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

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.

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Var-CNN: A Data-Efficient Website Fingerprinting Attack Based on Deep Learning

Proceedings on Privacy Enhancing Technologies ◽

10.2478/popets-2019-0070 ◽

2019 ◽

Vol 2019 (4) ◽

pp. 292-310 ◽

Cited By ~ 10

Author(s):

Sanjit Bhat ◽

David Lu ◽

Albert Kwon ◽

Srinivas Devadas

Keyword(s):

Deep Learning ◽

State Of The Art ◽

False Positive Rate ◽

True Positive Rate ◽

Training Data ◽

Open World ◽

Prior Art ◽

Lower False Positive Rate ◽

Positive Rate ◽

Fingerprinting Attack

Abstract In recent years, there have been several works that use website fingerprinting techniques to enable a local adversary to determine which website a Tor user visits. While the current state-of-the-art attack, which uses deep learning, outperforms prior art with medium to large amounts of data, it attains marginal to no accuracy improvements when both use small amounts of training data. In this work, we propose Var-CNN, a website fingerprinting attack that leverages deep learning techniques along with novel insights specific to packet sequence classification. In open-world settings with large amounts of data, Var-CNN attains over 1% higher true positive rate (TPR) than state-of-the-art attacks while achieving 4× lower false positive rate (FPR). Var-CNN’s improvements are especially notable in low-data scenarios, where it reduces the FPR of prior art by 3.12% while increasing the TPR by 13%. Overall, insights used to develop Var-CNN can be applied to future deep learning based attacks, and substantially reduce the amount of training data needed to perform a successful website fingerprinting attack. This shortens the time needed for data collection and lowers the likelihood of having data staleness issues.

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Neural Network Detection and Segmentation of Mental Foramen in Panoramic Imaging

Journal of Clinical Pediatric Dentistry ◽

10.17796/1053-4625-44.3.6 ◽

2020 ◽

Vol 44 (3) ◽

pp. 168-173

Author(s):

Lazar Kats ◽

Marilena Vered ◽

Sigalit Blumer ◽

Eytan Kats

Keyword(s):

Neural Network ◽

Deep Learning ◽

Biomedical Application ◽

False Positive Rate ◽

True Positive Rate ◽

Mental Foramen ◽

Dice Similarity Coefficient ◽

Tel Aviv ◽

Positive Rate ◽

Panoramic Images

Objective: To apply the technique of deep learning on a small dataset of panoramic images for the detection and segmentation of the mental foramen (MF). Study design: In this study we used in-house dataset created within the School of Dental Medicine, Tel Aviv University. The dataset contained randomly chosen and anonymized 112 digital panoramic X-ray images and corresponding segmentations of MF. In order to solve the task of segmentation of the MF we used a single fully convolution neural network, that was based on U-net as well as a cascade architecture. 70% of the data were randomly chosen for training, 15% for validation and accuracy was tested on 15%. The model was trained using NVIDIA GeForce GTX 1080 GPU. The SPSS software, version 17.0 (Chicago, IL, USA) was used for the statistical analysis. The study was approved by the ethical committee of Tel Aviv University. Results: The best results of the dice similarity coefficient ( DSC), precision, recall, MF-wise true positive rate (MFTPR) and MF-wise false positive rate (MFFPR) in single networks were 49.51%, 71.13%, 68.24%, 87.81% and 14.08%, respectively. The cascade of networks has shown better results than simple networks in recall and MFTPR, which were 88.83%, 93.75%, respectively, while DSC and precision achieved the lowest values, 31.77% and 23.92%, respectively. Conclusions: Currently, the U-net, one of the most used neural network architectures for biomedical application, was effectively used in this study. Methods based on deep learning are extremely important for automatic detection and segmentation in radiology and require further development.

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Permission-based Analysis of Android Applications Using Categorization and Deep Learning Scheme

MATEC Web of Conferences ◽

10.1051/matecconf/201925505005 ◽

2019 ◽

Vol 255 ◽

pp. 05005 ◽

Cited By ~ 1

Author(s):

Alimardani Hamidreza ◽

Nazeh Mohammed

Keyword(s):

Operating System ◽

Deep Learning ◽

Mobile Devices ◽

True Positive Rate ◽

Research Community ◽

True Positive ◽

Daily Lives ◽

Breaking News ◽

Positive Rate ◽

Android Applications

As mobile devices grow in popularity, they have become indispensable in people's daily lives, keeping us connected to social networks, breaking news, and the entire Internet. While there are multiple competing platforms, Google's Android is currently the most popular operating system for mobile devices. This popularity has drawn attention of hackers as well. Thus far, research works have analysed Android permissions individually, which makes analysis complex and time consuming. In this work, we propose categorizing Android permissions based on Google's recommendation and perform LSTM analysis on data. The used datasets are Drebin and AndroZoo, which are the most complete and well-respected among research community. The experiment results show that LSTM achieved 91% of true positive rate.

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Web Phishing Detection Using a Deep Learning Framework

Wireless Communications and Mobile Computing ◽

10.1155/2018/4678746 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 15

Author(s):

Ping Yi ◽

Yuxiang Guan ◽

Futai Zou ◽

Yao Yao ◽

Wei Wang ◽

...

Keyword(s):

Deep Learning ◽

Private Information ◽

Credit Card ◽

False Positive Rate ◽

True Positive Rate ◽

The Internet ◽

Internet Service ◽

Learning Framework ◽

Model Based ◽

Positive Rate

Web service is one of the key communications software services for the Internet. Web phishing is one of many security threats to web services on the Internet. Web phishing aims to steal private information, such as usernames, passwords, and credit card details, by way of impersonating a legitimate entity. It will lead to information disclosure and property damage. This paper mainly focuses on applying a deep learning framework to detect phishing websites. This paper first designs two types of features for web phishing: original features and interaction features. A detection model based on Deep Belief Networks (DBN) is then presented. The test using real IP flows from ISP (Internet Service Provider) shows that the detecting model based on DBN can achieve an approximately 90% true positive rate and 0.6% false positive rate.

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DeepStreaks: identifying fast-moving objects in the Zwicky Transient Facility data with deep learning

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/stz1096 ◽

2019 ◽

Vol 486 (3) ◽

pp. 4158-4165 ◽

Cited By ~ 7

Author(s):

Dmitry A Duev ◽

Ashish Mahabal ◽

Quanzhi Ye ◽

Kushal Tirumala ◽

Justin Belicki ◽

...

Keyword(s):

Deep Learning ◽

Fast Moving ◽

Moving Objects ◽

False Positive Rate ◽

True Positive Rate ◽

Learning System ◽

Data Sets ◽

Wide Field ◽

Positive Rate ◽

Near Earth Objects

ABSTRACT We present DeepStreaks, a convolutional-neural-network, deep-learning system designed to efficiently identify streaking fast-moving near-Earth objects that are detected in the data of the Zwicky Transient Facility (ZTF), a wide-field, time-domain survey using a dedicated 47 deg2 camera attached to the Samuel Oschin 48-inch Telescope at the Palomar Observatory in California, United States. The system demonstrates a 96–98 per cent true positive rate, depending on the night, while keeping the false positive rate below 1 per cent. The sensitivity of DeepStreaks is quantified by the performance on the test data sets as well as using known near-Earth objects observed by ZTF. The system is deployed and adapted for usage within the ZTF Solar system framework and has significantly reduced human involvement in the streak identification process, from several hours to typically under 10 min per day.

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PRATD: A Phased Remote Access Trojan Detection Method with Double-Sided Features

Electronics ◽

10.3390/electronics9111894 ◽

2020 ◽

Vol 9 (11) ◽

pp. 1894

Author(s):

Chun Guo ◽

Zihua Song ◽

Yuan Ping ◽

Guowei Shen ◽

Yuhei Cui ◽

...

Keyword(s):

False Positive ◽

Detection Method ◽

False Positive Rate ◽

True Positive Rate ◽

Remote Access ◽

Detection Methods ◽

Security Threats ◽

True Positive ◽

Trojan Detection ◽

Positive Rate

Remote Access Trojan (RAT) is one of the most terrible security threats that organizations face today. At present, two major RAT detection methods are host-based and network-based detection methods. To complement one another’s strengths, this article proposes a phased RATs detection method by combining double-side features (PRATD). In PRATD, both host-side and network-side features are combined to build detection models, which is conducive to distinguishing the RATs from benign programs because that the RATs not only generate traffic on the network but also leave traces on the host at run time. Besides, PRATD trains two different detection models for the two runtime states of RATs for improving the True Positive Rate (TPR). The experiments on the network and host records collected from five kinds of benign programs and 20 famous RATs show that PRATD can effectively detect RATs, it can achieve a TPR as high as 93.609% with a False Positive Rate (FPR) as low as 0.407% for the known RATs, a TPR 81.928% and FPR 0.185% for the unknown RATs, which suggests it is a competitive candidate for RAT detection.

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Ascertaining an efficient eligibility cut-off for extended Medicare items for eating disorders

Australasian Psychiatry ◽

10.1177/10398562211028632 ◽

2021 ◽

pp. 103985622110286

Author(s):

Tracey Wade ◽

Jamie-Lee Pennesi ◽

Yuan Zhou

Keyword(s):

Eating Disorders ◽

Eating Disorder ◽

False Positive Rate ◽

Area Under The Curve ◽

Rate Sensitivity ◽

True Positive Rate ◽

Eating Disorder Examination Questionnaire ◽

Eating Disorder Examination ◽

Positive Rate ◽

The Relationship

Objective: Currently eligibility for expanded Medicare items for eating disorders (excluding anorexia nervosa) require a score ⩾ 3 on the 22-item Eating Disorder Examination-Questionnaire (EDE-Q). We compared these EDE-Q “cases” with continuous scores on a validated 7-item version of the EDE-Q (EDE-Q7) to identify an EDE-Q7 cut-off commensurate to 3 on the EDE-Q. Methods: We utilised EDE-Q scores of female university students ( N = 337) at risk of developing an eating disorder. We used a receiver operating characteristic (ROC) curve to assess the relationship between the true-positive rate (sensitivity) and the false-positive rate (1-specificity) of cases ⩾ 3. Results: The area under the curve showed outstanding discrimination of 0.94 (95% CI: .92–.97). We examined two specific cut-off points on the EDE-Q7, which included 100% and 87% of true cases, respectively. Conclusion: Given the EDE-Q cut-off for Medicare is used in conjunction with other criteria, we suggest using the more permissive EDE-Q7 cut-off (⩾2.5) to replace use of the EDE-Q cut-off (⩾3) in eligibility assessments.

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Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Records

Political Analysis ◽

10.1093/pan/mpw001 ◽

2016 ◽

Vol 24 (2) ◽

pp. 263-272 ◽

Cited By ~ 29

Author(s):

Kosuke Imai ◽

Kabir Khanna

Keyword(s):

Mean Squared Error ◽

False Positive Rate ◽

True Positive Rate ◽

Voter Registration ◽

Racial Groups ◽

Ecological Inference ◽

Inference Problem ◽

Individual Level ◽

Positive Rate ◽

Election Results

In both political behavior research and voting rights litigation, turnout and vote choice for different racial groups are often inferred using aggregate election results and racial composition. Over the past several decades, many statistical methods have been proposed to address this ecological inference problem. We propose an alternative method to reduce aggregation bias by predicting individual-level ethnicity from voter registration records. Building on the existing methodological literature, we use Bayes's rule to combine the Census Bureau's Surname List with various information from geocoded voter registration records. We evaluate the performance of the proposed methodology using approximately nine million voter registration records from Florida, where self-reported ethnicity is available. We find that it is possible to reduce the false positive rate among Black and Latino voters to 6% and 3%, respectively, while maintaining the true positive rate above 80%. Moreover, we use our predictions to estimate turnout by race and find that our estimates yields substantially less amounts of bias and root mean squared error than standard ecological inference estimates. We provide open-source software to implement the proposed methodology.

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IoT Botnet Anomaly Detection Using Unsupervised Deep Learning

Electronics ◽

10.3390/electronics10161876 ◽

2021 ◽

Vol 10 (16) ◽

pp. 1876

Author(s):

Ioana Apostol ◽

Marius Preda ◽

Constantin Nila ◽

Ion Bica

Keyword(s):

Deep Learning ◽

Detection System ◽

False Positive Rate ◽

Empirical Evaluation ◽

Learning Approaches ◽

Promising Alternative ◽

Positive Rate ◽

Unsupervised Deep Learning ◽

Attack Surface ◽

Iot Devices

The Internet of Things has become a cutting-edge technology that is continuously evolving in size, connectivity, and applicability. This ecosystem makes its presence felt in every aspect of our lives, along with all other emerging technologies. Unfortunately, despite the significant benefits brought by the IoT, the increased attack surface built upon it has become more critical than ever. Devices have limited resources and are not typically created with security features. Lately, a trend of botnet threats transitioning to the IoT environment has been observed, and an army of infected IoT devices can expand quickly and be used for effective attacks. Therefore, identifying proper solutions for securing IoT systems is currently an important and challenging research topic. Machine learning-based approaches are a promising alternative, allowing the identification of abnormal behaviors and the detection of attacks. This paper proposes an anomaly-based detection solution that uses unsupervised deep learning techniques to identify IoT botnet activities. An empirical evaluation of the proposed method is conducted on both balanced and unbalanced datasets to assess its threat detection capability. False-positive rate reduction and its impact on the detection system are also analyzed. Furthermore, a comparison with other unsupervised learning approaches is included. The experimental results reveal the performance of the proposed detection method.

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Detection and classification of unilateral cleft alveolus with and without cleft palate on panoramic radiographs using a deep learning system

Scientific Reports ◽

10.1038/s41598-021-95653-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Chiaki Kuwada ◽

Yoshiko Ariji ◽

Yoshitaka Kise ◽

Takuma Funakoshi ◽

Motoki Fukuda ◽

...

Keyword(s):

Deep Learning ◽

Cleft Palate ◽

Panoramic Radiography ◽

False Positive Rate ◽

Normal Subjects ◽

Learning System ◽

Panoramic Radiographs ◽

Positive Rate ◽

Aided Diagnosis

AbstractAlthough panoramic radiography has a role in the examination of patients with cleft alveolus (CA), its appearances is sometimes difficult to interpret. The aims of this study were to develop a computer-aided diagnosis system for diagnosing the CA status on panoramic radiographs using a deep learning object detection technique with and without normal data in the learning process, to verify its performance in comparison to human observers, and to clarify some characteristic appearances probably related to the performance. The panoramic radiographs of 383 CA patients with cleft palate (CA with CP) or without cleft palate (CA only) and 210 patients without CA (normal) were used to create two models on the DetectNet. The models 1 and 2 were developed based on the data without and with normal subjects, respectively, to detect the CAs and classify them into with or without CP. The model 2 reduced the false positive rate (1/30) compared to the model 1 (12/30). The overall accuracy of Model 2 was higher than Model 1 and human observers. The model created in this study appeared to have the potential to detect and classify CAs on panoramic radiographs, and might be useful to assist the human observers.

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