A study of security issues of mobile apps in the android platform using machine learning approaches

Mobile app poses both traditional and new potential threats to system security and user privacy. There are malicious apps that may do harm to the system, and there are mis-behaviors of apps, which are reasonable and legal when not abused, yet may lead to real threats otherwise. Moreover, due to the nature of mobile apps, a running app in mobile devices may be only part of the software, and the server side behavior is usually not covered by analysis. Therefore, direct analysis on the app itself may be incomplete and additional sources of information are needed. In this dissertation, we discuss how we can apply machine learning techniques in multiple tasks for security issues in regard of mobile apps in the Android platform. These include malicious apps detection and security risk estimation of apps. Both direct sources of information from the developer of apps and indirect sources of information from user comments are utilized in these tasks. We also propose comparison of these different sources in the task of security risk estimation to point out the necessity of usage of indirect sources in mobile app security tasks

Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images.

Alzheimer's Disease (AD) is a progressive neurodegenerative disease where biomarkers for disease based on pathophysiology may be able to provide objective measures for disease diagnosis and staging. Neuroimaging scans acquired from MRI and metabolism images obtained by FDG-PET provide in-vivo measurements of structure and function (glucose metabolism) in a living brain. It is hypothesized that combining multiple different image modalities providing complementary information could help improve early diagnosis of AD. In this paper, we propose a novel deep-learning-based framework to discriminate individuals with AD utilizing a multimodal and multiscale deep neural network. Our method delivers 82.4% accuracy in identifying the individuals with mild cognitive impairment (MCI) who will convert to AD at 3 years prior to conversion (86.4% combined accuracy for conversion within 1-3 years), a 94.23% sensitivity in classifying individuals with clinical diagnosis of probable AD, and a 86.3% specificity in classifying non-demented controls improving upon results in published literature

Explainable AI in Fintech and Insurtech

The growing application of black-box Artificial Intelligence algorithms in many real-world application is raising the importance of understanding how models make their decision. The research field that aims to look into the inner workings of the black-box and to make predictions more interpretable is referred to as eXplainable Artificial Intelligence (XAI). Over the recent years, the research domain of XAI has seen important contributions and continuous developments, achieving great results with theoretically sound applied methodologies. These achievements enable both industry and regulators to improve on existing models and their supervision; this is done in term of explainability, which is the main purpose of these models, but it also brings new possibilities, namely the employment of eXplainable AI models and their outputs as an intermediate step to new applications, greatly expanding their usefulness beyond explainability of model decisions. This thesis is composed of six chapters: an introduction and a conclusion plus four self contained sections reporting the corresponding papers. Chapter 1 proposes the use of Shapley values in similarity networks and clustering models in order to bring out new pieces of information, useful for classification and analysis of the customer base, in an insurtech setting. In chapter 2 a comparison between SHAP and LIME, two of the most important XAI models, evaluating their parameters attribution methodologies and the information they are capable of include thereof, in italian Small and Medium Enterprises’ Probability of Default (PD) estimation, with balance sheet data as inputs. Chapter 3 introduces the use of Shapley values in feature selection techniques, with the analysis of wrapper and embedded feature selection algorithms and their ability to select relevant features with both raw data and their Shapley values, again in the setting of SME PD estimation. In chapter 4, a new methodology of model selection based on Lorenz Zoonoid is introduced, highlighting similarities with the game-theoretical concept of Shapley values and their variability decomposition attribution to independent variables as well as some advantages in terms of model comparability and standardization. These properties are explored through both a simulated example and the application to a real world dataset, provided by EU-certified rating agency Modefinance.The growing application of black-box Artificial Intelligence algorithms in many real-world application is raising the importance of understanding how models make their decision. The research field that aims to look into the inner workings of the black-box and to make predictions more interpretable is referred to as eXplainable Artificial Intelligence (XAI). Over the recent years, the research domain of XAI has seen important contributions and continuous developments, achieving great results with theoretically sound applied methodologies. These achievements enable both industry and regulators to improve on existing models and their supervision; this is done in term of explainability, which is the main purpose of these models, but it also brings new possibilities, namely the employment of eXplainable AI models and their outputs as an intermediate step to new applications, greatly expanding their usefulness beyond explainability of model decisions. This thesis is composed of six chapters: an introduction and a conclusion plus four self contained sections reporting the corresponding papers. Chapter 1 proposes the use of Shapley values in similarity networks and clustering models in order to bring out new pieces of information, useful for classification and analysis of the customer base, in an insurtech setting. In chapter 2 a comparison between SHAP and LIME, two of the most important XAI models, evaluating their parameters attribution methodologies and the information they are capable of include thereof, in italian Small and Medium Enterprises’ Probability of Default (PD) estimation, with balance sheet data as inputs. Chapter 3 introduces the use of Shapley values in feature selection techniques, with the analysis of wrapper and embedded feature selection algorithms and their ability to select relevant features with both raw data and their Shapley values, again in the setting of SME PD estimation. In chapter 4, a new methodology of model selection based on Lorenz Zoonoid is introduced, highlighting similarities with the game-theoretical concept of Shapley values and their variability decomposition attribution to independent variables as well as some advantages in terms of model comparability and standardization. These properties are explored through both a simulated example and the application to a real world dataset, provided by EU-certified rating agency Modefinance

Scalable and Weakly Supervised Bank Transaction Classification

This paper aims to categorize bank transactions using weak supervision, natural language processing, and deep neural network techniques. Our approach minimizes the reliance on expensive and difficult-to-obtain manual annotations by leveraging heuristics and domain knowledge to train accurate transaction classifiers. We present an effective and scalable end-to-end data pipeline, including data preprocessing, transaction text embedding, anchoring, label generation, discriminative neural network training, and an overview of the system architecture. We demonstrate the effectiveness of our method by showing it outperforms existing market-leading solutions, achieves accurate categorization, and can be quickly extended to novel and composite use cases. This can in turn unlock many financial applications such as financial health reporting and credit risk assessment

Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection.

Feature selection is essential in medical area; however, its process becomes complicated with the presence of censoring which is the unique character of survival analysis. Most survival feature selection methods are based on Cox's proportional hazard model, though machine learning classifiers are preferred. They are less employed in survival analysis due to censoring which prevents them from directly being used to survival data. Among the few work that employed machine learning classifiers, partial logistic artificial neural network with auto-relevance determination is a well-known method that deals with censoring and perform feature selection for survival data. However, it depends on data replication to handle censoring which leads to unbalanced and biased prediction results especially in highly censored data. Other methods cannot deal with high censoring. Therefore, in this article, a new hybrid feature selection method is proposed which presents a solution to high level censoring. It combines support vector machine, neural network, and K-nearest neighbor classifiers using simple majority voting and a new weighted majority voting method based on survival metric to construct a multiple classifier system. The new hybrid feature selection process uses multiple classifier system as a wrapper method and merges it with iterated feature ranking filter method to further reduce features. Two endovascular aortic repair datasets containing 91% censored patients collected from two centers were used to construct a multicenter study to evaluate the performance of the proposed approach. The results showed the proposed technique outperformed individual classifiers and variable selection methods based on Cox's model such as Akaike and Bayesian information criterions and least absolute shrinkage and selector operator in p values of the log-rank test, sensitivity, and concordance index. This indicates that the proposed classifier is more powerful in correctly predicting the risk of re-intervention enabling doctor in selecting patients' future follow-up plan

Classification with Large Sparse Datasets: Convergence Analysis and Scalable Algorithms

Large and sparse datasets, such as user ratings over a large collection of items, are common in the big data era. Many applications need to classify the users or items based on the high-dimensional and sparse data vectors, e.g., to predict the profitability of a product or the age group of a user, etc. Linear classifiers are popular choices for classifying such datasets because of their efficiency. In order to classify the large sparse data more effectively, the following important questions need to be answered. 1. Sparse data and convergence behavior. How different properties of a dataset, such as the sparsity rate and the mechanism of missing data systematically affect convergence behavior of classification? 2. Handling sparse data with non-linear model. How to efficiently learn non-linear data structures when classifying large sparse data? This thesis attempts to address these questions with empirical and theoretical analysis on large and sparse datasets. We begin by studying the convergence behavior of popular classifiers on large and sparse data. It is known that a classifier gains better generalization ability after learning more and more training examples. Eventually, it will converge to the best generalization performance with respect to a given data distribution. In this thesis, we focus on how the sparsity rate and the missing data mechanism systematically affect such convergence behavior. Our study covers different types of classification models, including generative classifier and discriminative linear classifiers. To systematically explore the convergence behaviors, we use synthetic data sampled from statistical models of real-world large sparse datasets. We consider different types of missing data mechanisms that are common in practice. From the experiments, we have several useful observations about the convergence behavior of classifying large sparse data. Based on these observations, we further investigate the theoretical reasons and come to a series of useful conclusions. For better applicability, we provide practical guidelines for applying our results in practice. Our study helps to answer whether obtaining more data or missing values in the data is worthwhile in different situations, which is useful for efficient data collection and preparation. Despite being efficient, linear classifiers cannot learn the non-linear structures such as the low-rankness in a dataset. As a result, its accuracy may suffer. Meanwhile, most non-linear methods such as the kernel machines cannot scale to very large and high-dimensional datasets. The third part of this thesis studies how to efficiently learn non-linear structures in large sparse data. Towards this goal, we develop novel scalable feature mappings that can achieve better accuracy than linear classification. We demonstrate that the proposed methods not only outperform linear classification but is also scalable to large and sparse datasets with moderate memory and computation requirement. The main contribution of this thesis is to answer important questions on classifying large and sparse datasets. On the one hand, we study the convergence behavior of widely used classifiers under different missing data mechanisms; on the other hand, we develop efficient methods to learn the non-linear structures in large sparse data and improve classification accuracy. Overall, the thesis not only provides practical guidance for the convergence behavior of classifying large sparse datasets, but also develops highly efficient algorithms for classifying large sparse datasets in practice