Related papers: A novel non-linear transformation based multi-user…

Community identification algorithms have been used to enhance the quality of the services perceived by its users. Although algorithms for community have a widespread use in the Web, their application to portals or specific subsets of the…

Biometrics technologies are gaining popularity today since they provide more reliable and efficient means of authentication and verification. Keystroke Dynamics is one of the famous biometric technologies, which will try to identify the…

Identifying user's identity is a key problem in many data mining applications, such as product recommendation, customized content delivery and criminal identification. Given a set of accounts from the same or different social network…

We investigate an efficient context-dependent clustering technique for recommender systems based on exploration-exploitation strategies through multi-armed bandits over multiple users. Our algorithm dynamically groups users based on their…

Recent studies have shown how motion-based biometrics can be used as a form of user authentication and identification without requiring any human cooperation. This category of behavioural biometrics deals with the features we learn in our…

The goal of this article is twofold. Firstly, nonlinear system identification is introduced to a wide audience, guiding practicing engineers and newcomers in the field to a sound solution of their data driven modeling problems for nonlinear…

Most users of online services have unique behavioral or usage patterns. These behavioral patterns can be exploited to identify and track users by using only the observed patterns in the behavior. We study the task of identifying users from…

In random-access communication systems, the number of active users varies with time, and has considerable bearing on receiver's performance. Thus, techniques aimed at identifying not only the information transmitted, but also that number,…

Different users can use a given Internet application in many different ways. The ability to record detailed event logs of user in-application activity allows us to discover ways in which the application is being used. This enables…

In real life, mostly problems are dynamic. Many algorithms have been proposed to handle the static problems, but these algorithms do not handle or poorly handle the dynamic environment problems. Although, many algorithms have been proposed…

Authenticated lateral movement via compromised accounts is a common adversarial maneuver that is challenging to discover with signature- or rules-based intrusion detection systems. In this work a behavior-based approach to detecting…

Modern computing systems inherently trust human input devices, creating an exploitable attack surface for adversarial automation. USB Human Interface Device (HID) emulation attacks, such as those enabled by the USB Rubber Ducky, exploit…

Identification and authentication are two basic functionalities of traditional random access protocols. In ALOHA-based random access, the packets usually include a field with a unique user address. However, when the number of users is…

Global models are trained to be as generalizable as possible, with user invariance considered desirable since the models are shared across multitudes of users. As such, these models are often unable to produce personalized responses for…

We address the problem of cluster identity estimation in a hierarchical federated learning setting in which users work toward learning different tasks. To overcome the challenge of task heterogeneity, users need to be grouped in a way such…

Neural population activity often exhibits regime-dependent non-stationarity in the form of switching dynamics. Learning accurate switching dynamical system models can reveal how behavior is encoded in neural activity. Existing switching…

We address the problem of learning linear system models from observing multiple trajectories from different system dynamics. This framework encompasses a collaborative scenario where several systems seeking to estimate their dynamics are…

Risk stratification is a key tool in clinical decision-making, yet current approaches often fail to translate sophisticated survival analysis into actionable clinical criteria. We present a novel method for unsupervised machine learning…

Keystroke timing based active authentication systems are conceptually attractive because: (i) they use the keyboard as the sensor and are not hardware-cost prohibitive, and (ii) they use the keystrokes generated from normal usage of…

This study presents a method, along with its algorithmic and computational framework implementation, and performance verification for dynamical system identification. The approach incorporates insights from phase space structures, such as…