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

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

Sensors ◽

10.3390/s21134592 ◽

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.

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A Learning-based Neural Network Model for the Detection and Classification of SQL Injection Attacks

Deep Learning and Neural Networks ◽

10.4018/978-1-7998-0414-7.ch026 ◽

2020 ◽

pp. 450-475

Author(s):

Naghmeh Moradpoor Sheykhkanloo

Keyword(s):

Neural Network ◽

Web Application ◽

Query Language ◽

False Positive Rate ◽

True Positive Rate ◽

Injection Technique ◽

Data Sets ◽

Injection Attacks ◽

Proposed Model ◽

Positive Rate

Structured Query Language injection (SQLi) attack is a code injection technique where hackers inject SQL commands into a database via a vulnerable web application. Injected SQL commands can modify the back-end SQL database and thus compromise the security of a web application. In the previous publications, the author has proposed a Neural Network (NN)-based model for detections and classifications of the SQLi attacks. The proposed model was built from three elements: 1) a Uniform Resource Locator (URL) generator, 2) a URL classifier, and 3) a NN model. The proposed model was successful to: 1) detect each generated URL as either a benign URL or a malicious, and 2) identify the type of SQLi attack for each malicious URL. The published results proved the effectiveness of the proposal. In this paper, the author re-evaluates the performance of the proposal through two scenarios using controversial data sets. The results of the experiments are presented in order to demonstrate the effectiveness of the proposed model in terms of accuracy, true-positive rate as well as false-positive rate.

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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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Detection of Cyber-Attack in Broad-Scale Smart Grids using Deep and Scalable Unsupervised Machine Learning System

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.j7543.0891020 ◽

2020 ◽

Vol 9 (10) ◽

pp. 335-344

Keyword(s):

Anomaly Detection ◽

Power Distribution ◽

Smart Grids ◽

Detection System ◽

False Positive Rate ◽

True Positive Rate ◽

Statistical Correlation ◽

Learning System ◽

Cyber Attack ◽

Positive Rate

The increase in the reliability, efficiency and security of the electrical grids was credited to the innovation of the smart grid. It is also a fact that the smart grids a very dependable on the digital communication technology that in turn gives rise to undiscovered weaknesses which have to be reconsidered for dependable and coherent power distribution. In this paper, we propose an unsupervised anomaly detection which is mainly focused the statistical correlation among the data. The main aim is to create a scalable anomaly detection system suitable for huge-scale smart grids, which are capable to denote a difference between a real fault from a disruption and an intelligent cyber-attack. We have presented a methodology that applies the concept of attribute extraction by the use of Symbolic Dynamic Filtering (SDF) to decrease compilation drift whilst uncovering usual interactions among subsystems. Results of simulation obtained on IEEE 39, 118 and 2848 bus systems confirm the execution of the method, proposed in this paper, under various working conditions. The results depict a precision of almost 99 percent, along with 98 percent of true positive rate and less than 2 percent of false positive rate.

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

10.21203/rs.3.rs-173173/v1 ◽

2021 ◽

Author(s):

Chiaki Kuwada ◽

Yoshiko Ariji ◽

Motoki Fukuda ◽

Tsutomu Kuwada ◽

Kenichi Gotoh ◽

...

Keyword(s):

Deep Learning ◽

Cleft Palate ◽

Panoramic Radiography ◽

False Positive Rate ◽

Normal Subjects ◽

Learning System ◽

Panoramic Radiographs ◽

Positive Rate ◽

Aided Diagnosis

Abstract Although panoramic radiography has a role in the examination of patients with cleft alveolus (CA), its appearances is sometimes difficult to interpret. The aims of the present 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, and to clarify some characteristic appearances probably related to the performance. The panoramic radiographs of 383 CA patients with cleft palate (CA with CP group) or without cleft palate (CA only group) and 210 patients without CA (normal group) were used to create 2 learning models on the DetectNet. The models 1 and 2 were developed based on the data with and without normal subjects, respectively, to detect the CAs and classify them into the CA only and CA with CP groups. The model 2 reduced the false positive rate (1/30) compared to the model 1 (12/30). The model 2 performances were higher in almost values than those in the model 1, but no difference in the recall of CA with CP groups. The model created in the present study appeared to have the potential to detect and classify CAs on panoramic radiographs.

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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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Deep Learning Exoplanets Detection by Combining Real and Synthetic Data

10.20944/preprints202112.0070.v1 ◽

2021 ◽

Author(s):

Sara Cuéllar ◽

Paulo Granados ◽

Ernesto Fabregas ◽

Michel Curé ◽

Hector Vargas ◽

...

Keyword(s):

Deep Learning ◽

Synthetic Data ◽

Real Data ◽

True Positive Rate ◽

Learning System ◽

Classification Model ◽

Training Stage ◽

Positive Rate ◽

Optimisation Technique ◽

Planetary Transits

Scientists and astronomers have attached Scientists and astronomers have attached great importance to the task of discovering new exoplanets, even more so if they are in the habitable zone. To date, more than 4300 exoplanets have been confirmed by NASA, using various discovery techniques, including planetary transits, in addition to the use of various databases provided by space and ground-based telescopes. This article proposes the development of a deep learning system for detecting planetary transits in Kepler Telescope lightcurves. The approach is based on related work from the literature and enhanced to validation with real lightcurves. A CNN classification model is trained from a mixture of real and synthetic data, and validated only with real data and different from those used in the training stage. The best ratio of synthetic data is determined by the perform of an optimisation technique and a sensitivity analysis. The precision, accuracy and true positive rate of the best model obtained are determined and compared with other similar works. The results demonstrate that the use of synthetic data on the training stage can improve the transit detection performance on real light curves.

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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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