5,680 research outputs found
ML-Based User Authentication Through Mouse Dynamics
Increasing reliance on digital services and the limitations of traditional authentication methods have necessitated the development of more advanced and secure user authentication methods. For user authentication and intrusion detection, mouse dynamics, a form of behavioral biometrics, offers a promising and non-invasive method. This paper presents a comprehensive study on ML-Based User Authentication Through Mouse Dynamics.
This project proposes a novel framework integrating sophisticated techniques such as embeddings extraction using Transformer models with cutting-edge machine learning algorithms such as Recurrent Neural Networks (RNN). The project aims to accurately identify users based on their distinct mouse behavior and detect unauthorized access by utilizing the hybrid models. Using a mouse dynamics dataset, the proposed framework’s performance is evaluated, demonstrating its efficacy in accurately identifying users and detecting intrusions.
In addition, a comparative analysis with existing methodologies is provided, highlighting the enhancements made by the proposed framework. This paper contributes to the development of more secure, reliable, and user-friendly authentication systems that leverage the power of machine learning and behavioral biometrics, ultimately augmenting the privacy and security of digital services and resources
An empirical biometric-based study for user identification from different roles in the online game League of Legends
© 2017 CEUR-WS. All rights reserved. The popularity of computer games has grown exponentially in the last few years. In some games, players can choose to play with different characters from a pre-defined list, exercising distinct roles in each match. Although such games were created to promote competition and promote self-improvement, there are several recurrent issues. One that has received the least amount of attention is the problem of "account sharing" so far is when a player pays more experienced players to progressing in the game. The companies running those games tend to punish this behaviour, but this specific case is hard to identify. The aim of this study is to use a database of mouse and keystroke dynamics biometric data of League of Legends players as a case study to understand the specific characteristics a player will keep (or not) when playing different roles and distinct characters
An evolutionary computing model for the study of within-host evolution
Evolution of an individual within another individual is known as within-host dynamics (WHD). The most common modeling technique to study WHD involves ordinary differential equations (ODEs). In the field of biology, models of this kind assume, for example, that both the number of viruses and the number of mouse cells susceptible to being infected change according to their interaction as stated in the ODE model. However, viruses can undergo mutations and, consequently, evolve inside the mouse, whereas the mouse, in turn, displays evolutionary mechanisms through its immune system (e.g., clonal selection), defending against the invading virus. In this work, as the main novelty, we propose an evolutionary WHD model simulating the coexistence of an evolving invader within a host. In addition, instead of using ODEs we developed an alternative methodology consisting of the hybridization of a genetic algorithm with an artificial immune system. Aside from the model, interest in biology, and its potential clinical use, the proposed WHD model may be useful in those cases where the invader exhibits evolutionary changes, for instance, in the design of anti-virus software, intrusion detection algorithms in a corporation’s computer systems, etc. The model successfully simulates two intruder detection paradigms (i.e., humoral detection, danger detection) in which the intruder represents an evolving invader or guest (e.g., virus, computer program,) that infects a host (e.g., mouse, computer memory). The obtained results open up the possibility of simulating environments in which two entities (guest versus host) compete evolutionarily with each other when occupying the same space (e.g., organ cells, computer memory, network
Anomaly Detection over User Profiles for Intrusion Detection
Intrusion detection systems (IDS) have often been used to analyse network traffic to help network administrators quickly identify and respond to intrusions. These detection systems generally operate over the entire network, identifying “anomalies” atypical of the network’s normal collective user activities. We show that anomaly detection could also be host-based so that the normal usage patterns of an individual user could be profiled. This enables the detection of masquerading intruders by comparing a learned user profile against the current session’s profile. A prototype behavioural IDS applies the concept of anomaly detection to user behaviour and compares the effects of using multiple characteristics to profile users. Behaviour captured within the system consists of application usage, application performance (CPU and memory), the websites a user visits, the number of windows a user has open, and their typing habits. The results show that such a system is entirely feasible, that characteristics physically related to the user are more relevant to profiling behaviour and that the combination of characteristics can significantly decrease the time taken to detect an intruder
LoggerMan, a comprehensive logging and visualisation tool to capture computer usage
As we become increasingly dependent on our computers and spending a major part of our day interacting with these machines, it is becoming important for lifeloggers and human-computer interaction (HCI) researchers to capture this aspect of our life. In this paper, we present LoggerMan, a comprehensive logging tool to capture many aspects of our computer usage. It also comes with reporting capabilities to give insights to the data owner about his/her computer usage. By this work, we aim to fill the current lack of logging software in this domain, which would help us and other researchers as well to build data sets for HCI experiments and also to better understand computer usage patterns. Our tool is published online (loggerman.org) to be used freely by the community
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