Evaluation metrics for classification.

The integration of mobile devices into adolescents’ daily lives is significant, making it imperative to prioritize their safety and security. With the imminent arrival of fast internet (6G), offering increased bandwidth and reduced latency compared to its predecessor (5G), real-time streaming of high-quality video and audio to mobile devices will become feasible. To effectively leverage the fast internet, accurately classifying Mobile Applications (M-APPs) is crucial to shield adolescents from inappropriate content, including violent videos, pornography, hate speech, and cyberbullying. This work introduces an innovative approach utilizing Deep Learning techniques, specifically Attentional Convolutional Neural Networks (A-CNNs), for classifying M-APPs. The goal is to secure adolescent mobile usage by predicting the potential negative impact of M-APPs on adolescents. The proposed methodology employs multiple Machine and Deep Learning (M/DL) models, but A-CNNs based on Bidirectional Encoder Representations from Transformers embeddings outperformed other models, achieving an average accuracy of 88.74% and improving the recall from 99.33% to 99.65%.

移动设备融入青少年日常生活的程度日益加深，保障青少年移动使用安全已成为当务之急。随着高速互联网（6G）的临近落地，相较于其前代技术（5G），6G具备更高带宽与更低时延，将使得向移动设备实时传输高质量音视频流媒体成为可能。为有效利用这一高速互联网技术，精准分类移动应用（Mobile Applications, M-APPs）至关重要，此举可帮助青少年免受暴力视频、色情内容、仇恨言论以及网络欺凌等不当内容的侵害。本研究提出一种创新性方案，借助深度学习技术——尤其是注意力卷积神经网络（Attentional Convolutional Neural Networks, A-CNNs）——开展移动应用分类任务。其核心目标为：通过预测移动应用对青少年可能造成的负面影响，保障青少年的移动设备使用安全。本研究提出的方法对比了多种机器学习与深度学习（Machine and Deep Learning, M/DL）模型，其中基于Transformer双向编码器表征（Bidirectional Encoder Representations from Transformers）嵌入的A-CNNs模型性能最优，平均准确率达88.74%，召回率从99.33%提升至99.65%。