A Phishing Webpage Detection Method Based on Stacked Autoencoder and Correlation Coefficients

Phishing is a kind of cyber-attack that targets naive online users by tricking them into revealing sensitive information. There are many anti-phishing solutions proposed to date, such as blacklist or whitelist, heuristic-based and machine learning-based methods. However, online users are still being trapped into revealing sensitive information in phishing websites. In this paper, we propose a novel phishing webpage detection model, based on features that are extracted from URL, source codes of HTML, and the third-party services to represent the basic characters of phishing webpages, which uses a deep learning method – Stacked Autoencoder (SAE) to detect phishing webpages. To make features in the same order of magnitude, three kinds of normalization methods are adopted. In particular, a method to calculate correlation coefficients between weight matrixes of SAE is proposed to determine optimal width of hidden layers, which shows high computational efficiency and feasibility. Based on the testing of a set of phishing and benign webpages, the model using SAE achieves the best performance when compared to other algorithms such as Naive Bayes (NB), Support Vector Machine (SVM), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). It indicates that the proposed detection model is promising and can be applied effectively to phishing detection

Dimensionality Reduction and Dynamical Mode Recognition of Circular Arrays of Flame Oscillators Using Deep Neural Network

Oscillatory combustion in aero engines and modern gas turbines often has significant adverse effects on their operation, and accurately recognizing various oscillation modes is the prerequisite for understanding and controlling combustion instability. However, the high-dimensional spatial-temporal data of a complex combustion system typically poses considerable challenges to the dynamical mode recognition. Based on a two-layer bidirectional long short-term memory variational autoencoder (Bi-LSTM-VAE) dimensionality reduction model and a two-dimensional Wasserstein distance-based classifier (WDC), this study proposes a promising method (Bi-LSTM-VAE-WDC) for recognizing dynamical modes in oscillatory combustion systems. Specifically, the Bi-LSTM-VAE dimension reduction model was introduced to reduce the high-dimensional spatial-temporal data of the combustion system to a low-dimensional phase space; Gaussian kernel density estimates (GKDE) were computed based on the distribution of phase points in a grid; two-dimensional WD values were calculated from the GKDE maps to recognize the oscillation modes. The time-series data used in this study were obtained from numerical simulations of circular arrays of laminar flame oscillators. The results show that the novel Bi-LSTM-VAE method can produce a non-overlapping distribution of phase points, indicating an effective unsupervised mode recognition and classification. Furthermore, the present method exhibits a more prominent performance than VAE and PCA (principal component analysis) for distinguishing dynamical modes in complex flame systems, implying its potential in studying turbulent combustion.Comment: research paper (18 pages, 1 table 10 figures) with supplementary material (8 pages, 1 table, 5 figures

Integrating GAN and Texture Synthesis for Enhanced Road Damage Detection

In the domain of traffic safety and road maintenance, precise detection of road damage is crucial for ensuring safe driving and prolonging road durability. However, current methods often fall short due to limited data. Prior attempts have used Generative Adversarial Networks to generate damage with diverse shapes and manually integrate it into appropriate positions. However, the problem has not been well explored and is faced with two challenges. First, they only enrich the location and shape of damage while neglect the diversity of severity levels, and the realism still needs further improvement. Second, they require a significant amount of manual effort. To address these challenges, we propose an innovative approach. In addition to using GAN to generate damage with various shapes, we further employ texture synthesis techniques to extract road textures. These two elements are then mixed with different weights, allowing us to control the severity of the synthesized damage, which are then embedded back into the original images via Poisson blending. Our method ensures both richness of damage severity and a better alignment with the background. To save labor costs, we leverage structural similarity for automated sample selection during embedding. Each augmented data of an original image contains versions with varying severity levels. We implement a straightforward screening strategy to mitigate distribution drift. Experiments are conducted on a public road damage dataset. The proposed method not only eliminates the need for manual labor but also achieves remarkable enhancements, improving the mAP by 4.1% and the F1-score by 4.5%.Comment: 10 pages, 13 figures, 2 Table

Deformable Voxel Grids for Shape Comparisons

We present Deformable Voxel Grids (DVGs) for 3D shapes comparison and processing. It consists of a voxel grid which is deformed to approximate the silhouette of a shape, via energy-minimization. By interpreting the DVG as a local coordinates system, it provides a better embedding space than a regular voxel grid, since it is adapted to the geometry of the shape. It also allows to deform the shape by moving the control points of the DVG, in a similar manner to the Free Form Deformation, but with easier interpretability of the control points positions. After proposing a computation scheme of the energies compatible with meshes and pointclouds, we demonstrate the use of DVGs in a variety of applications: correspondences via cubification, style transfer, shape retrieval and PCA deformations. The first two require no learning and can be readily run on any shapes in a matter of minutes on modest hardware. As for the last two, they require to first optimize DVGs on a collection of shapes, which amounts to a pre-processing step. Then, determining PCA coordinates is straightforward and brings a few parameters to deform a shape

A comprehensive survey on generative adversarial networks

Generative Adversarial Networks (GANs) are a class of neural network architectures that have been used to generate a wide variety of realistic data, including images, videos, and audio. GANs consist of two main components: a generator network, which produces new data, and a discriminator network, which attempts to distinguish the generated data from real data. The two networks are trained in a competitive manner, with the generator trying to produce data that can fool the discriminator, and the discriminator trying to correctly identify the generated data. Since their introduction in 2014, GANs have been applied to a wide range of tasks, such as image synthesis, image-to-image translation, and text-to-image synthesis. GANs have also been used in various fields such as computer vision, natural language processing, and speech recognition. Despite their success, GANs have several limitations and challenges, including mode collapse, where the generator produces only a limited number of distinct samples, and instability during training. Several methods have been proposed to address these challenges, including regularization techniques, architectural modifications, and alternative training algorithms. Overall, GANs have proven to be a powerful tool for generating realistic data, and research on GANs is an active area of study in the field of machine learning. This survey paper aims to provide an overview of the GANs architecture and its variants, applications and challenges, and the recent developments in GANs

Weather Image Generation using a Generative Adversarial Network

This thesis will inspect, if coupling a simple U-Net segmentation model with an image-to-image transformation Generative Adversarial Network, CycleGAN, will improve data augmentation result compared to sole CycleGAN. To evaluate the proposed method, a dataset consisting of weather images of different weather conditions and corresponding segmentation masks is used. Furthermore, we investigate the performance of different pre-trained CNNs in the encoder part of the U-Net model. The main goal is to provide a solution for generating data to be used in future data augmentation projects for real applications. Images of the proposed segmentation and CycleGAN model pipeline will be evaluated with Fréchet Inception Distance metric, and compared to sole CycleGAN results. The results indicate that there is an increase in generated image quality by coupling a segmentation model with a generator of CycleGAN, at least with the used dataset. Additional improvements might be achieved by adding an attention model to the pipeline or changing the segmentation or generative adversarial network model architectures.Tämä tutkielma selvittää, tuottaako yksinkertaisen U-Net segmentaatiomallin yhdistäminen kuvasta-kuvaan generatiiviseen vastakkaisverkkoon, CycleGANiin, parempia tuloksia kuin pelkkä CycleGAN. Esitetyn ratkaisun arvioimiseksi käytetään sääkuvista ja niitä vastaavista segmentaatioleimoista koostuvaa datasettiä. Lisäksi tutkimme, paljonko eroavaisuuksia esiopetetuilla CNN:llä on U-Net arkkitehtuurin enkooderissa suorituskyvyn osalta. Tutkielman päätavoite on tuottaa ratkaisu uuden datan generoimiseksi reaalimailman sovelluskohteisiin. Ehdotetun segmentaatio- ja CycleGAN-mallista koostuvan liukuhihnan suorituskyky arvioidaan Fréchetin aloitusetäisyys-menetelmällä, jota myös verrataan pelkällä CycleGANilla saatuihin tuloksiin. Tutkielman tulokset implikoivat, että kuvanlaatu nousee esitettyä liukuhihnaa käyttämällä ainakin kyseessä olevalla datasetillä. Lisäparannuksia voi saada aikaan liukuhihnaan erillisen huomiomallin tai muuttamalla segmentaatio- tai generatiivisen vastakkaisverkon arkkitehtuuri

Learning and inference with Wasserstein metrics

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2018.Cataloged from PDF version of thesis.Includes bibliographical references (pages 131-143).This thesis develops new approaches for three problems in machine learning, using tools from the study of optimal transport (or Wasserstein) distances between probability distributions. Optimal transport distances capture an intuitive notion of similarity between distributions, by incorporating the underlying geometry of the domain of the distributions. Despite their intuitive appeal, optimal transport distances are often difficult to apply in practice, as computing them requires solving a costly optimization problem. In each setting studied here, we describe a numerical method that overcomes this computational bottleneck and enables scaling to real data. In the first part, we consider the problem of multi-output learning in the presence of a metric on the output domain. We develop a loss function that measures the Wasserstein distance between the prediction and ground truth, and describe an efficient learning algorithm based on entropic regularization of the optimal transport problem. We additionally propose a novel extension of the Wasserstein distance from probability measures to unnormalized measures, which is applicable in settings where the ground truth is not naturally expressed as a probability distribution. We show statistical learning bounds for both the Wasserstein loss and its unnormalized counterpart. The Wasserstein loss can encourage smoothness of the predictions with respect to a chosen metric on the output space. We demonstrate this property on a real-data image tagging problem, outperforming a baseline that doesn't use the metric. In the second part, we consider the probabilistic inference problem for diffusion processes. Such processes model a variety of stochastic phenomena and appear often in continuous-time state space models. Exact inference for diffusion processes is generally intractable. In this work, we describe a novel approximate inference method, which is based on a characterization of the diffusion as following a gradient flow in a space of probability densities endowed with a Wasserstein metric. Existing methods for computing this Wasserstein gradient flow rely on discretizing the underlying domain of the diffusion, prohibiting their application to problems in more than several dimensions. In the current work, we propose a novel algorithm for computing a Wasserstein gradient flow that operates directly in a space of continuous functions, free of any underlying mesh. We apply our approximate gradient flow to the problem of filtering a diffusion, showing superior performance where standard filters struggle. Finally, we study the ecological inference problem, which is that of reasoning from aggregate measurements of a population to inferences about the individual behaviors of its members. This problem arises often when dealing with data from economics and political sciences, such as when attempting to infer the demographic breakdown of votes for each political party, given only the aggregate demographic and vote counts separately. Ecological inference is generally ill-posed, and requires prior information to distinguish a unique solution. We propose a novel, general framework for ecological inference that allows for a variety of priors and enables efficient computation of the most probable solution. Unlike previous methods, which rely on Monte Carlo estimates of the posterior, our inference procedure uses an efficient fixed point iteration that is linearly convergent. Given suitable prior information, our method can achieve more accurate inferences than existing methods. We additionally explore a sampling algorithm for estimating credible regions.by Charles Frogner.Ph. D

Mapping of multiple muscles with transcranial magnetic stimulation: Absolute and relative test-retest reliability

The spatial accuracy of transcranial magnetic stimulation (TMS) may be as small as a few millimeters. Despite such great potential, navigated TMS (nTMS) mapping is still underused for the assessment of motor plasticity, particularly in clinical settings. Here, we investigate the within‐limb somatotopy gradient as well as absolute and relative reliability of three hand muscle cortical representations (MCRs) using a comprehensive grid‐based sulcus‐informed nTMS motor mapping. We enrolled 22 young healthy male volunteers. Two nTMS mapping sessions were separated by 5–10 days. Motor evoked potentials were obtained from abductor pollicis brevis (APB), abductor digiti minimi, and extensor digitorum communis. In addition to individual MRI‐based analysis, we studied normalized MNI MCRs. For the reliability assessment, we calculated intraclass correlation and the smallest detectable change. Our results revealed a somatotopy gradient reflected by APB MCR having the most lateral location. Reliability analysis showed that the commonly used metrics of MCRs, such as areas, volumes, centers of gravity (COGs), and hotspots had a high relative and low absolute reliability for all three muscles. For within‐limb TMS somatotopy, the most common metrics such as the shifts between MCR COGs and hotspots had poor relative reliability. However, overlaps between different muscle MCRs were highly reliable. We, thus, provide novel evidence that inter‐muscle MCR interaction can be reliably traced using MCR overlaps while shifts between the COGs and hotspots of different MCRs are not suitable for this purpose. Our results have implications for the interpretation of nTMS motor mapping results in healthy subjects and patients with neurological conditions

Deep Risk Prediction and Embedding of Patient Data: Application to Acute Gastrointestinal Bleeding

Acute gastrointestinal bleeding is a common and costly condition, accounting for over 2.2 million hospital days and 19.2 billion dollars of medical charges annually. Risk stratification is a critical part of initial assessment of patients with acute gastrointestinal bleeding. Although all national and international guidelines recommend the use of risk-assessment scoring systems, they are not commonly used in practice, have sub-optimal performance, may be applied incorrectly, and are not easily updated. With the advent of widespread electronic health record adoption, longitudinal clinical data captured during the clinical encounter is now available. However, this data is often noisy, sparse, and heterogeneous. Unsupervised machine learning algorithms may be able to identify structure within electronic health record data while accounting for key issues with the data generation process: measurements missing-not-at-random and information captured in unstructured clinical note text. Deep learning tools can create electronic health record-based models that perform better than clinical risk scores for gastrointestinal bleeding and are well-suited for learning from new data. Furthermore, these models can be used to predict risk trajectories over time, leveraging the longitudinal nature of the electronic health record. The foundation of creating relevant tools is the definition of a relevant outcome measure; in acute gastrointestinal bleeding, a composite outcome of red blood cell transfusion, hemostatic intervention, and all-cause 30-day mortality is a relevant, actionable outcome that reflects the need for hospital-based intervention. However, epidemiological trends may affect the relevance and effectiveness of the outcome measure when applied across multiple settings and patient populations. Understanding the trends in practice, potential areas of disparities, and value proposition for using risk stratification in patients presenting to the Emergency Department with acute gastrointestinal bleeding is important in understanding how to best implement a robust, generalizable risk stratification tool. Key findings include a decrease in the rate of red blood cell transfusion since 2014 and disparities in access to upper endoscopy for patients with upper gastrointestinal bleeding by race/ethnicity across urban and rural hospitals. Projected accumulated savings of consistent implementation of risk stratification tools for upper gastrointestinal bleeding total approximately $1 billion 5 years after implementation. Most current risk scores were designed for use based on the location of the bleeding source: upper or lower gastrointestinal tract. However, the location of the bleeding source is not always clear at presentation. I develop and validate electronic health record based deep learning and machine learning tools for patients presenting with symptoms of acute gastrointestinal bleeding (e.g., hematemesis, melena, hematochezia), which is more relevant and useful in clinical practice. I show that they outperform leading clinical risk scores for upper and lower gastrointestinal bleeding, the Glasgow Blatchford Score and the Oakland score. While the best performing gradient boosted decision tree model has equivalent overall performance to the fully connected feedforward neural network model, at the very low risk threshold of 99% sensitivity the deep learning model identifies more very low risk patients. Using another deep learning model that can model longitudinal risk, the long-short-term memory recurrent neural network, need for transfusion of red blood cells can be predicted at every 4-hour interval in the first 24 hours of intensive care unit stay for high risk patients with acute gastrointestinal bleeding. Finally, for implementation it is important to find patients with symptoms of acute gastrointestinal bleeding in real time and characterize patients by risk using available data in the electronic health record. A decision rule-based electronic health record phenotype has equivalent performance as measured by positive predictive value compared to deep learning and natural language processing-based models, and after live implementation appears to have increased the use of the Acute Gastrointestinal Bleeding Clinical Care pathway. Patients with acute gastrointestinal bleeding but with other groups of disease concepts can be differentiated by directly mapping unstructured clinical text to a common ontology and treating the vector of concepts as signals on a knowledge graph; these patients can be differentiated using unbalanced diffusion earth mover’s distances on the graph. For electronic health record data with data missing not at random, MURAL, an unsupervised random forest-based method, handles data with missing values and generates visualizations that characterize patients with gastrointestinal bleeding. This thesis forms a basis for understanding the potential for machine learning and deep learning tools to characterize risk for patients with acute gastrointestinal bleeding. In the future, these tools may be critical in implementing integrated risk assessment to keep low risk patients out of the hospital and guide resuscitation and timely endoscopic procedures for patients at higher risk for clinical decompensation