Next Article in Journal
CNN-Based Fall Detection Strategy with Edge Computing Scheduling in Smart Cities
Next Article in Special Issue
Security Information Sharing in Smart Grids: Persisting Security Audits to the Blockchain
Previous Article in Journal
Enhanced Thermal Management of GaN Power Amplifier Electronics with Micro-Pin Fin Heat Sinks
Previous Article in Special Issue
An Approach for the Application of a Dynamic Multi-Class Classifier for Network Intrusion Detection Systems
 
 
Article

C3-Sex: A Conversational Agent to Detect Online Sex Offenders

1
Faculty of Computer Engineering, Escuela Colombiana de Ingeniería Julio Garavito, AK.45 No.205-59, Bogotá 111166, Colombia
2
School of Engineering, Science and Technology, Universidad del Rosario, Carrera 6 No. 12 C-16, Bogotá 111711, Colombia
3
Faculty of Computer Science, Campus de Espinardo, University of Murcia, 30100 Murcia, Spain
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(11), 1779; https://doi.org/10.3390/electronics9111779
Received: 8 August 2020 / Revised: 1 October 2020 / Accepted: 9 October 2020 / Published: 27 October 2020
(This article belongs to the Special Issue Advanced Cybersecurity Services Design)
Prevention of cybercrime is one of the missions of Law Enforcement Agencies (LEA) aiming to protect and guarantee sovereignty in the cyberspace. In this regard, online sex crimes are among the principal ones to prevent, especially those where a child is abused. The paper at hand proposes C3-Sex, a smart chatbot that uses Natural Language Processing (NLP) to interact with suspects in order to profile their interest regarding online child sexual abuse. This solution is based on our Artificial Conversational Entity (ACE) that connects to different online chat services to start a conversation. The ACE is designed using generative and rule-based models in charge of generating the posts and replies that constitute the conversation from the chatbot side. The proposed solution also includes a module to analyze the conversations performed by the chatbot and calculate a set of 25 features that describes the suspect’s behavior. After 50 days of experiments, the chatbot generated a dataset with 7199 profiling vectors with the features associated to each suspect. Afterward, we applied an unsupervised method to describe the results that differentiate three groups, which we categorize as indifferent, interested, and pervert. Exhaustive analysis is conducted to validate the applicability and advantages of our solution. View Full-Text
Keywords: chatbot; online child sexual abuse; criminal profiling; natural language processing; law enforcement agencies chatbot; online child sexual abuse; criminal profiling; natural language processing; law enforcement agencies
Show Figures

Figure 1

MDPI and ACS Style

Rodríguez, J.I.; Durán, S.R.; Díaz-López, D.; Pastor-Galindo, J.; Mármol, F.G. C3-Sex: A Conversational Agent to Detect Online Sex Offenders. Electronics 2020, 9, 1779. https://doi.org/10.3390/electronics9111779

AMA Style

Rodríguez JI, Durán SR, Díaz-López D, Pastor-Galindo J, Mármol FG. C3-Sex: A Conversational Agent to Detect Online Sex Offenders. Electronics. 2020; 9(11):1779. https://doi.org/10.3390/electronics9111779

Chicago/Turabian Style

Rodríguez, John Ibañez, Santiago Rocha Durán, Daniel Díaz-López, Javier Pastor-Galindo, and Félix Gómez Mármol. 2020. "C3-Sex: A Conversational Agent to Detect Online Sex Offenders" Electronics 9, no. 11: 1779. https://doi.org/10.3390/electronics9111779

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop