Grey Wolf-Based Method for an Implicit Authentication of Smartphone Users

Smartphones have now become an integral part of our everyday lives. User authentication on smartphones is often accomplished by mechanisms (like face unlock, pattern, or pin password) that authenticate the user’s identity. These technologies are simple, inexpensive, and fast for repeated logins. However, these technologies are still subject to assaults like smudge assaults and shoulder surfing. Users’ touch behavior while using their cell phones might be used to authenticate them, which would solve the problem. The performance of the authentication process may be influenced by the attributes chosen (from these behaviors). The purpose of this study is to present an effective authentication technique that implicitly offers a better authentication method for smartphone usage while avoiding the cost of a particular device and considering the constrained capabilities of smartphones. We began by concentrating on feature selection methods utilizing the grey wolf optimization strategy. The random forest classifier is used to evaluate these tactics. The testing findings demonstrated that the grey wolf-based methodology works as a better optimum feature selection for building an implicit authentication mechanism for the smartphone environment when using a public dataset. It achieved a 97.89% accuracy rate while utilizing just 16 of the 53 characteristics like utilizing minimum mobile resources mainly; processing power of the device and memory to validate individuals. Simultaneously, the findings revealed that our approach has a lower equal error rate (EER) of 0.5104, a false acceptance rate (FAR) of 1.00, and a false rejection rate (FRR) of 0.0209 compared to the methods discussed in the literature. These promising results will be used to create a mobile application that enables implicit validation of authorized users yet avoids current identification concerns and requires fewer mobile resources.