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 suspect’s behavior. After 50 days of experiments, the chatbot generated a dataset with 7199 of profiling vectors with the features associated to each suspect. Afterwards, we applied an unsupervised method to describe the results that differentiates three groups, which we categorize as indifferent, interested, and pervert. Exhaustive analysis is conducted to validate the applicability and advantages of our solution.

This work would not have been possible without the inestimable contribution of:

• John Ibáñez Rodríguez
• Santiago Rocha Durán
• Daniel O. Díaz López
• Javier Pastor Galindo

John Ibáñez Rodríguez, Santiago Rocha Durán, Daniel O Díaz López, Javier Pastor Galindo, Félix Gómez Mármol, «C3-Sex: A conversational agent to detect online sex offenders«, Special Issue on Advanced Cybersecurity Services Design, Electronics, vol. 9, no. 11, 2020

• Journal: Electronics
• Category: Engineering, Electrical & Electronic
• Rank: 145/273
• Quartile: Q3
• Impact Factor: 2.397