查看文章

The increasing growth of illegal online activities in the so-called dark web—that is, the hidden collective of internet sites only accessible by a specialized web browsers—has challenged law enforcement agencies in recent years with sparse research efforts to help. For example, research has been devoted to supporting law enforcement by employing Natural Language Processing (NLP) to detect illegal activities on the dark web and build models for their classification. However, current approaches strongly rely upon the linguistic characteristics used to train the models, e.g., language semantics, which threatens their generalizability. To overcome this limitation, we tackle the problem of predicting illegal and criminal activities—a process defined as threat intelligence—on the dark web from a complementary perspective—that of dark web code maintenance and evolution— and propose a novel approach that uses …