Name
Abstract | ||
|---|---|---|
With the development of Internet and communication technologies, various information can easily spread to children via applications (Apps) on Internet-of-Things (IoT) devices (e.g., emerging smart toys, watches, and phones), especially, the Apps on smart phones based on Android. While greatly bringing up convenience for children's lives and studies, these Apps also make illegal and inappropriate contents (such as violence, pornography, gambling, and drug) more accessible to kids, which is harmful to minors' growth. To keep children away from inappropriate contents in applications, previous researches mainly focused on detecting unsuitable videos and advertisements in children applications or designing App maturity rating methods and parental control software. There are few literature that specially investigate the inspection of inappropriate contents in children Android Apps. Toward this end, we propose a novel automatic content inspection and the forensics framework to identify children Android Apps which are not proper for kids under 12. In addition, this framework offers evidence to make users understand why the inspected App is judged as unsuitable. In experiments, we apply this framework on some specially chosen Android Apps which distinctly include inappropriate contents to verify its performance. The results show that it can successfully identify those applications with high precision that reaches 85.7%. Besides, by analyzing the collected children's Android Apps through our framework, we find that 40% of them are identified to be improper, which illustrates the serious issue of unsuitable children Android Apps. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/JIOT.2020.2982248 | IEEE Internet of Things Journal |
Keywords | DocType | Volume |
Children Android applications (Apps),content forensics,content inspection,inappropriate contents,Internet of Things (IoT),maturity rating | Journal | 7 |
Issue | ISSN | Citations |
8 | 2327-4662 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qian Luo | 1 | 3 | 2.74 |
| Jiajia Liu | 2 | 140 | 11.42 |
| Jiadai Wang | 3 | 72 | 3.38 |
| Yawen Tan | 4 | 0 | 0.34 |
| Yurui Cao | 5 | 13 | 0.79 |
| Nei Kato | 6 | 3982 | 263.66 |