Continuous Authentication Using Touch Dynamics in Minecraft Dataset

Główne pojęcia

The author aims to demonstrate the effectiveness of touch dynamics for continuous authentication using machine learning models, particularly focusing on the dataset collected from users playing Minecraft. The core thesis is that behavioral biometrics can be utilized for robust user identification beyond traditional entry point authentication methods.

Streszczenie

The research explores continuous authentication through touch dynamics data extracted from users playing Minecraft. Machine learning models like KNN, SVC, and RF were employed to achieve accuracy rates ranging from 78% to 90%. The study contributes a novel dataset and highlights the potential of touch-based behavioral biometrics for enhancing security systems.
The content discusses the limitations of a small and homogeneous dataset, emphasizing the need for broader data collection across diverse populations and devices. Results are compared with previous studies, showcasing the efficacy of SVC in distinguishing users based on their touch behavior while playing Minecraft.

Statystyki

Our most robust model was SVC, which achieved an average accuracy of approximately 90%
Accuracy test results: KNN (78%), SVC (90%), RF (99%)
Model evaluation results: KNN (AUC = 93.4%, Precision = 88%, F1-Score = 88%), SVC (AUC ~0.9693), RF (AUC = 0.998)

Cytaty

"No hyperparameter tuning was necessary with our data and model."
"The choice of Minecraft as the application for data collection proves strategic."
"Our decision to make the touchscreen data publicly available aims to foster collaboration."

Kluczowe wnioski z

Your device may know you better than you know yourself -- continuous authentication on novel dataset using machine learning

by Pedro Gomes ... o arxiv.org 03-07-2024

https://arxiv.org/pdf/2403.03832.pdf

Głębsze pytania

How can continuous authentication systems be improved beyond touch dynamics?

Continuous authentication systems can be enhanced by incorporating multiple factors for user identification beyond touch dynamics. One approach is to implement multi-modal biometric authentication, combining touch dynamics with other biometric modalities such as facial recognition, voice recognition, iris scanning, or fingerprint scanning. This multi-factor authentication increases the security of the system by requiring multiple forms of verification from the user.
Another improvement could involve integrating contextual information into the authentication process. Contextual factors like location data, device usage patterns, time of access, and network behavior can provide additional layers of security in verifying a user's identity. By analyzing these contextual cues alongside touch dynamics, continuous authentication systems can better adapt to changing circumstances and detect anomalies that may indicate unauthorized access.
Furthermore, advancements in artificial intelligence and machine learning algorithms can enable continuous authentication systems to learn and adapt over time based on user behavior patterns. By leveraging advanced AI techniques like anomaly detection and pattern recognition, these systems can continuously refine their models for identifying legitimate users while detecting suspicious activities more effectively.

What are potential drawbacks of relying solely on machine learning models for user identification?

While machine learning models offer significant advantages in user identification for continuous authentication systems, there are several potential drawbacks to relying solely on them:

Vulnerability to Adversarial Attacks: Machine learning models are susceptible to adversarial attacks where malicious actors manipulate input data to deceive the model into making incorrect decisions. Without robust defenses against such attacks, the integrity of the user identification process could be compromised.

Overfitting: Overfitting occurs when a machine learning model performs well on training data but fails to generalize effectively to unseen data. Relying solely on ML models without proper regularization techniques or validation strategies may lead to overfitting issues that reduce the accuracy and reliability of user identification.

Bias and Fairness Concerns: Machine learning algorithms trained on biased datasets may perpetuate existing biases or discrimination in user identification processes. If not carefully monitored and mitigated through bias detection methods or fairness-aware algorithms, these biases could result in unfair treatment towards certain groups of users.

Interpretability Challenges: Some complex machine learning models lack interpretability which makes it difficult for stakeholders to understand how decisions are made during the user identification process. Lack of transparency in ML models hinders trust-building efforts among users who seek explanations for why certain actions were classified as authentic or fraudulent.

Data Privacy Risks: Relying solely on machine learning models requires extensive collection and storage of sensitive biometric data for training purposes which raises concerns about privacy protection and compliance with regulations such as GDPR (General Data Protection Regulation). Inadequate safeguards around data handling could expose users' personal information to security breaches or misuse.

How might advancements in touchscreen authentication impact cybersecurity practices?

Advancements in touchscreen authentication have significant implications for cybersecurity practices:
1- Enhanced Security: Touchscreen-based behavioral biometrics offer an additional layer of security compared to traditional password-based methods since they rely on unique physical characteristics like finger pressure patterns rather than easily replicable passwords.
2- Improved User Experience: Touchscreen-based continuous authentication provides a seamless experience for users by eliminating repetitive password entries while maintaining high levels of security.
3- Reduced Fraudulent Activities: Advanced touchscreen technologies combined with machine-learning-powered analytics help detect unusual behaviors during interactions with devices leadingto early detectionand preventionof fraudulent activities.
4- Compliance Requirements: Organizations dealing with sensitive information must adhere tocertain regulatory standards regardinguserauthentication.Touchscreenauthenticationmethods that meet stringentsecurity requirementscan help organizationscomplywithregulationslikeGDPRorHIPAA.
5- -Cybersecurity Awareness: The adoptionof innovative touchscreenauthentication solutionsraisesawarenessaboutcybersecuritybestpracticesamongusersandorganizations.Encouragingtheuseofsecureauthenticationmethodscanhelpinbuildingastrongerdefenseagainstcyberthreats.
These advancements underscorethe importanceofcontinuousinnovationinuserauthenticationsystemstoaddressevolvingcybersecuritychallengesandsafeguardcriticalinformationfrommaliciousactorswhileenhancingusere xperienceandprivacyprotectionstandards