scholarly journals Niffler: A Context-Aware and User-Independent Side-Channel Attack System for Password Inference

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Benxiao Tang ◽  
Zhibo Wang ◽  
Run Wang ◽  
Lei Zhao ◽  
Lina Wang
Keyword(s):  
Mobile Devices ◽  
Ground Truth ◽  
Training Data ◽  
Side Channel ◽  
Context Aware ◽  
Time Warping ◽  
Side Channel Attack ◽  
Training Samples ◽  
Independent Features ◽  
Dynamic Time

Digital password lock has been commonly used on mobile devices as the primary authentication method. Researches have demonstrated that sensors embedded on mobile devices can be employed to infer the password. However, existing works focus on either each single keystroke inference or entire password sequence inference, which are user-dependent and require huge efforts to collect the ground truth training data. In this paper, we design a novel side-channel attack system, called Niffler, which leverages the user-independent features of movements of tapping consecutive buttons to infer unlocking passwords on smartphones. We extract angle features to reflect the changing trends and build a multicategory classifier combining the dynamic time warping algorithm to infer the probability of each movement. We further use the Markov model to model the unlocking process and use the sequences with the highest probabilities as the attack candidates. Moreover, the sensor readings of successful attacks will be further fed back to continually improve the accuracy of the classifier. In our experiments, 100,000 samples collected from 25 participants are used to evaluate the performance of Niffler. The results show that Niffler achieves 70% and 85% accuracy with 10 attempts in user-independent and user-dependent environments with few training samples, respectively.

scholarly journals Satellite Image Time Series Clustering via Time Adaptive Optimal Transport

2021 ◽  
Vol 13 (19) ◽  
pp. 3993
Author(s):  
Zheng Zhang ◽  
Ping Tang ◽  
Weixiong Zhang ◽  
Liang Tang
Keyword(s):  
Time Series ◽  
Similarity Measure ◽  
Dynamic Time Warping ◽  
Optimal Transport ◽  
Satellite Image ◽  
Time Warping ◽  
Unsupervised Analysis ◽  
Training Samples ◽  
Dynamic Time ◽  
Visual Results

Satellite Image Time Series (SITS) have become more accessible in recent years and SITS analysis has attracted increasing research interest. Given that labeled SITS training samples are time and effort consuming to acquire, clustering or unsupervised analysis methods need to be developed. Similarity measure is critical for clustering, however, currently established methods represented by Dynamic Time Warping (DTW) still exhibit several issues when coping with SITS, such as pathological alignment, sensitivity to spike noise, and limitation on capacity. In this paper, we introduce a new time series similarity measure method named time adaptive optimal transport (TAOT) to the application of SITS clustering. TAOT inherits several promising properties of optimal transport for the comparing of time series. Statistical and visual results on two real SITS datasets with two different settings demonstrate that TAOT can effectively alleviate the issues of DTW and further improve the clustering accuracy. Thus, TAOT can serve as a usable tool to explore the potential of precious SITS data.

scholarly journals Forest-Type Classification Using Time-Weighted Dynamic Time Warping Analysis in Mountain Areas: A Case Study in Southern China

Forests ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 1040 ◽  
Author(s):  
Kai Cheng ◽  
Juanle Wang
Keyword(s):  
Time Series ◽  
Dynamic Time Warping ◽  
Forest Type ◽  
Southern China ◽  
Training Data ◽  
Mountain Forest ◽  
Time Warping ◽  
Mountain Areas ◽  
Dynamic Time ◽  
Time Series Images

Efficient methodologies for mapping forest types in complicated mountain areas are essential for the implementation of sustainable forest management practices and monitoring. Existing solutions dedicated to forest-type mapping are primarily focused on supervised machine learning algorithms (MLAs) using remote sensing time-series images. However, MLAs are challenged by complex and problematic forest type compositions, lack of training data, loss of temporal data caused by clouds obscuration, and selection of input feature sets for mountainous areas. The time-weighted dynamic time warping (TWDTW) is a supervised classifier, an adaptation of the dynamic time warping method for time series analysis for land cover classification. This study evaluates the performance of the TWDTW method that uses a combination of Sentinel-2 and Landsat-8 time-series images when applied to complicated mountain forest-type classifications in southern China with complex topographic conditions and forest-type compositions. The classification outputs were compared to those produced by MLAs, including random forest (RF) and support vector machine (SVM). The results presented that the three forest-type maps obtained by TWDTW, RF, and SVM have high consistency in spatial distribution. TWDTW outperformed SVM and RF with mean overall accuracy and mean kappa coefficient of 93.81% and 0.93, respectively, followed by RF and SVM. Compared with MLAs, TWDTW method achieved the higher classification accuracy than RF and SVM, with even less training data. This proved the robustness and less sensitivities to training samples of the TWDTW method when applied to mountain forest-type classifications.

scholarly journals A Dynamic Time Warping Based Algorithm to Evaluate Kinect-Enabled Home-Based Physical Rehabilitation Exercises for Older People

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2882 ◽  
Author(s):  
Xiaoqun Yu ◽  
Shuping Xiong
Keyword(s):  
Older People ◽  
Dynamic Time Warping ◽  
Tai Chi ◽  
Physical Rehabilitation ◽  
Training Data ◽  
Time Warping ◽  
Home Based ◽  
Gaming Environment ◽  
Dynamic Time ◽  
Rehabilitation Exercises

Older people face difficulty engaging in conventional rehabilitation exercises for improving physical functions over a long time period due to the passive nature of the conventional exercise, inconvenience, and cost. This study aims to develop and validate a dynamic time warping (DTW) based algorithm for assessing Kinect-enabled home-based physical rehabilitation exercises, in order to support auto-coaching in a virtual gaming environment. A DTW-based algorithm was first applied to compute motion similarity between two time series from an individual user and a virtual coach. We chose eight bone vectors of the human skeleton and body orientation as the input features and proposed a simple but innovative method to further convert the DTW distance to a meaningful performance score in terms of the percentage (0–100%), without training data and experience of experts. The effectiveness of the proposed algorithm was validated through a follow-up experiment with 21 subjects when playing a Tai Chi exergame. Results showed that the algorithm scores had a strong positive linear relationship (r = 0.86) with experts’ ratings and the calibrated algorithm scores were comparable to the gold standard. These findings suggested that the DTW-based algorithm could be effectively used for automatic performance evaluation of an individual when performing home-based rehabilitation exercises.

scholarly journals Deep Learning and NLP based Side Channel Attack for Text Inference in Smartphones

2019 ◽  
Vol 9 (2) ◽  
pp. 1132-1137
Keyword(s):  
Small Volume ◽  
Detection Method ◽  
Critical Role ◽  
Mobile Security ◽  
Training Data ◽  
Embedded Sensors ◽  
Side Channel ◽  
Proof Of Concept ◽  
Side Channel Attack ◽  
The Past

Over the past years, smartphones have witnessed an alarming rise in embedded sensors which enhance their support for applications. However, they can be regarded as loopholes as seemingly innocuous information can be obtained without any user permissions in Android thus invading the user’s privacy. Our work establishes a side channel attack by illegitimately inferring the information being typed by the user on a smartphone using the readings from ‘zero-permission’ sensors like accelerometer and gyroscope. This serves as a proof of concept to prevent such attacks on mobile devices in the future. While previous research has been conducted in this space, our narrative involves a predictive model using Recurrent Neural Networks that can predict the letters being typed in the keyboard solely based on the motion sensor readings, thus inferring the text. Our research was able to identify 37.5% of the unseen words typed by the user using a very small volume of training data. Our tap detection method has shown 92% accuracy which plays a critical role in the text inference. This research lays the foundation to further progress in this area, thus helping to strengthen the mobile security

scholarly journals Surgical data processing for smart intraoperative assistance systems

2017 ◽  
Vol 2 (3) ◽  
pp. 145-152 ◽  
Author(s):  
Ralf Stauder ◽  
Daniel Ostler ◽  
Thomas Vogel ◽  
Dirk Wilhelm ◽  
Sebastian Koller ◽  
...  
Keyword(s):  
Neural Networks ◽  
Dynamic Time Warping ◽  
Data Science ◽  
Markov Models ◽  
Context Aware ◽  
Time Warping ◽  
Surgical Assistance ◽  
Surgical Data ◽  
Assistance Systems ◽  
Dynamic Time

AbstractDifferent components of the newly defined field of surgical data science have been under research at our groups for more than a decade now. In this paper, we describe our sensor-driven approaches to workflow recognition without the need for explicit models, and our current aim is to apply this knowledge to enable context-aware surgical assistance systems, such as a unified surgical display and robotic assistance systems. The methods we evaluated over time include dynamic time warping, hidden Markov models, random forests, and recently deep neural networks, specifically convolutional neural networks.

scholarly journals Automated Segmentation of Epithelial Tissue Using Cycle-Consistent Generative Adversarial Networks

2018 ◽  
Author(s):  
Matthias Häring ◽  
Jörg Großhans ◽  
Fred Wolf ◽  
Stephan Eule
Keyword(s):  
Ground Truth ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Epithelial Tissue ◽  
Automated Segmentation ◽  
Segmentation Method ◽  
Adversarial Networks ◽  
Training Samples ◽  
Segmentation Of Images ◽  
Image Mask

AbstractA central problem in biomedical imaging is the automated segmentation of images for further quantitative analysis. Recently, fully convolutional neural networks, such as the U-Net, were applied successfully in a variety of segmentation tasks. A downside of this approach is the requirement for a large amount of well-prepared training samples, consisting of image - ground truth mask pairs. Since training data must be created by hand for each experiment, this task can be very costly and time-consuming. Here, we present a segmentation method based on cycle consistent generative adversarial networks, which can be trained even in absence of prepared image - mask pairs. We show that it successfully performs image segmentation tasks on samples with substantial defects and even generalizes well to different tissue types.

Application of Mobile Terrestrial LiDAR Scanning Systems for Identification of Potential Pavement Rutting Locations

2021 ◽  
pp. 036119812110057
Author(s):  
Afshin Famili ◽  
Wayne A. Sarasua ◽  
Alireza Shams ◽  
William J. Davis ◽  
Jennifer H. Ogle
Keyword(s):  
Grid Cell ◽  
Ground Truth ◽  
Pavement Management ◽  
Time Warping ◽  
Terrestrial Lidar ◽  
Transverse Profile ◽  
Cell Groups ◽  
Raster Cell ◽  
Using Data ◽  
Dynamic Time

Periodic measurement and identification of the presence and severity of pavement rutting are crucial for pavement management programs conducted by state transportation agencies. This paper proposes a novel analytical method for identifying pavement rutting locations using data collected by mobile terrestrial LiDAR scanning (MTLS). Four vendor MTLS systems were evaluated based on their ability to accurately reproduce a roadway’s transverse profile. To establish ground-truth measurements, 2 in. interval pavement transverse profiles, which included rutting sections, were collected using traditional surveying techniques. MTLS transverse profiles were evaluated using partial curve mapping, Fréchet distance, area, curve length, and dynamic time warping techniques. Resultant pavement transverse profiles were compared between vendors and a profile created from traditional surveying. Results indicate that calibrated MTLS systems can provide accurate transverse profiles for potential identification of pavement rut areas. Based on this determination, a novel method was developed for use in identifying locations of pavement rutting through analysis of the curvature of MTLS raster surfaces. After evaluating three grid cell sizes for elevation raster surfaces, a raster grid cell size of 1 ft × 1 ft was determined to be most suitable for identifying continuous concave raster cell groups along wheel path trajectories. These cell groupings were found to reliably identify pavement rutting locations. The analytical procedures employed through application of this method consist of an efficient workflow process that is not reliant on a time-consuming continuous comparison with an MTLS-modeled uniform surface.

scholarly journals Location Privacy Leakage through Sensory Data

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Yi Liang ◽  
Zhipeng Cai ◽  
Qilong Han ◽  
Yingshu Li
Keyword(s):  
Mobile Devices ◽  
Dynamic Time Warping ◽  
Location Privacy ◽  
Embedded Sensors ◽  
Location Information ◽  
Time Warping ◽  
Sensory Data ◽  
Privacy Leakage ◽  
Bounded Area ◽  
Dynamic Time

Mobile devices bring benefits as well as the risk of exposing users’ location information, as some embedded sensors can be accessed without users’ permission and awareness. In this paper, we show that, only by using the data collected from the embedded sensors in mobile devices instead of GPS data, we can infer a user’s location information with high accuracy. Three issues are addressed which are route identification, user localization in a specific route, and user localization in a bounded area. The Dynamic Time Warping based technique is designed and we develop a Hidden Markov Model to solve the localization problem. Real experiments are performed to evaluate our proposed methods.

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