Name
Abstract | ||
|---|---|---|
Terrorist Networks (TNs) and Organized Crime (OC) are nowadays an increasing threat in the modern society. Due to the strong adoption of the IT technology, an emergent phenomenon is represented by the exploitation of social media, such as Twitter, Facebook, YouTube to disseminate and promote illegal activities, recruit terrorists and establish collaborations. The traditional approaches and countermeasures against cyber-crimes result inadequate in the cyber-space. In this context, the paper proposes an engineering method, centered on three main phases, to support the analysis of suspicious users on social media related to OC and TNs. It is based on the exploitation and extension of social network analysis approaches combined with well-known clustering techniques and association rules. It aims to identify similarities as well as groups of users associated to specific illegal activities such as drugs, weapons and human trafficking. Moreover, it supports the identification process of leaders in groups and mediators between them. A software, which enables the automatic execution of the proposed method, is developed and experimented on the Twitter social media. The results show both the identification of groups of users related to OC and TNs along with their intra-group activities as well as inter-group relationships through potential mediators. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICCCN.2019.8847028 | 2019 28th International Conference on Computer Communication and Networks (ICCCN) |
Keywords | DocType | ISSN |
similarity analysis,criminals,social networks,strong adoption,emergent phenomenon,recruit terrorists,cyber-crimes,cyber-space,engineering method,suspicious users,social network analysis,association rules,specific illegal activities,mediators,Twitter social media,intra-group activities,terrorist networks,organized crime,TN | Conference | 1095-2055 |
ISBN | Citations | PageRank |
978-1-7281-1857-4 | 0 | 0.34 |
References | Authors | |
17 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrea Tundis | 1 | 47 | 14.38 |
| Archit Jain | 2 | 1 | 0.69 |
| Gaurav Bhatia | 3 | 1 | 0.69 |
| Max Mühlhäuser | 4 | 1652 | 252.87 |