Generating useful and meaningful knowledge out of personal bigdata is a difficult task that presents multiple challenges due to the intrinsic characteristics of these type of data, namely their volume, velocity, variety and noisiness 🤖
This talk presents an interdisciplinary approach for solving this problem, based on the idea that the user and the world surrounding him can be modelled, defining most of the elements of her context as entities (locations, people, objects) in addition with their attributes and the relations among them 🤝 This allows to create a structure out of the unstructured, noisy and highly variable sensor data that can then be used by the machine to provide personalized, context-aware services to the final user with the final goal of improving her quality of life.
User identification is paramount to guarantee the security of a web system. However, classical information to identify users (e.g. user credentials or browsing history) are not reliable enough 👥 Biometrics markers (e.g. keystroke patterns), on the other hand, represent a viable reference to correctly identify users. These data can be easily collected - no disruptive change in user experience - and can be derived from users’ interaction with the system 🤳
In this talk I will present how Machine and Deep Learning techniques can be effectively used to learn unique identification markers from keystroke behavioural patterns of users aiming to prevent user-account hijacking frauds