Neural Network Detection and Segmentation of Mental Foramen in Panoramic Imaging

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.

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

2017 ◽  
Vol 7 (2) ◽  
pp. 16-41 ◽  
Author(s):  
Naghmeh Moradpoor Sheykhkanloo
Keyword(s):  
Neural Network ◽  
Web Application ◽  
Query Language ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Injection Technique ◽  
Injection Attacks ◽  
Proposed Model ◽  
Positive Rate ◽  
Sql Injection Attacks

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.

WLI Fuzzy Clustering and Adaptive Lion Neural Network (ALNN) for Cloud Intrusion Detection

2019 ◽  
Vol 11 (1) ◽  
pp. 1-17
Author(s):  
Pinki Sharma ◽  
Jyotsna Sengupta ◽  
P. K. Suri
Keyword(s):  
Neural Network ◽  
Fuzzy Clustering ◽  
Clustered Data ◽  
False Positive Rate ◽  
Cloud Service ◽  
True Positive Rate ◽  
Aggregated Data ◽  
Marquardt Algorithm ◽  
Positive Rate ◽  
And Performance

Cloud computing is the internet-based technique where the users utilize the online resources for computing services. The attacks or intrusion into the cloud service is the major issue in the cloud environment since it degrades performance. In this article, we propose an adaptive lion-based neural network (ALNN) to detect the intrusion behaviour. Initially, the cloud network has generated the clusters using a WLI fuzzy clustering mechanism. This mechanism obtains the different numbers of clusters in which the data objects are grouped together. Then, the clustered data is fed into the newly designed adaptive lion-based neural network. The proposed method is developed by the combination of Levenberg-Marquardt algorithm of neural network and adaptive lion algorithm where female lions are used to update the weight adaptively using lion optimization algorithm. Then, the proposed method is used to detect the malicious activity through training process. Thus, the different clustered data is given to the proposed ALNN model. Once the data is trained, then it needs to be aggregated. Subsequently, the aggregated data is fed into the proposed ALNN method where the intrusion behaviour is detected. Finally, the simulation results of the proposed method and performance is analysed through accuracy, false positive rate, and true positive rate. Thus, the proposed ALNN algorithm attains 96.46% accuracy which ensures better detection performance.

A hybrid optimization-based deep belief neural network for the classification of vegetation area in multi-spectral satellite image

2021 ◽  
Vol 24 (4) ◽  
pp. 363-379
Author(s):  
Anil B. Gavade ◽  
Vijay S. Rajpurohit
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Land Cover ◽  
False Positive Rate ◽  
Satellite Image ◽  
Harmony Search ◽  
Classification Problem ◽  
True Positive Rate ◽  
Positive Rate

Over the last few decades, multiple advances have been done for the classification of vegetation area through land cover, and land use. However, classification problem is one of the most complicated and contradicting problems that has received considerable attention. Therefore, to tackle this problem, this paper proposes a new Firefly-Harmony search based Deep Belief Neural Network method (FHS-DBN) for the classification of land cover, and land use. The segmentation process is done using Bayesian Fuzzy Clustering,and the feature matrix is developed. The feature matrix is given to the proposed FHS-DBN method that distinguishes the land coverfrom the land use in the multispectral satellite images, for analyzing the vegetation area. The proposed FHS-DBN method is designedby training the DBN using the FHS algorithm, which is developed by the combination of Firefly Algorithm (FA) and Harmony Search (HS) algorithm. The performance of the FHS-DBN model is evaluated using three metrics, such as Accuracy, True Positive Rate (TPR), and False Positive Rate (FPR). From the experimental analysis, it is concludedthat the proposed FHS-DBN model achieves ahigh classification accuracy of 0.9381, 0.9488, 0.9497, and 0.9477 usingIndian Pine, Salinas scene, Pavia Centre and university, and Pavia University scene dataset.

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

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.

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

2019 ◽  
Vol 2019 (4) ◽  
pp. 292-310 ◽  
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.

scholarly journals Web Phishing Detection Using a Deep Learning Framework

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
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.

scholarly journals Computer-aided COVID-19 diagnosis and a comparison of deep learners using augmented CXRs

2021 ◽  
pp. 1-21
Author(s):  
Asma Naseer ◽  
Maria Tamoor ◽  
Arifah Azhar
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
False Positive Rate ◽  
False Negative ◽  
True Positive Rate ◽  
X Rays ◽  
Infected People ◽  
Computer Aided ◽  
High True Positive Rate ◽  
Positive Rate

Background: Coronavirus Disease 2019 (COVID-19) is contagious, producing respiratory tract infection, caused by a newly discovered coronavirus. Its death toll is too high, and early diagnosis is the main problem nowadays. Infected people show a variety of symptoms such as fatigue, fever, tastelessness, dry cough, etc. Some other symptoms may also be manifested by radiographic visual identification. Therefore, Chest X-Rays (CXR) play a key role in the diagnosis of COVID-19. Methods: In this study, we use Chest X-Rays images to develop a computer-aided diagnosis (CAD) of the disease. These images are used to train two deep networks, the Convolution Neural Network (CNN), and the Long Short-Term Memory Network (LSTM) which is an artificial Recurrent Neural Network (RNN). The proposed study involves three phases. First, the CNN model is trained on raw CXR images. Next, it is trained on pre-processed CXR images and finally enhanced CXR images are used for deep network CNN training. Geometric transformations, color transformations, image enhancement, and noise injection techniques are used for augmentation. From augmentation, we get 3,220 augmented CXRs as training datasets. In the final phase, CNN is used to extract the features of CXR imagery that are fed to the LSTM model. The performance of the four trained models is evaluated by the evaluation techniques of different models, including accuracy, specificity, sensitivity, false-positive rate, and receiver operating characteristic (ROC) curve. Results: We compare our results with other benchmark CNN models. Our proposed CNN-LSTM model gives superior accuracy (99.02%) than the other state-of-the-art models. Our method to get improved input, helped the CNN model to produce a very high true positive rate (TPR 1) and no false-negative result whereas false negative was a major problem while using Raw CXR images. Conclusions: We conclude after performing different experiments that some image pre-processing and augmentation, remarkably improves the results of CNN-based models. It will help a better early detection of the disease that will eventually reduce the mortality rate of COVID.

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

2019 ◽  
Vol 486 (3) ◽  
pp. 4158-4165 ◽  
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.

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

Electronics ◽  
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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