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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63800
Volume:
10 |
5 |
May 2026
Pages:
1-6
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
MASTER OF COMPUTER APPLICATIONS
BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY
MASTER OF COMPUTER APPLICATIONS
BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY
MASTER OF COMPUTER APPLICATIONS
BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY
A machine learning-based system called the Intelligent Model for Teen E-Safety Prediction was created to assess teens online activity and forecast their cybersecurity risk levels. Teens are more susceptible to cybersecurity risks like phishing cyberbullying data breaches and exposure to harmful content as they rely more and more education and entertainment. Conventional tools such as parental controls and antivirus software are unable to evaluate behavioral patterns or offer tailored safety insights. In order to close this gap the suggested system uses sophisticated algorithms like Random Forest XGBoost and LightGBM in conjunction with reliable preprocessing methods to categorize teens cybersecurity behavior according to characteristics like browsing patterns social media usage device patterns password strength and exposure to online threats. High prediction accuracy is guaranteed by this data-driven strategy which also encourages young users to adopt safer online practices. The system incorporates a full web-based platform with administrative and user features predictive analytics.Trustworthy advice administrators can maintain legal aid information pertaining to cyber issues update FAQs and manage user accounts. After registering and logging in users can view educational materials to increase their digital awareness and receive real-time predictions about their cybersecurity behavior.
KEYWORDS
To evaluate cybersecurity risks among teenagers the Intelligent Teen E-Safety Prediction System effectively combines machine-learning techniques with behavioral data. By using sophisticated models and preprocessing pipelines the system guarantees precise and significant forecasts. Because of its user-friendly design the platform can be effectively used by administrators and teenagers. The system promotes responsible online behavior and raises awareness of cyber risks by offering practical insights into online safety practices. All things considered the project shows how artificial intelligence can be effectively used to improve cyber safety in a contemporary digital environment
Ø A Deep Learning Framework for Teenage Cybersecurity Awareness Using Real- World E- Safety Data; Authors: Shu Xu, Song Peng, Jie An; Publication: Informatica Vol. 49, No. 18 (2025); DOI: 10.31449/inf.v49i18.7861 Ø Cybersecurity awareness for children: A systematic literature review; Authors: F Quayyum, D.S. Cruzes, L. Jaccheri; Publication: International Journal of Child-Computer Interaction, Vol 30 (2021); DOI: 10.1016/j.ijcci.2021.100343; E-ISSN: 2212- 8689. Ø Online Safety for Children and Youth under the 4Cs Framework — A Focus on Digital Policies in Australia, Canada, and the UK; Authors: Yujin Jang, Bomin Ko; Publication: Children (Basel), Vol 10 Issue 8 (2023); DOI: 10.3390/children10081415; E-ISSN: 2227- 9067. Ø Improving Children’s E-Safety Skills through an Interactive Learning Environment: A Quasi-Experimental Study; Authors: Iolie Nicolaidou & Agnes Venizelou; Publication: Multimodal Technologies and Interaction, Vol 4, Issue 2 (2020); DOI: 10.3390/mti4020010. Ø YouthSafe: A Youth-Centric Safety Benchmark and Safeguard Model for Large Language Models; Authors: Yaman Yu, Yiren Liu, Jacky Zhang, Yun Huang, Yang Wang; Publication: arXiv preprint arXiv:2509.08997v1 (2025); DOI/identifier: arXiv:2509.08997v1
Publication Date
5/22/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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