Deep Learning and Machine Learning, Better Together Than Apart: A Review on Biometrics Mobile Authentication

Kokal, Sara; Vanamala, Mounika; Dave, Rushit

doi:10.3390/jcp3020013

Cited by 6 publications

(3 citation statements)

References 98 publications

(259 reference statements)

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“…Subsequently, a machine learning classifier receives these features and uses their interpretation to decide whether the current user is authorized or an intruder. A machine learning classifier can be constructed for a system in several ways [11].…”

Section: Non-intrusive User Experiencementioning

confidence: 99%

Using Behavioural Biometrics and Machine Learning in Smart Gadgets for Continuous User Authentication Purposes

Balachandra,

K V

et al. 2024

JMC

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In the ever-evolving realm of technology, the identification of human activities using intelligent devices such as smartwatches, fitness bands, and smartphones has emerged as a crucial area of study. These devices, equipped with inertial sensors, gather a wealth of data and provide insights into users' movements and behaviors. These data not only serve practical purposes, but also hold significant implications for domains such as healthcare and fitness tracking. Traditionally, these devices have been employed to monitor various health metrics such as step counts, calorie expenditure, and real-time blood pressure monitoring. However, recent research has shifted its focus to leveraging the data collected by these sensors for user authentication purposes. This innovative approach involves the utilization of Machine Learning (ML) models to analyze the routine data captured by sensors in smart devices employing ML algorithms, which can recognize and authenticate users based on their unique movement patterns and behaviors. This introduces a paradigm shift from traditional one-time authentication methods to continuous authentication, adding an extra layer of security to protect users against potential threats. Continuous authentication offers several advantages over its conventional counterparts. First, it enhances security by constantly verifying a user's identity through their interaction with the device, thereby mitigating the risk of unauthorized access. Second, it provides a seamless and nonintrusive user experience, eliminating the need for repetitive authentication prompts. Moreover, it offers robust protection against various threats such as identity theft, unauthorized access, and device tampering. The application of continuous authentication extends beyond individual devices and encompasses interconnected systems and networks. This holistic approach ensures a comprehensive security across digital platforms and services. The experiments demonstrate that the logistic regression model achieves an accuracy of 82.32% on the test dataset, highlighting its robustness for binary classification tasks. Additionally, the random forest model outperforms with a 92.18% accuracy, emphasizing its superior capability in handling complex feature interactions. In the study, the sequential neural network achieved an accuracy of 92% on the HAR dataset, outperforming traditional machine learning models by a significant margin. The model also demonstrated robust generalization capabilities with a minimal drop in performance across various cross-validation folds.

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Section: Non-intrusive User Experiencementioning

confidence: 99%

Using Behavioural Biometrics and Machine Learning in Smart Gadgets for Continuous User Authentication Purposes

Balachandra,

K V

et al. 2024

JMC

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“…This lack of cybersecurity professionals is not limited to the government [16]. Additionally, automated systems such as authentication using machine learning are not enough to make up for this discrepancy [17], [18], [19]. Thus, considering the evident lack of cybersecurity professionals, it would be prudent for individuals to learn to protect themselves.…”

Section: Literature Reviewmentioning

confidence: 99%

Secure Arcade: A Gamified Defense Against Cyber Attacks

Loesch,

Hrastich,

Herbert

et al. 2023

IJCSIT

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In modernity, we continually receive increasingly intricate technologies that allow us to increase our lives’ convenience and efficiency. Our technology, particularly technology available over the internet, is advancing at unprecedented speed. However, this speed of advancement allows those behind malicious attacks to have an increasingly easier time taking advantage of those who know little about computer security. Unfortunately, education in the computer security field is generally limited only to tertiary education. This research addresses this problem through a gamified web-based application that drives users to reach learning goals to help them become more vigilant internet users: 1. Learn and memorize general computer security terminology, 2. Become familiar with basic cryptography concepts, 3. Learn to recognize potential phishing scams via email quickly, and 4. Learn common attacks on servers and how to deal with them.

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“…It also outperforms them when used on known faces, with accuracy reaching 98.24%. One limitation is that the identity of the face must be known for detection [9]. This research also presents a new deepfake and real video dataset, Vox-DeepFake.…”

Section: Identity-focusedmentioning

confidence: 99%

Untitled

2023

IJCSIT

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A deepfake is a photo or video of a person whose image has been digitally altered or partially replaced with an image of someone else. Deepfakes have the potential to cause a variety of problems and are often used maliciously. A common usage is altering videos of prominent political figures and celebrities. These deepfakes can portray them making offensive, problematic, and/or untrue statements. Current deepfakes can be very realistic, and when used in this way, can spread panic and even influence elections and political opinions. There are many deepfake detection strategies currently in use but finding the most comprehensive and universal method is critical. So, in this survey we will address the problems of malicious deepfake creation and the lack of universal deepfake detection methods. Our objective is to survey and analyze a variety of current methods and advances in the field of deepfake detection.

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Deep Learning and Machine Learning, Better Together Than Apart: A Review on Biometrics Mobile Authentication

Cited by 6 publications

References 98 publications

Using Behavioural Biometrics and Machine Learning in Smart Gadgets for Continuous User Authentication Purposes

Using Behavioural Biometrics and Machine Learning in Smart Gadgets for Continuous User Authentication Purposes

Secure Arcade: A Gamified Defense Against Cyber Attacks

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