A Review of Real-Time Risk Detection for Adolescent Cybersecurity: Technological Advances, Challenges, and Prospects

Abstract

Amid the wave of digitization, the internet has become a core context for adolescents’ lives and learning. The scale of underage netizens in China has reached 193 million, with an internet penetration rate of 97.2%, essentially achieving saturated coverage. The prevalence of short-form video platforms and smart devices has rendered online risks more concealed, prevalent among younger children, and cross-contextual. Reports indicate that 34.7% of adolescents have encountered cyberbullying, and 28.3% have been exposed to harmful information, urgently necessitating the construction of precise and efficient real-time risk detection systems. This study aims to systematically review the research, core technologies, and existing bottlenecks in the field of real-time risk detection for adolescent cybersecurity, providing references for theoretical deepening and practical implementation. The research method adopts a literature review, conducting in-depth analysis around the sample characteristics, technical methods, and governance mechanisms in domestic and international research. The findings indicate that the field has formed a “technology development-governance coordination-literacy cultivation” framework. Traditional technologies suffer from imbalances between timeliness and accuracy. Dual frameworks combining “reinforcement learning + deep learning” and multimodal fusion technologies offer effective paths to resolve core contradictions, yet research gaps remain in areas such as adaptation for younger age groups, cross-context migration, and human-computer collaboration. The conclusion points out that future efforts need to focus on technological innovation, scenario expansion, and governance coordination to build a refined and intelligent protection system.