Hybrid Text-based Deception Models for Native and Non-Native English Cybercriminal Networks
Alex V. Mbaziira, James H. Jones · 2017
Cybercriminals are increasingly using Internet messaging to exploit their victims. We develop and apply a text-based deception detection approach to build hybrid models for detecting cybercrime in the text Internet communications from native and non-native English speaking cybercriminal networks, where our models use both computational linguistics (CL) and psycholinguistic (PL) features. We study four types of deception-based cybercrime: fraud, scam, favorable fake reviews, and unfavorable fake reviews. We build two types of generalized hybrid models for both native and non-native English speaking cybercriminal networks: 2-dataset and 3-dataset hybrid models using Naïve Bayes, Support Vector Machines, and kth Nearest Neighbor algorithms. All 2-dataset models are trained on any two forms of cybercrime in different web genres, which are then used to detect and analyze other types of cybercrime in web genres that were not part of the training set to establish model generalizability. Similarly, the 3-dataset models are trained on any three forms of cybercrime in different web genres, that are also used to detect and analyze cybercrime in a web genre that was not part of the training set. Model performance on the test datasets ranges from 60% to 80% accuracy, with the best performance on detection of unfavorable reviews and fraud, and notable differences emerged between detection in messages from native and non-native English speaking groups. Our work may be applied as provider- or user-based filtering tools to identify cybercriminal actors and block or label undesirable messages before they reach their intended targets.