1,721,454 research outputs found
CLAPMETRICS: Decoding Users’ Gender and Age Through Smartwatch Gesture Dynamics
No full textSmartwatches offer a unique platform for the noninvasive
estimation of soft biometric attributes such as age and
gender. This capability is crucial for advancing personalized
healthcare, enhancing security through behavioral biometrics,
and refining user interfaces for improved interactions. Although
traditional biometrics, such as face, have been well-utilized,
smartwatch sensors offer a fresh avenue for biometric data
collection that has not yet been explored, presenting exciting
possibilities for advancements in wearable devices. In this paper,
we explore the viability of estimating gender and age from
smartwatch sensory data. Specifically, we propose a method that
leverages the natural arm-movements produced from clapping
actions, detected by the smartwatch’s sensors, combined with a
Deep Neural Network, to predict these personal attributes. Our
tests with clap-generated micro movements, collected using our
developed customized application, affirm the effectiveness of our
proposed method. Precisely, we report an accuracy of 98.77%
and 99.44% for gender and age estimation, respectively
2IN1: A Bimodal Behavioral Biometric-based User Authentication Scheme for Smartphones
Get PDFThis paper introduces a bi-modal mechanism that leverages the way a smartphone user signs on the touchscreen and taps/enters any ”text-independent” 8-digit numbers to authenticate their identity. Pre- cisely, by extracting the trajectory of touch-points and touch-timing features during the enrollment stage, our scheme creates a digital identity of a user based on these behaviors. In the verification stage, our scheme compares the captured touch-points and touch-timing signatures with the digital identity of the user created during the enrollment stage. If the captured signatures match the digital identity within a certain tolerance, the user is authenticated. The choice of low-level events, such as signing on the screen and touch-typing, as biometric modalities makes our scheme easier to implement and adapt. We evaluated our approach using multiple classifiers, i.e., K-Nearest Neighbor, Support Vector Machine, and Deep Neural Network, and achieved a high True Acceptance Rate of 97.1% with a low False Acceptance Rate of just 0.2%, and an accuracy of 98.45% on a dataset of 20 volunteers. These results prove our scheme accurate in verifying the identity of users while also maintaining a low rate of false acceptance of unauthorized users
ANSWERAUTH: A bimodal behavioral biometric-based user authentication scheme for smartphones
No full text© 2018 Elsevier Ltd In this paper, we present a behavioral biometric-based smartphone user authentication mechanism, namely, ANSWERAUTH, which relies on the very common users’ behavior. Behavior, here, refers to the way a user slides the lock button on the screen, to unlock the phone, and brings the phone towards her ear. The authentication mechanism works with the biometric behavior based on the extracted features from the data recorded using the built-in smartphone sensors, i.e., accelerometer, gyroscope, gravity, magnetometer and touchscreen, while the user performed sliding and phone-lifting actions. We tested ANSWERAUTH on a dataset of 10,200 behavioral patterns collected from 85 users while they performed the unlocking actions, in sitting, standing, and walking postures, using six state-of-the-art conceptually different machine learning classifiers in two settings, i.e., with and without simultaneous feature selection and classification. Among all the chosen classifiers, Random Forest (RF) classifier proved to be the most consistent and accurate classifier on both full and reduced features and provided a True Acceptance Rate (TAR) as high as 99.35%. We prototype proof-of-the-concept Android app, based on our findings, and evaluate it in terms of security and usability. Security analysis of ANSWERAUTH confirms its robustness against the possible mimicry attacks. Similarly, the usability study based on Software Usability Scale (SUS)1 questionnaire verifies the user-friendliness of the proposed scheme (SUS Score of 75.11). Experimental results prove ANSWERAUTH as a secure and usable authentication mechanism.sponsorship: The work was partially supported by the EIT Digital project: Android App Reputation Service (ARTS), by the European Training Network for CyberSecurity (NeCS) grant number 675320 and by the project XProbes funded by the Provincia Autonoma di Trento. Mauro Conti is supported by a Marie Curie Fellowship funded by the European Commission (agreement PCIG11-GA-2012-321980). This work is also partially supported by the EU Tag-ItSmart! Project (agreement H2020-ICT30-2015-688061), the EU-India REACH Project (agreement ICI+/2014/342-896), by the project CNR-MOST/Taiwan 2016-17 "Verifiable Data Structure Streaming", the grant n. 2017-166478 (3696) from Cisco University Research Program Fund and Silicon Valley Community Foundation, and by the grant "Scalable IoT Management and Key security aspects in 5G systems" from Intel. (EIT Digital project: Android App Reputation Service (ARTS), European Training Network for CyberSecurity (NeCS)|675320, project XProbes - Provincia Autonoma di Trento, Marie Curie Fellowship - European Commission|PCIG11-GA-2012-321980, EU Tag-ItSmart! Project|H2020-ICT30-2015-688061, EU-India REACH Project|ICI+/2014/342-896, project CNR-MOST/Taiwan 2016-17 "Verifiable Data Structure Streaming", Cisco University Research Program Fund|2017-166478 (3696), Silicon Valley Community Foundation, grant "Scalable IoT Management and Key security aspects in 5G systems" from Intel)status: Publishe
Balancing the Scales: Using GANs and Class Balance for Superior Malware Detection
No full textEnsuring the security of a network infrastructure necessitates the
precise detection and categorization of malware. While existing
methodologies have demonstrated higher accuracy, their effective-
ness has predominantly been validated on a limited subset of mal-
ware families or samples. These analyses often focus on malware
families with a higher number of samples, potentially leading to bi-
ased and unrepresentative classification results. To address this gap,
our study aims to enhance the accuracy and robustness of malware
detection and categorization systems by investigating the impact
of dataset size, class balance, and data augmentation techniques
on classifier performance. We demonstrate the efficacy of our ap-
proach on a comparatively larger dataset titled Blue Hexagon Open
Dataset for Malware AnalysiS, comprising of 134k samples. Our
analysis, exploiting 85 malware families with at least 50 samples
each, results in the highest accuracy of 92.28% using Random Forest
as the classifier on the original imbalanced dataset. However, by
employing Generative Adversarial Networks to generate synthetic
samples and achieve balanced class distributions (resulted in bal-
anced datasets), our approach demonstrates the improvement in
the classifier’s accuracy to 99.35%
A chimerical dataset combining physiological and behavioral biometric traits for reliable user authentication on smart devices and ecosystems
Get PDFWe present a chimerical dataset that combines both physiological and behavioral biometric traits, for reliable user authentication on smart devices and ecosystems [1]. The data are composed of statistical features computed from swipe-gesture, voice-prints, and face-images. The swipe and voice-prints data presented herein after are collected using a customized Android application -DriverAuth, however, the face data is obtained from the MOBIO Dataset [2].
We collected 10,320 swipe and voice-prints samples from 86 users worldwide by collaborating with a professional crowd-sourcing platform and formed a chimerical dataset adjunct to the publicly available MOBIO dataset with our collected dataset. The dataset consists of various statistical features computed from the raw data
for all three traits, i.e., swipe, voice-print, and face
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
The Next-Gen Interactive Runtime Simulator for Neural Network Programming
No full textUllah S, Attaullah H, Jungeblut T. The Next-Gen Interactive Runtime Simulator for Neural Network Programming. In: Söderberg E, Church L, eds. Companion Proceedings of the 8th International Conference on the Art, Science, and Engineering of Programming. New York, NY, USA: ACM; 2024: 8-10
A Real-Time Hybrid Approach to Combat In-Browser Cryptojacking Malware
Get PDFCryptojacking is a type of computer piracy in which a hacker uses a victim’s computer resources, without their knowledge or consent, to mine for cryptocurrency. This is made possible by new memory-based cryptomining techniques and the growth of new web technologies such as WebAssembly, allowing mining to occur within a browser. Most of the research in the field of cryptojacking has focused on detection methods rather than prevention methods. Some of the detection methods proposed in the literature include using static and dynamic features of in-browser cryptojacking malware, along with machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), and others. However, these methods can be effective in detecting known cryptojacking malware, but they may not be able to detect new or unknown variants. The existing prevention methods are shown to be effective only against web-assembly (WASM)-based cryptojacking malware and cannot handle mining service-providing scripts that use non-WASM modules. This paper proposes a novel hybrid approach for detecting and preventing web-based cryptojacking. The proposed approach performs the real-time detection and prevention of in-browser cryptojacking malware, using the blacklisting technique and statistical code analysis to identify unique features of non-WASM cryptojacking malware. The experimental results show positive performances in the ease of use and efficiency, with the detection accuracy improved from 97% to 99.6%. Moreover, the time required to prevent already known malware in real time can be decreased by 99.8%
عطااللہ عیسیٰ خیلوی کے اردو گیتوں میں درد کے رجحان کا تجزیاتی مطالعہ: An Analysis of Pain in Urdu Songs of Attaullah Khan Easkhelvi
No full textAttaullah Khan Easkhelvi was born in a Pathan tribe at the Mianwali district of Punjab. He grew up in the village life and had not an interest in taking education but in the music since his childhood. He recited the qawalies and sang national songs. He is the singer of seven languages and famous in all over the world. He is not only a singer but also worked as play back singer in some films. However, he is considered as the symbolic of pain and ambassador of sorrow due to singing songs in his particular style. He is awarded a number of awards due to his admirable performance. This study is primary research which reveals the pain in Urdu songs of Attaullah Khan Easkhelvi
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