scholarly journals Deep Learning Approaches for Continuous Authentication Based on Activity Patterns Using Mobile Sensing

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7519
Author(s):  
Sakorn Mekruksavanich ◽  
Anuchit Jitpattanakul
Keyword(s):  
Deep Learning ◽  
Credit Card ◽  
User Authentication ◽  
Binary Classification ◽  
Activity Patterns ◽  
Personal Data ◽  
Security And Privacy ◽  
Sensor Data ◽  
Learning Approaches ◽  
Continuous Authentication

Smartphones as ubiquitous gadgets are rapidly becoming more intelligent and context-aware as sensing, networking, and processing capabilities advance. These devices provide users with a comprehensive platform to undertake activities such as socializing, communicating, sending and receiving e-mails, and storing and accessing personal data at any time and from any location. Nowadays, smartphones are used to store a multitude of private and sensitive data including bank account information, personal identifiers, account passwords and credit card information. Many users remain permanently signed in and, as a result, their mobile devices are vulnerable to security and privacy risks through assaults by criminals. Passcodes, PINs, pattern locks, facial verification, and fingerprint scans are all susceptible to various assaults including smudge attacks, side-channel attacks, and shoulder-surfing attacks. To solve these issues, this research introduces a new continuous authentication framework called DeepAuthen, which identifies smartphone users based on their physical activity patterns as measured by the accelerometer, gyroscope, and magnetometer sensors on their smartphone. We conducted a series of tests on user authentication using several deep learning classifiers, including our proposed deep learning network termed DeepConvLSTM on the three benchmark datasets UCI-HAR, WISDM-HARB and HMOG. Results demonstrated that combining various motion sensor data obtained the highest accuracy and energy efficiency ratio (EER) values for binary classification. We also conducted a thorough examination of the continuous authentication outcomes, and the results supported the efficacy of our framework.

scholarly journals Security, Privacy, and Usability in Continuous Authentication: A Survey

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5967
Author(s):  
Ahmed Fraz Baig ◽  
Sigurd Eskeland
Keyword(s):  
User Authentication ◽  
Personal Data ◽  
Privacy Preserving ◽  
Sensor Data ◽  
Security Issues ◽  
Replay Attacks ◽  
Continuous Authentication ◽  
Security Challenges ◽  
User Friendly ◽  
Privacy Issues

Continuous authentication has been proposed as a possible approach for passive and seamless user authentication, using sensor data comprising biometric, behavioral, and context-oriented characteristics. Since these are personal data being transmitted and are outside the control of the user, this approach causes privacy issues. Continuous authentication has security challenges concerning poor matching rates and susceptibility of replay attacks. The security issues are mainly poor matching rates and the problems of replay attacks. In this survey, we present an overview of continuous authentication and comprehensively discusses its different modes, and issues that these modes have related to security, privacy, and usability. A comparison of privacy-preserving approaches dealing with the privacy issues is provided, and lastly recommendations for secure, privacy-preserving, and user-friendly continuous authentication.

scholarly journals Deep Learning for Laryngopharyngeal Reflux Diagnosis

2021 ◽  
Vol 11 (11) ◽  
pp. 4753
Author(s):  
Gen Ye ◽  
Chen Du ◽  
Tong Lin ◽  
Yan Yan ◽  
Jack Jiang
Keyword(s):  
Deep Learning ◽  
Speech Processing ◽  
Data Augmentation ◽  
Laryngopharyngeal Reflux ◽  
Ph Monitoring ◽  
Binary Classification ◽  
Classification Problem ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Auc Value

(1) Background: Deep learning has become ubiquitous due to its impressive performance in various domains, such as varied as computer vision, natural language and speech processing, and game-playing. In this work, we investigated the performance of recent deep learning approaches on the laryngopharyngeal reflux (LPR) diagnosis task. (2) Methods: Our dataset is composed of 114 subjects with 37 pH-positive cases and 77 control cases. In contrast to prior work based on either reflux finding score (RFS) or pH monitoring, we directly take laryngoscope images as inputs to neural networks, as laryngoscopy is the most common and simple diagnostic method. The diagnosis task is formulated as a binary classification problem. We first tested a powerful backbone network that incorporates residual modules, attention mechanism and data augmentation. Furthermore, recent methods in transfer learning and few-shot learning were investigated. (3) Results: On our dataset, the performance is the best test classification accuracy is 73.4%, while the best AUC value is 76.2%. (4) Conclusions: This study demonstrates that deep learning techniques can be applied to classify LPR images automatically. Although the number of pH-positive images used for training is limited, deep network can still be capable of learning discriminant features with the advantage of technique.

scholarly journals Trends in Computer-Aided Diagnosis Using Deep Learning Techniques: A Review of Recent Studies on Algorithm Development

2017 ◽  
Author(s):  
Bosede Iyiade Edwards ◽  
Nosiba Hisham Osman Khougali ◽  
Adrian David Cheok
Keyword(s):  
Deep Learning ◽  
Human Subjects ◽  
Binary Classification ◽  
Computer Aided Diagnosis ◽  
Network Architectures ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Computer Aided ◽  
Multi Class Classification ◽  
Aided Diagnosis

With recent focus on deep neural network architectures for development of algorithms for computer-aided diagnosis (CAD), we provide a review of studies within the last 3 years (2015-2017) reported in selected top journals and conferences. 29 studies that met our inclusion criteria were reviewed to identify trends in this field and to inform future development. Studies have focused mostly on cancer-related diseases within internal medicine while diseases within gender-/age-focused fields like gynaecology/pediatrics have not received much focus. All reviewed studies employed image datasets, mostly sourced from publicly available databases (55.2%) and few based on data from human subjects (31%) and non-medical datasets (13.8%), while CNN architecture was employed in most (70%) of the studies. Confirmation of the effect of data manipulation on quality of output and adoption of multi-class rather than binary classification also require more focus. Future studies should leverage collaborations with medical experts to aid future with actual clinical testing with reporting based on some generally applicable index to enable comparison. Our next steps on plans for CAD development for osteoarthritis (OA), with plans to consider multi-class classification and comparison across deep learning approaches and unsupervised architectures were also highlighted.

scholarly journals Expert-Level Intracranial Electroencephalogram Ictal Pattern Detection by a Deep Learning Neural Network

2021 ◽  
Vol 12 ◽  
Author(s):  
Alexander C. Constantino ◽  
Nathaniel D. Sisterson ◽  
Naoir Zaher ◽  
Alexandra Urban ◽  
R. Mark Richardson ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Single Channel ◽  
Binary Classification ◽  
Initial Period ◽  
Learning Approaches ◽  
Intracranial Eeg ◽  
Strongly Connected ◽  
Precision Recall Curve ◽  
Deep Learning Neural Network

Background: Decision-making in epilepsy surgery is strongly connected to the interpretation of the intracranial EEG (iEEG). Although deep learning approaches have demonstrated efficiency in processing extracranial EEG, few studies have addressed iEEG seizure detection, in part due to the small number of seizures per patient typically available from intracranial investigations. This study aims to evaluate the efficiency of deep learning methodology in detecting iEEG seizures using a large dataset of ictal patterns collected from epilepsy patients implanted with a responsive neurostimulation system (RNS).Methods: Five thousand two hundred and twenty-six ictal events were collected from 22 patients implanted with RNS. A convolutional neural network (CNN) architecture was created to provide personalized seizure annotations for each patient. Accuracy of seizure identification was tested in two scenarios: patients with seizures occurring following a period of chronic recording (scenario 1) and patients with seizures occurring immediately following implantation (scenario 2). The accuracy of the CNN in identifying RNS-recorded iEEG ictal patterns was evaluated against human neurophysiology expertise. Statistical performance was assessed via the area-under-precision-recall curve (AUPRC).Results: In scenario 1, the CNN achieved a maximum mean binary classification AUPRC of 0.84 ± 0.19 (95%CI, 0.72–0.93) and mean regression accuracy of 6.3 ± 1.0 s (95%CI, 4.3–8.5 s) at 30 seed samples. In scenario 2, maximum mean AUPRC was 0.80 ± 0.19 (95%CI, 0.68–0.91) and mean regression accuracy was 6.3 ± 0.9 s (95%CI, 4.8–8.3 s) at 20 seed samples. We obtained near-maximum accuracies at seed size of 10 in both scenarios. CNN classification failures can be explained by ictal electro-decrements, brief seizures, single-channel ictal patterns, highly concentrated interictal activity, changes in the sleep-wake cycle, and progressive modulation of electrographic ictal features.Conclusions: We developed a deep learning neural network that performs personalized detection of RNS-derived ictal patterns with expert-level accuracy. These results suggest the potential for automated techniques to significantly improve the management of closed-loop brain stimulation, including during the initial period of recording when the device is otherwise naïve to a given patient's seizures.

Addressing Privacy in Traditional and Cloud-Based Systems

2017 ◽  
pp. 1900-1930
Author(s):  
Christos Kalloniatis ◽  
Evangelia Kavakli ◽  
Stefanos Gritzalis
Keyword(s):  
Credit Card ◽  
System Analysis ◽  
Systems Design ◽  
Personal Data ◽  
Research Area ◽  
Security And Privacy ◽  
Software Systems ◽  
Privacy Requirements ◽  
Credit Card Data ◽  
Privacy Violation

A major challenge in the field of software engineering is to make users trust the software that they use in their everyday activities for professional or recreational reasons. Amid the main criteria that formulate users' trust is the way that that their privacy is protected. Indeed, privacy violation is an issue of great importance for active online users that daily accomplish several transactions that may convey personal data, sensitive personal data, employee data, credit card data and so on. In addition, the appearance of cloud computing has elevated the number of personally identifiable information that users provide in order to gain access to various services, further raising user concerns as to how and to what extend information about them is communicated to others. The aim of this work is to elevate the modern practices for ensuring privacy during software systems design. To this end, the basic privacy requirements that should be considered during system analysis are introduced. Additionally, a number of well-known methods that have been introduced in the research area of requirements engineering which aim on eliciting and modeling privacy requirements during system design are introduced and critically analyzed. The work completes with a discussion of the additional security and privacy concepts that should be considered in the context of cloud-based information systems and how these affect current research.

Addressing Privacy in Traditional and Cloud-Based Systems

2015 ◽  
pp. 1631-1659
Author(s):  
Christos Kalloniatis ◽  
Evangelia Kavakli ◽  
Stefanos Gritzalis
Keyword(s):  
Credit Card ◽  
System Analysis ◽  
Systems Design ◽  
Personal Data ◽  
Research Area ◽  
Security And Privacy ◽  
Software Systems ◽  
Privacy Requirements ◽  
Credit Card Data ◽  
Privacy Violation

A major challenge in the field of software engineering is to make users trust the software that they use in their everyday activities for professional or recreational reasons. Amid the main criteria that formulate users' trust is the way that that their privacy is protected. Indeed, privacy violation is an issue of great importance for active online users that daily accomplish several transactions that may convey personal data, sensitive personal data, employee data, credit card data and so on. In addition, the appearance of cloud computing has elevated the number of personally identifiable information that users provide in order to gain access to various services, further raising user concerns as to how and to what extend information about them is communicated to others. The aim of this work is to elevate the modern practices for ensuring privacy during software systems design. To this end, the basic privacy requirements that should be considered during system analysis are introduced. Additionally, a number of well-known methods that have been introduced in the research area of requirements engineering which aim on eliciting and modeling privacy requirements during system design are introduced and critically analyzed. The work completes with a discussion of the additional security and privacy concepts that should be considered in the context of cloud-based information systems and how these affect current research.

Time Series in Sensor Data Using State-of-the-Art Deep Learning Approaches: A Systematic Literature Review

2021 ◽  
pp. 503-514
Author(s):  
Luis-Roberto Jácome-Galarza ◽  
Miguel-Andrés Realpe-Robalino ◽  
Jonathan Paillacho-Corredores ◽  
José-Leonardo Benavides-Maldonado
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
State Of The Art ◽  
Sensor Data ◽  
Learning Approaches

scholarly journals Gait-Based Implicit Authentication Using Edge Computing and Deep Learning for Mobile Devices

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4592
Author(s):  
Xin Zeng ◽  
Xiaomei Zhang ◽  
Shuqun Yang ◽  
Zhicai Shi ◽  
Chihung Chi
Keyword(s):  
Deep Learning ◽  
Mobile Devices ◽  
User Authentication ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Edge Computing ◽  
Behavior Modeling ◽  
Security And Privacy ◽  
Positive Rate ◽  
Edge Based

Implicit authentication mechanisms are expected to prevent security and privacy threats for mobile devices using behavior modeling. However, recently, researchers have demonstrated that the performance of behavioral biometrics is insufficiently accurate. Furthermore, the unique characteristics of mobile devices, such as limited storage and energy, make it subject to constrained capacity of data collection and processing. In this paper, we propose an implicit authentication architecture based on edge computing, coined Edge computing-based mobile Device Implicit Authentication (EDIA), which exploits edge-based gait biometric identification using a deep learning model to authenticate users. The gait data captured by a device’s accelerometer and gyroscope sensors is utilized as the input of our optimized model, which consists of a CNN and a LSTM in tandem. Especially, we deal with extracting the features of gait signal in a two-dimensional domain through converting the original signal into an image, and then input it into our network. In addition, to reduce computation overhead of mobile devices, the model for implicit authentication is generated on the cloud server, and the user authentication process also takes place on the edge devices. We evaluate the performance of EDIA under different scenarios where the results show that i) we achieve a true positive rate of 97.77% and also a 2% false positive rate; and ii) EDIA still reaches high accuracy with limited dataset size.

scholarly journals A Novel Context Aware Joint Segmentation and Classification Framework for Glaucoma Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
S. Sankar Ganesh ◽  
G. Kannayeram ◽  
Alagar Karthick ◽  
M. Muhibbullah
Keyword(s):  
Deep Learning ◽  
Binary Classification ◽  
Ocular Disease ◽  
Classification Model ◽  
Context Aware ◽  
Learning Approaches ◽  
Unique Challenge ◽  
Classification Framework ◽  
Glaucoma Detection ◽  
Insidious Onset

Glaucoma is a chronic ocular disease characterized by damage to the optic nerve resulting in progressive and irreversible visual loss. Early detection and timely clinical interventions are critical in improving glaucoma-related outcomes. As a typical and complicated ocular disease, glaucoma detection presents a unique challenge due to its insidious onset and high intra- and interpatient variabilities. Recent studies have demonstrated that robust glaucoma detection systems can be realized with deep learning approaches. The optic disc (OD) is the most commonly studied retinal structure for screening and diagnosing glaucoma. This paper proposes a novel context aware deep learning framework called GD-YNet, for OD segmentation and glaucoma detection. It leverages the potential of aggregated transformations and the simplicity of the YNet architecture in context aware OD segmentation and binary classification for glaucoma detection. Trained with the RIGA and RIMOne-V2 datasets, this model achieves glaucoma detection accuracies of 99.72%, 98.02%, 99.50%, and 99.41% with the ACRIMA, Drishti-gs, REFUGE, and RIMOne-V1 datasets. Further, the proposed model can be extended to a multiclass segmentation and classification model for glaucoma staging and severity assessment.

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