Anti-Cyber Childhood Exploitation: An Online Game Chat Monitoring System
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Despite its revolutionary benefits, the Internet has been utilized to abuse children through chat in online gaming. Exhibiting harmful content can negatively impact children's psychology and behaviour, particularly during their developmental years. This paper examines the psychological effects of online predation and the growing risks posed by Internet predators (with focus on children under 15 years old). This paper proposed an Anti-Cyber Childhood Exploitation (A2CE) system, a comprehensive framework designed to detect and prevent three major forms of online abuse: psychological manipulation, cyberbullying, and online grooming. Leveraging advanced Natural language processing (NLP) techniques, A2CE analyses online conversations in real time. The system is trained on three well-known datasets: PUBG, Dota 2, and PAN12. Upon detecting an attack, A2CE provides immediate alerts and warnings to parents, helping mitigate psychological harm. The experimental results demonstrate high detection accuracy: 96.8% for grooming, 94.2% for psychological manipulation, and 92.8% for cyberbullying. These findings indicate that A2CE can be considered a powerful tool for protecting children in this critical age.
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[1] Abimbola-akinola and A. Dickson, “The Cyber Crime and Internet and Internet Sexual Exploitation of Children,” All Student Theses. 107., 2017.
[2] K. J. Brakas and M. Alanezi, “Measuring the Extent of Cyberbullying Comments in Facebook Groups for Mosul University Students,” Mesopotamian Journal of CyberSecurity, vol. 5, no. 2, pp. 337–348, May 2025, doi: 10.58496/MJCS/2025/021.
[3] K. A. Al-Enezi, S. S. M. Aldabbagh, I. F. T. Al Shaikhli, and J. M. Alwuhaib, “The influence of internet and social media on purchas decisions in Indonesia and a comparison between Indonesia and Kuwait,” J Comput Theor Nanosci, vol. 16, no. 3, 2019, doi: 10.1166/jctn.2019.7993.
[4] J. Sekeres, “Methodologies for the Management, Normalization and Identification of Sexual Predation of Minors in Cyber Chat Logs ,” Concordia University, Canada, 2022.
[5] S. H. Abdullah, A. H. Ayad, N. M. Mohammed, and R. M. A. Saad, “Adaptive Fault-Tolerance During Job Scheduling in Cloud Services Based on Swarm Intelligence and Apache Spark,” International Journal of Intelligent Systems and Applications in Engineering, vol. 11, no. 2, pp. 74–81, Feb. 2023.
[6] M. Al-Dabbagh and A. K. Ali, “Employing light fidelity technology in health monitoring system,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 26, no. 2, 2022, doi: 10.11591/ijeecs.v26.i2.pp989-997.
[7] S. Ali, H. A. Haykal, and E. Y. M. Youssef, “Child Sexual Abuse and the Internet—A Systematic Review,” Human Arenas, vol. 6, no. 2, 2023, doi: 10.1007/s42087-021-00228-9.
[8] S. A. Baker and A. S. Nori, “Internet of Things Security: A Survey,” in Advances in Cyber Security. ACeS 2020. Communications in Computer and Information Science, vol. 1347, Singapore: Springer, 2021, pp. 95–117.
[9] S. J. Mohammed and D. B. Taha, “Paillier cryptosystem enhancement for Homomorphic Encryption technique,” Multimed Tools Appl, vol. 83, no. 8, 2024, doi: 10.1007/s11042-023-16301-0.
[10] A. Q. Saeed et al., “Integrating Three Machine Learning Algorithms in Ensemble Learning Model for Improving Content-based Spam Email Recognition,” Journal of Soft Computing and Data Mining, vol. 5, no. 2, pp. 188–196, Dec. 2024, doi: 10.30880/jscdm.2024.05.02.014.
[11] S. A. Pasha, S. Ali, and R. Jeljeli, “Artificial Intelligence Implementation to Counteract Cybercrimes Against Children in Pakistan,” Human Arenas, 2022, doi: 10.1007/s42087-022-00312-8.
[12] I. M. Ahmed, A. K. Ali, and M. S. Mahmood, “Employing Hybrid Watermarking to Improve Email Security Against Cyber Attacks,” Journal of Soft Computing and Data Mining, vol. 6, no. 1, pp. 435–447, Jun. 2025, doi: 10.30880/jscdm.2025.06.01.029.
[13] J. K. Wang, S. K. Wang, E. B. Lee, and R. T. Chang, “Natural Language Processing (NLP) in AI,” in Digital Eye Care and Teleophthalmology: A Practical Guide to Applications, 2023. doi: 10.1007/978-3-031-24052-2_17.
[14] K. J. Brakas and M. Alanezi, “A Dynamic DNA Cryptosystem for Secure File Sharing,” Mesopotamian Journal of CyberSecurity, vol. 5, no. 2, pp. 424–435, Jun. 2025.
[15] P. Anderson, Z. Zuo, L. Yang, and Y. Qu, “An Intelligent Online Grooming Detection System Using AI Technologies,” in IEEE International Conference on Fuzzy Systems, 2019. doi: 10.1109/FUZZ-IEEE.2019.8858973.
[16] M. M. Islam, M. A. Uddin, L. Islam, A. Akter, S. Sharmin, and U. K. Acharjee, “Cyberbullying Detection on Social Networks Using Machine Learning Approaches,” in 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020, 2020. doi: 10.1109/CSDE50874.2020.9411601.
[17] S. Preuß et al., “Automatically identifying online grooming chats using CNN-based feature extraction,” in KONVENS 2021 - Proceedings of the 17th Conference on Natural Language Processing, 2021.
[18] P. R. Borj, K. Raja, and P. Bours, “Detecting Online Grooming By Simple Contrastive Chat Embeddings,” in IWSPA 2023 - Proceedings of the 9th ACM International Workshop on Security and Privacy Analytics, 2023. doi: 10.1145/3579987.3586564.
[19] J. Street, I. Ihianle, F. Olajide, and A. Lotfi, “Enhanced Online Grooming Detection Employing Context Determination and Message-Level Analysis,” {arXiv preprint arXiv:2409.07958, Dec. 2024.
[20] M. M. Rezapour, A. Fatemi, and M. A. Nematbakhsh, “A methodology for using players’ chat content for dynamic difficulty adjustment in metaverse multiplayer games,” Appl Soft Comput, vol. 156, 2024, doi: 10.1016/j.asoc.2024.111497.
[21] Z. Yang, C. Huang, and J. Liu, “Unveiling Cybersecurity Threats from Online Chat Groups: A Triple Extraction Approach,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2023. doi: 10.1007/978-3-031-40292-0_15.
[22] A. Muneer, A. Alwadain, M. G. Ragab, and A. Alqushaibi, “Cyberbullying Detection on Social Media Using Stacking Ensemble Learning and Enhanced BERT,” Information (Switzerland), vol. 14, no. 8, 2023, doi: 10.3390/info14080467.
[23] A. Bozyiğit, S. Utku, and E. Nasibov, “Cyberbullying detection: Utilizing social media features,” Expert Syst Appl, vol. 179, 2021, doi: 10.1016/j.eswa.2021.115001.
[24] A. K. Singh, “Sentiment Analysis of Dota 2 videogame chat in context of Cyber-bullying,” National College of Ireland, Ireland, 2022.
[25] C. Maurya, T. Muhammad, P. Dhillon, and P. Maurya, “The effects of cyberbullying victimization on depression and suicidal ideation among adolescents and young adults: a three year cohort study from India,” BMC Psychiatry, vol. 22, no. 1, 2022, doi: 10.1186/s12888-022-04238-x.
[26] J. W. Patchin and S. Hinduja, “Cyberbullying Research Center,” Feb. 16, 2024. Accessed: Feb. 02, 2025. [Online]. Available: https://cyberbullying.org/
[27] P. R. Borj, K. Raja, and P. Bours, “Online grooming detection: A comprehensive survey of child exploitation in chat logs,” Knowl Based Syst, vol. 259, 2023, doi: 10.1016/j.knosys.2022.110039.
[28] T. R. Ringenberg, K. C. Seigfried-Spellar, J. M. Rayz, and M. K. Rogers, “A scoping review of child grooming strategies: pre- and post-internet,” Child Abuse Negl, vol. 123, 2022, doi: 10.1016/j.chiabu.2021.105392.
[29] N. Lorenzo-Dus, C. Evans, and R. Mullineux-Morgan, Online Child Sexual Grooming Discourse. 2023. doi: 10.1017/9781009314626.
[30] G. M. Winters and E. L. Jeglic, Sexual Grooming: Integrating Research, Practice, Prevention, and Policy. 2022. doi: 10.1007/978-3-031-07222-2.
[31] R. Sun, “More than optimism and pessimism: investor emotions and stock returns,” University of Glasgow, 2025.
[32] M. Q. Abdullah, “Optimism/Pessimism and Its Relationship with Locus of Control Among Children and Adolescents,” Mathews Journal of Psychiatry and Mental Health, vol. 3, no. 1, 2018.
[33] A. Lobel, R. C. M. E. Engels, L. L. Stone, W. J. Burk, and I. Granic, “Video Gaming and Children’s Psychosocial Wellbeing: A Longitudinal Study,” J Youth Adolesc, vol. 46, no. 4, 2017, doi: 10.1007/s10964-017-0646-z.
[34] G. Van Houdt, C. Mosquera, and G. Nápoles, “A review on the long short-term memory model,” Artif Intell Rev, vol. 53, no. 8, 2020, doi: 10.1007/s10462-020-09838-1.
[35] X. H. Le, H. V. Ho, G. Lee, and S. Jung, “Application of Long Short-Term Memory (LSTM) neural network for flood forecasting,” Water (Switzerland), vol. 11, no. 7, 2019, doi: 10.3390/w11071387.
[36] J. P. Tan, A. L. A. Ramos, M. V. Abante, R. L. Tadeo, and R. R. Lansigan, “A Performance Review of Recurrent Neural Networks Long Short-Term Memory (LSTM),” in 2022 3rd International Conference for Emerging Technology, INCET 2022, 2022. doi: 10.1109/INCET54531.2022.9824567.
[37] M. Lewis et al., “BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension,” in Proceedings of the Annual Meeting of the Association for Computational Linguistics, 2020. doi: 10.18653/v1/2020.acl-main.703.
[38] G. Hartawan, D. Sa’adillah Maylawati, and W. Uriawan, “JIP (Jurnal Informatika Polinema) Halaman| 535 BIDIRECTIONAL AND AUTO-REGRESSIVE TRANSFORMER (BART) FOR INDONESIAN ABSTRACTIVE TEXT SUMMARIZATION”.
[39] S. Wah Tan, C. P. Lee, K. M. Lim, C. Tee, and A. Alqahtani, “QARR-FSQA: Question-Answer Replacement and Removal Pretraining Framework for Few-Shot Question”, doi: 10.1109/ACCESS.2023.1120000.
[40] H. Li, F. Wang, J. Liu, J. Huang, T. Zhang, and S. Yang, “Micro-Knowledge Embedding for Zero-shot Classification,” Computers and Electrical Engineering, vol. 101, 2022, doi: 10.1016/j.compeleceng.2022.108068.
[41] C. F. Moreno-Garcia, C. Jayne, E. Elyan, and M. Aceves-Martins, “A novel application of machine learning and zero-shot classification methods for automated abstract screening in systematic reviews,” Decision Analytics Journal, vol. 6, 2023, doi: 10.1016/j.dajour.2023.100162.
[42] M. Vogt, U. Leser, and A. Akbik, “Early detection of sexual predators in chats,” in ACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference, 2021. doi: 10.18653/v1/2021.acl-long.386.
[43] Y. Li, “Identifying Online Sexual Predators Using Support Vector Identifying Online Sexual Predators Using Support Vector Machine Machine,” School of Computer Science, 2020, doi: 10.21427/20ba-8g14.
[44] A. K. Singh, “Sentiment Analysis of Dota 2 videogame chat in context of Cyber-bullying MSc Research Project Masters of Science in Data Analytics.” [Online]. Available: https://www.vuelio.com/uk/resources/white-papers/pr-media-travel-trends-2021/