Research has found that human’s deceptive behaviors would affect their keystroke patterns. Detecting deceptive behaviors through keystroke patterns is a critical step toward building a cyber information security system in the field of social networking. However, the existing models detecting deceptive behaviors still suffered from the problems of high invasion and low real-time performance. To solve the problems, we first designed an experiment to collect a wide range of stroke features (i.e., single-key features, content features and double-key features) from users’ typing process of short text and then developed a predictive model to detect the deceptive behaviors by using Genetic Algorithms (GAs) and Support Vector Machines (SVMs) as feature selection and model building methods, respectively. The results showed that the developed model could effectively detect the deceptive behaviors with accuracy of 82.86%; all the three categories of keystroke features had contributions to detecting deceptive behaviors. In addition, the effects of cognitive workload and pressure on keystroke pattern of deceivers had also been explored.
