, pp. 1-6. [22] A. T. Y. Chong, H. N. Chua, M. B. Jasser, and R. T. K. Wong, "Bot or Human? Detection of DeepFake Text with Semantic, Emoji, Sentiment and Linguistic Features," in 2023 IEEE 13th International Conference on System Engineering and Technology (ICSET), 2-2 Oct. 2023 2023, pp. 205-210, doi: 10.1109/ICSET59111.2023.10295100. [23] F. Wei and U. T. Nguyen, "Twitter Bot Detection Using Neural Networks and Linguistic Embeddings," IEEE Open Journal of the Computer Society, vol. 4, pp. 218-230, 2023, doi: 10.1109/OJCS.2023.3302286.[24] Y. Wu, Y. Fang, S. Shang, J. Jin, L. Wei, and H. Wang, "A novel framework for detecting social bots with deep neural networks and active learning," Knowledge-Based Systems, vol. 211, p. 106525, 2021/01/09/ 2021, doi: https://doi.org/10.1016/j.knosys.2020.106525.[25] M. Mazoochi, N. Asadi, F. Rahmani, and L. Rabiei, "Identifying Persian bots on Twitter; which feature is more important: Account Information or Tweet Contents?," International Journal of Information and Communication Technology Research, vol. 15, no. 1, pp. 35-44, 2023.[26] F. Alhayan and H. Himdi, "Ensemble learning approach for distinguishing human and computer-generated Arabic reviews," PeerJ Computer Science, vol. 10, p. e2345, 2024.[27] F. Harrag, M. Debbah, K. Darwish, and A. Abdelali, "Bert transformer model for detecting Arabic GPT2 auto-generated tweets," arXiv preprint arXiv:2101.09345, 2021.[28] H. Alshammari, "AI-Generated Text Detector for Arabic Language," University of Bridgeport, 2024. [29] H. Almerekhi and T. Elsayed, "Detecting automatically-generated arabic tweets," in Information Retrieval Technology: 11th Asia Information Retrieval Societies Conference, AIRS 2015, Brisbane, QLD, Australia, December 2-4, 2015. Proceedings 11, 2015: Springer, pp. 123-134. [30] A. K. Uysal and S. Gunal, "The impact of preprocessing on text classification," Information processing & management, vol. 50, no. 1, pp. 104-112, 2014.[31] N. Reimers, "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks," arXiv preprint arXiv:1908.10084, 2019.[32] W. I. D. Mining, "Data mining: Concepts and techniques," Morgan Kaufinann, vol. 10, no. 559-569, p. 4, 2006.[33] A. Safaya, M. Abdullatif, and D. Yuret, "Kuisail at semeval-2020 task 12: Bert-cnn for offensive speech identification in social media," arXiv preprint arXiv:2007.13184, 2020.[34] L. Tunstall et al., "Efficient few-shot learning without prompts," arXiv preprint arXiv:2209.11055, 2022.[35] T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, "A simple framework for contrastive learning of visual representations," in International conference on machine learning, 2020: PMLR, pp. 1597-1607. [36] S. I. Hassan, L. Elrefaei, and M. S. Andraws, "Arabic Tweets Spam Detection Based on Various Supervised Machine Learning and Deep Learning Classifiers," MSA Engineering Journal, vol. 2, no. 2, pp. 1099-1119, 2023.DavudMohammadpurMahmoodKhoeini6292026505810.66224/jist.50894.14.53.50http://jist.ir/en/Article/50894http://jist.ir/en/Article/Download/50894http://jist.ir/en/Article/Download/50894http://jist.ir/en/Article/Download/50894http://jist.ir/en/Article/Download/50894http://jist.ir/en/Article/Download/50894http://jist.ir/en/Article/Download/50894http://jist.ir/en/Article/Download/50894[1] H. Tabrizchi, and M. Kuchaki Rafsanjani, “A survey on security challenges in cloud computing: issues, threats, and solutions”, The journal of supercomputing, 2020, Vol. 76, No. 12, pp. 9493-9532. [2] N. Mohammadi, A. Rezakhani, and Haj H. Seyyed Javadi, “FLHB-AC: federated learning history-based access control using deep neural networks in healthcare system”, Journal of Information Systems and Telecommunication (JIST), 2024, Vol.2, No. 46, pp. 90.[3] C. Choudhary, V. Narayan, and U. Kumar Lilhore, “Cloud Security: Challenges and Strategies for Ensuring Data Protection”, in Proc. of 2023 3rd International Conference on Technological Advancements in Computational Sciences (ICTACS), Nov. 2023, pp. 669-673. [4] L. Rikhtechi, V. Rafe, and A. Rezakhani, “Secured access control in security information and event management systems”, Journal of Information Systems and Telecommunication, 2021, Vol. 9, No. 33, pp. 67-78.[5] U. Butt, R. Amin, M. Mehmood, H. Aldabbas, M. Alharbi, and N. Albaqami, “Cloud security threats and solutions: A survey”, Wireless Personal Communications, 2023, Vol. 128, No. 1, pp. 387-413. [6] C. Wang, K. Ren, W. Lou, and J. Li, “Toward publicly auditable secure cloud data storage services”, IEEE Network, 2010, Vol. 24, No. 4, pp. 19-24. [7] S. Azizi, and D. Mohammadpur, “Searchable Encrypted String for Query Support on Different Encrypted Data Types”, KSII Transactions on Internet & Information Systems, 2020, Vol. 14, No. 10, pp. 4198-4213. [8] E. Khalaf, and M. Kadi, “A survey of access control and data encryption for database security”, Journal of King Abdulaziz University, 2017, Vol. 28, No. 1, pp. 19-30. [9] S. K. Kermanshahi, J. K. Liu, R. Steinfeld, S. Nepal, S. Lai, R. Loh, and C. Zuo, “Multi-client cloud-based symmetric searchable encryption”, IEEE Transactions on Dependable and Secure Computing, 2019, Vol. 18, No. 5, pp. 2419-2437. [10]Z. He, W. K. Wong, B. Kao, D. W. L. Cheung, R. Li, S. M. Yiu, and E. Lo, “SDB: A secure query processing system with data interoperability”, in Proc. of the VLDB Endowment, 2015, Vol. 8, No. 12, pp. 1876-1879. [11] W. K. Wong, B. Kao, D. W. L. Cheung, R. Li, and S. M. Yiu, “Secure query processing with data interoperability in a cloud database environment”, in Proc. of the 2014 ACM SIGMOD international conference on Management of data, 2014, pp. 1395-1406. [12]E. Bertino, and R. Sandhu, “Database security-concepts, approaches, and challenges”, IEEE Transactions on Dependable and secure computing, 2005, Vol. 2, No.1, pp. 2-19. [13]Z. Liu, J. Li, J. Li, C. Jia, J. Yang, and K. Yuan, “SQL-based fuzzy query mechanism over the encrypted database”, International Journal of Data Warehousing and Mining (IJDWM), 2014, Vol. 10, No. 4, pp. 71-87. AliMaroosiAmir Hossein HojatiniaArashDeldari629202611510.66224/jist.51082.14.53.1http://jist.ir/en/Article/51082http://jist.ir/en/Article/Download/51082http://jist.ir/en/Article/Download/51082http://jist.ir/en/Article/Download/51082http://jist.ir/en/Article/Download/51082http://jist.ir/en/Article/Download/51082http://jist.ir/en/Article/Download/51082http://jist.ir/en/Article/Download/51082[1] M. Moudi, A. Soleimani, and A. Hojjatinia, "A Survey of Intrusion Detection Systems Based On Deep Learning for IoT Data," Journal of Information Systems and Telecommunication (JIST), vol. 3, no. 47, p. 197, 2024, doi: https://doi.org/10.61186/jist.44521.12.47.197.[2] L. Kang and H. Shen, "A transfer learning based abnormal can bus message detection system," in 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS), Denver, CO, USA, 2021: IEEE, pp. 545-553, doi: https://doi.org/10.1109/MASS52906.2021.00073. [3] Z. Zhang, Q. Liu, S. Qiu, S. Zhou, and C. Zhang, "Unknown attack detection based on zero-shot learning," IEEE Access, vol. 8, pp. 193981-193991, 2020, doi: https://doi.org/10.1109/ACCESS.2020.3033494.[4] L. Bilge and T. Dumitraş, "Before we knew it: an empirical study of zero-day attacks in the real world," in Proceedings of the 2012 ACM conference on Computer and communications security, New York, NY, USA, 2012: Association for Computing Machinery, pp. 833-844, doi: https://doi.org/10.1145/2382196.2382284. [5] H. B. Arteaga. "Intrusion Detection System using Machine Learning." https://github.com/BioAITeam/Intrusion-Detection-System-using-Machine-Learning/tree/main/DBs (accessed.[6] E. Seo, H. M. Song, and H. K. Kim, "GIDS: GAN based intrusion detection system for in-vehicle network," in 2018 16th Annual Conference on Privacy, Security and Trust (PST), Belfast, Ireland, 2018: IEEE, pp. 1-6, doi: https://doi.org/10.1109/PST.2018.8514157. [7] P. Wu, H. Guo, and R. Buckland, "A transfer learning approach for network intrusion detection," in 2019 IEEE 4th international conference on big data analytics (ICBDA), Suzhou, China, 2019: IEEE, pp. 281-285, doi: https://doi.org/10.1109/ICBDA.2019.8713213. [8] Y. Yang, J. Cheng, Z. Liu, H. Li, and G. Xu, "A multi-classification detection model for imbalanced data in NIDS based on reconstruction and feature matching," Journal of Cloud Computing, vol. 13, no. 1, p. 31, 2024, Art no. 31, doi: https://doi.org/10.1186/s13677-023-00584-7.[9] I. Ahmed, G. Jeon, and A. Ahmad, "Deep learning-based intrusion detection system for internet of vehicles," IEEE Consumer Electronics Magazine, vol. 12, no. 1, pp. 117-123, 2021, doi: https://doi.org/10.1109/MCE.2021.3139170.[10] U. K. Lilhore et al., "HIDM: Hybrid Intrusion Detection Model for Industry 4.0 Networks Using an Optimized CNN-LSTM with Transfer Learning," Sensors, vol. 23, no. 18, p. 7856, 2023, doi: https://doi.org/10.3390/s23187856 [11] W. Yutao, L. Zhongtian, B. Yi, L. Jie, X. Fangzheng, and B. Yu, "Internet of Things Intrusion Detection System based on Transfer Learning," in 2022 IEEE 2nd International Conference on Electronic Technology, Communication and Information (ICETCI), Changchun, China, 2022: IEEE, pp. 25-30, doi: https://doi.org/10.1109/ICETCI55101.2022.9832387. [12] O. D. Okey, D. C. Melgarejo, M. Saadi, R. L. Rosa, J. H. Kleinschmidt, and D. Z. Rodríguez, "Transfer learning approach to IDS on cloud IoT devices using optimized CNN," IEEE Access, vol. 11, pp. 1023-1038, 2023, doi: https://doi.org/10.1109/ACCESS.2022.3233775.[13] J. Zhao, S. Shetty, J. W. Pan, C. Kamhoua, and K. Kwiat, "Transfer learning for detecting unknown network attacks," EURASIP Journal on Information Security, vol. 2019, pp. 1-13, 2019, Art no. 1, doi: https://doi.org/10.1186/s13635-019-0084-4.[14] C.-W. Tien, T.-Y. Huang, P.-C. Chen, and J.-H. Wang, "Using autoencoders for anomaly detection and transfer learning in IoT," Computers, vol. 10, no. 7, p. 88, 2021.[15] H. Elubeyd, D. Yiltas-Kaplan, and Ş. Bahtıyar, "A Multi-Modal Deep Transfer Learning Framework for Attack Detection in Software-Defined Networks," IEEE Access, vol. 11, pp. 114128-114145, 2023, doi: https://doi.org/10.1109/ACCESS.2023.3324878.[16] H. Wang, Y. Wang, and Y. Guo, "A Novel Approach of Unknown Network Attack Detection Based on Zero-Shot Learning," in 2021 IEEE International Conference on Data Science and Computer Application (ICDSCA), Dalian, China, 2021: IEEE, pp. 312-318, doi: https://doi.org/10.1109/ICDSCA53499.2021.9650182. [17] G. Zachos, G. Mantas, K. Porfyrakis, J. M. C. S. d. Bastos, and J. Rodriguez, "Anomaly-Based Intrusion Detection for IoMT Networks: Design, Implementation, Dataset Generation, and ML Algorithms Evaluation," IEEE Access, vol. 13, pp. 41994-42028, 2025, doi: 10.1109/ACCESS.2025.3547572.[18] G. Logeswari, J. D. Roselind, K. Tamilarasi, and V. Nivethitha, "A Comprehensive Approach to Intrusion Detection in IoT Environments Using Hybrid Feature Selection and Multi-Stage Classification Techniques," IEEE Access, vol. 13, pp. 24970-24987, 2025, doi: 10.1109/ACCESS.2025.3532895.[19] U. C. Akuthota and L. Bhargava, "Transformer-Based Intrusion Detection for IoT Networks," IEEE Internet of Things Journal, vol. 12, no. 5, pp. 6062-6067, 2025, doi: 10.1109/JIOT.2025.3525494.[20] J. Gao, M. Fan, Y. He, D. Han, Y. Lu, and Y. Qiao, "MACAE: memory module-assisted convolutional autoencoder for intrusion detection in IoT networks," The Journal of Supercomputing, vol. 81, no. 1, p. 231, 2024/12/02 2024, doi: 10.1007/s11227-024-06704-7.[21] Z. Alwaeli, O. A. Fadare, and F. Al-Turjman, "Developing Deep Learning-Based Network Intrusion Detection Systems (NIDS) for Iot Networks," in Smart Infrastructures in the IoT Era, F. Al-Turjman Ed. Cham: Springer Nature Switzerland, 2025, pp. 1105-1113.[22] M. L. Hernandez-Jaimes, A. Martinez-Cruz, K. A. Ramírez-Gutiérrez, and A. Morales-Reyes, "Network traffic inspection to enhance anomaly detection in the Internet of Things using attention-driven Deep Learning," Integration, vol. 103, p. 102398, 2025/07/01/ 2025, doi: https://doi.org/10.1016/j.vlsi.2025.102398.[23] S. Walling and S. Lodh, "An Extensive Review of Machine Learning and Deep Learning Techniques on Network Intrusion Detection for IoT," Transactions on Emerging Telecommunications Technologies, vol. 36, no. 2, p. e70064, 2025, doi: https://doi.org/10.1002/ett.70064.[24] Y. Fan, Y. Li, M. Zhan, H. Cui, and Y. Zhang, "Iotdefender: A federated transfer learning intrusion detection framework for 5g iot," in 2020 IEEE 14th international conference on big data science and engineering (BigDataSE), Guangzhou, China, 2020: IEEE, pp. 88-95, doi: https://doi.org/10.1109/BigDataSE50710.2020.00020. [25] N. Khatri, S. Lee, and S. Y. Nam, "Transfer Learning-based Intrusion Detection System for a Controller Area Network," IEEE Access, vol. 11, pp. 120963-120982, 2023, doi: https://doi.org/10.1109/ACCESS.2023.3328182.[26] Ü. Çavuşoğlu, D. Akgun, and S. Hizal, "A novel cyber security model using deep transfer learning," Arabian Journal for Science and Engineering, vol. 49, no. 3, pp. 3623-3632, 2024, doi: https://doi.org/10.1007/s13369-023-08092-1.[27] U. S. N. Australia. "The UNSW-NB15 Dataset." https://research.unsw.edu.au/projects/unsw-nb15-dataset (accessed.[28] C. I. f. Cybersecurity. "ISCX NSL-KDD dataset 2009 " https://www.unb.ca/cic/datasets/nsl.html (accessed.[29] cloudstor. "Download Ton-IoT Dataset." https://cloudstor.aarnet.edu.au/plus/s/ds5zW91vdgjEj9i?path=%2FTrain_Test_datasets (accessed.[30] I. Idrissi, M. Azizi, and O. Moussaoui, "Accelerating the update of a DL-based IDS for IoT using deep transfer learning," Indones. J. Electr. Eng. Comput. Sci, vol. 23, no. 2, pp. 1059-1067, 2021, doi: https://doi.org/10.11591/ijeecs.v23.i2.[31] G.-P. Fernando, A.-A. H. Brayan, A. M. Florina, C.-B. Liliana, A.-M. Héctor-Gabriel, and T.-S. Reinel, "Enhancing Intrusion Detection in IoT Communications through ML Model Generalization with a New Dataset (IDSAI)," IEEE Access, vol. 11, pp. 70542-70559, 2023.[32] Y. Wang, Y. Lai, Y. Chen, J. Wei, and Z. Zhang, "Transfer learning-based self-learning intrusion detection system for in-vehicle networks," Neural Computing and Applications, vol. 35, pp. 10257–10273, 2023, doi: https://doi.org/10.1007/s00521-023-08233-5.[33] J. Wang, P. Li, W. Kong, and R. An, "Unknown Security Attack Detection of Industrial Control System by Deep Learning," Mathematics, vol. 10, no. 16, p. 2872, 2022, doi: https://doi.org/10.3390/math10162872.[34] F. S. Alrayes, M. Zakariah, S. U. Amin, Z. I. Khan, and M. Helal, "Intrusion detection in IoT systems using denoising autoencoder," IEEE Access, 2024.[35] C. Zha et al., "SKT-IDS: Unknown attack detection method based on Sigmoid Kernel Transformation and encoder–decoder architecture," Computers & Security, vol. 146, p. 104056, 2024.Reza AmiriMohammad HadiZahediMehdiAzadimotlagh6292026404910.66224/jist.51448.14.53.40http://jist.ir/en/Article/51448http://jist.ir/en/Article/Download/51448http://jist.ir/en/Article/Download/51448http://jist.ir/en/Article/Download/51448http://jist.ir/en/Article/Download/51448http://jist.ir/en/Article/Download/51448http://jist.ir/en/Article/Download/51448http://jist.ir/en/Article/Download/51448References[1] Ali, A., Abd Razak, S., Othman, S. H., Eisa, T. A. E., Al-Dhaqm, A., Nasser, M., ... & Saif, A. (2022). Financial fraud detection based on machine learning: a systematic literature review. Applied Sciences, 12(19), 9637.[2] Zhou, Y., Li, H., Xiao, Z., & Qiu, J. (2023). A user-centered explainable artificial intelligence approach for financial fraud detection. Finance Research Letters, 58, 104309.[3] Zhang, Y., Xu, T., Song, X., Zhu, X. F., Feng, Z., & Wu, X. J. (2024). Towards accurate unsupervised video captioning with implicit visual feature injection and explicit. Pattern Recognition Letters, 183, 133-139.[4] Li, Z., Zhu, Y., & Van Leeuwen, M. (2023). A survey on explainable anomaly detection. ACM Transactions on Knowledge Discovery from Data, 18(1), 1-54.[5]. Iqbal, A., & Amin, R. (2025). An efficient mechanism for time series forecasting and anomaly detection using explainable artificial intelligence. The Journal of Supercomputing, 81(4), 523.[6] Dhanesha, P., & Mehta, D. (2024, December). Uncovering Hidden Frauds: Isolation Forest-Based Anomaly Detection in Credit Card Transactions. In International Conference on Information and Communication Technology for Competitive Strategies (pp. 39-51). Singapore: Springer Nature Singapore.[7] Neloy, A. A., & Turgeon, M. (2024). A comprehensive study of auto-encoders for anomaly detection: Efficiency and trade-offs. Machine Learning with Applications, 17, 100572.[8] Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in neural information processing systems, 30.[9] Ribeiro, M. T., Singh, S., & Guestrin, C. (2016, August). " Why should i trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1135-1144).[10] Dal Pozzolo, A., Boracchi, G., Caelen, O., Alippi, C., & Bontempi, G. (2017). Credit card fraud detection: a realistic modeling and a novel learning strategy. IEEE transactions on neural networks and learning systems, 29(8), 3784-3797.[11] Agrawal, V., Panigrahi, B. K., & Subbarao, P. M. V. (2018). Increasing reliability of fault detection systems for industrial applications. IEEE Intelligent Systems, 33(3), 28-39.[12] Dal Pozzolo, A., Caelen, O., Le Borgne, Y. A., Waterschoot, S., & Bontempi, G. (2014). Learned lessons in credit card fraud detection from a practitioner perspective. Expert systems with applications, 41(10), 4915-4928.[13] Branco, P., Torgo, L., & Ribeiro, R. P. (2017, April). Relevance-based evaluation metrics for multi-class imbalanced domains. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 698-710). Cham: Springer International Publishing. GholamrezaAhmadiHamedMonkaresi6292026677810.66224/jist.52165.14.53.67http://jist.ir/en/Article/52165http://jist.ir/en/Article/Download/52165http://jist.ir/en/Article/Download/52165http://jist.ir/en/Article/Download/52165http://jist.ir/en/Article/Download/52165http://jist.ir/en/Article/Download/52165http://jist.ir/en/Article/Download/52165http://jist.ir/en/Article/Download/521651. Vahid Shahrivari, Mohammad Mahdi Darabi, Mohammad Izadi. Phishing detection using machine learning techniques. arXiv preprint arXiv:200911116. 2020; 2. Aljofey A, Jiang Q, Rasool A, Chen H, Liu W, Qu Q, et al. An effective detection approach for phishing websites using URL and HTML features. Sci Rep. 2022 May 25;12(1):8842. 3. Akinyelu AA. Machine Learning and Nature Inspired Based Phishing Detection: A Literature Survey. International Journal on Artificial Intelligence Tools. 2019 Aug 29;28(05):1930002. 4. Goenka R, Chawla M, Tiwari N. A comprehensive survey of phishing: mediums, intended targets, attack and defence techniques and a novel taxonomy. Int J Inf Secur. 2024 Apr 19;23(2):819–48. 5. Sarker IH. Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective. SN Comput Sci. 2021 May 20;2(3):154. 6. Khan A, Sohail A, Zahoora U, Qureshi AS. A survey of the recent architectures of deep convolutional neural networks. Artif Intell Rev. 2020 Dec 21;53(8):5455–516. 7. Prakash P, Kumar M, Kompella RR, Gupta M. PhishNet: Predictive Blacklisting to Detect Phishing Attacks. In: 2010 Proceedings IEEE INFOCOM. IEEE; 2010. p. 1–5. 8. Zhang N, Tan Y an, Yang C, Li Y. Deep learning feature exploration for Android malware detection. Appl Soft Comput. 2021 Apr;102:107069. 9. Khanan A, Abdelgadir Mohamed Y, Mohamed AHHM, Bashir M. From Bytes to Insights: A Systematic Literature Review on Unraveling IDS Datasets for Enhanced Cybersecurity Understanding. IEEE Access. 2024;12:59289–317. 10. Arivukarasi M, Antonidoss A. Performance Analysis of Malicious URL Detection by using RNN and LSTM. In: 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC). IEEE; 2020. p. 454–8. 11. Shrivastava GK, Pateriya RK, Kaushik P. An efficient focused crawler using LSTM-CNN based deep learning. International Journal of System Assurance Engineering and Management. 2023 Feb 19;14(1):391–407. 12. Abdali S, Gurav R, Menon S, Fonseca D, Entezari N, Shah N, et al. Identifying Misinformation from Website Screenshots. Proceedings of the International AAAI Conference on Web and Social Media. 2021 May 22;15:2–13. Costa H V., Ribeiro AGR, Souza VMA. Fusion of Image Representations for Time Series Classification with Deep Learning. In 2024. p. 235–50. 14. Jang S, Li S, Sung Y. FastText-Based Local Feature Visualization Algorithm for Merged Image-Based Malware Classification Framework for Cyber Security and Cyber Defense. Mathematics. 2020 Mar 24;8(3):460. 15. Adebowale MA, Lwin KT, Sánchez E, Hossain MA. Intelligent web-phishing detection and protection scheme using integrated features of Images, frames and text. Expert Syst Appl. 2019 Jan;115:300–13. 16. Nguyen G, Dlugolinsky S, Bobák M, Tran V, López García Á, Heredia I, et al. Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey. Artif Intell Rev. 2019 Jun 19;52(1):77–124. 17. Gupta P, Bagchi A. Introduction to Pandas. In 2024. p. 161–96. 18. Zhang Z. Improved Adam Optimizer for Deep Neural Networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS). IEEE; 2018. p. 1–2. 19. Muhammad MB, Yeasin M. Eigen-CAM: Class Activation Map using Principal Components. In: 2020 International Joint Conference on Neural Networks (IJCNN). IEEE; 2020. p. 1–7.