In this paper we propose a new access control mechanism, Dynamic Sensitivity-Based Access Control (DSBAC), designed to regulate users' access to sensitive data stored in relational databases. The DSBAC is an extension of the basic mandatory access control (MAC) mechanism, and it uses the M-score (Misuseability score) measure in order to assign, dynamically, an access class to each set of tuples.
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Detecting and preventing data leakage and data misuse poses a serious challenge for organizations, especially when dealing with insiders with legitimate permissions to access the organization's systems and its critical data. In this paper, we present a new concept, Misuseability Weight, for estimating the risk emanating from data exposed to insiders. This concept focuses on assigning a score that represents the sensitivity level of the data exposed to the user and by that predicts the ability of the user to maliciously exploit this data. Then, we propose a new measure, the M-score, which assigns a misuseability weight to tabular data, discuss some of its properties, and demonstrate its usefulness in several leakage scenarios. One of the main challenges in applying the M-score measure is in acquiring the required knowledge from a domain expert. Therefore, we present and evaluate two approaches toward eliciting misuseability conceptions from the domain expert.
We present a novel approach for the construction of ensemble classifiers based on dimensionality reduction. Dimensionality reduction methods represent datasets using a small number of attributes while preserving the information conveyed by the original dataset. The ensemble members are trained based on dimension-reduced versions of the training set. These versions are obtained by applying dimensionality reduction to the original training set using different values of the input parameters. This construction meets both the diversity and accuracy criteria which are required to construct an ensemble classifier where the former criterion is obtained by the various input parameter values and the latter is achieved due to the decorrelation and noise reduction properties of dimensionality reduction. In order to classify a test sample, it is first embedded into the dimension reduced space of each individual classifier by using an out-of-sample extension algorithm. Each classifier is then applied to the embedded sample and the classification is obtained via a voting scheme. We present three variations of the proposed approach based on the Random Projections, the Diffusion Maps and the Random Subspaces dimensionality reduction algorithms. We also present a multi-strategy ensemble which combines AdaBoost and Diffusion Maps. A comparison is made with the Bagging, AdaBoost, Rotation Forest ensemble classifiers and also with the base classifier which does not incorporate dimensionality reduction. Our experiments used seventeen benchmark datasets from the UCI repository. The results obtained by the proposed algorithms were superior in many cases to other algorithms.
Motivation High-resolution microbial strain typing is essential for various clinical purposes, including disease outbreak investigation, tracking of microbial transmission events and epidemiological surveillance of bacterial infections. The widely used approach for multilocus sequence typing (MLST) that is based on the core genome, cgMLST, has the advantage of a high level of typeability and maximal discriminatory power. Yet, the transition from a seven loci-based scheme to cgMLST involves several challenges, that include the need by some users to maintain backward compatibility, growing difficulties in the day-to-day communication within the microbiology community with respect to nomenclature and ontology, issues with typeability, especially if a more stringent approach to loci presence is used, and computational requirements concerning laboratory data management and sharing with end-users. Hence, methods for optimizing cgMLST schemes through careful reduction of the number of loci are expected to be beneficial for practical needs in different settings. Results We present a new machine learning-based methodology, minMLST, for minimizing the number of genes in cgMLST schemes by identifying subsets of informative genes and analyzing the trade-off between gene reduction and typing performance. The results achieved with minMLST over eight bacterial species show that despite the reduction in the number of genes up to a factor of 10, the typing performance remains very high and significant with an Adjusted Rand Index that ranges between 0.4 and 0.93 in different species and a P-value < 10-3. The identification of such optimized MLST schemes for bacterial strain typing is expected to improve the implementation of cgMLST by improving interlaboratory agreement and communication. Availability and implementation The python package minMLST is available at https://PyPi.org/project/minmlst/PyPI and supported on Linux and Windows. Supplementary information Supplementary data are available at Bioinformatics online.
Over the past few years data leakage and data misuse have become a major concern for organizations. A data leakage or data misuse incident can damage an organization's reputation and brand name as well as compromise the privacy of its customers. Much research has been conducted in order to find a solution to these threats. Most methods are based on anomaly detection that tracks the user's behavior by examining the syntax of SQL queries in order to detect outlier queries. Other methods examine the data retrieved by the query. In this paper, we propose a new concept for analyzing the retrieved data - the Misuseability Weight. This approach focuses on assigning a score that represents the sensitivity level of the data exposed to the user. This measure predicts the ability of a user to exploit the exposed data in a malicious way. We suggest a new measure, the M-score, which assigns a misuseability weight to a table of data, propose some properties of the new measure and demonstrate its usefulness using over several leakage scenarios.
Anomaly detection algorithms are often thought to be limited because they don't facilitate the process of validating results performed by domain experts. In Contrast, deep learning algorithms for anomaly detection, such as autoencoders, point out the outliers, saving experts the time-consuming task of examining normal cases in order to find anomalies. Most outlier detection algorithms output a score for each instance in the database. The top-k most intense outliers are returned to the user for further inspection; however the manual validation of results becomes challenging without additional clues. An explanation of why an instance is anomalous enables the experts to focus their investigation on most important anomalies and may increase their trust in the algorithm. Recently, a game theory-based framework known as SHapley Additive exPlanations (SHAP) has been shown to be effective in explaining various supervised learning models. In this research, we extend SHAP to explain anomalies detected by an autoencoder, an unsupervised model. The proposed method extracts and visually depicts both the features that most contributed to the anomaly and those that offset it. A preliminary experimental study using real world data demonstrates the usefulness of the proposed method in assisting the domain experts to understand the anomaly and filtering out the uninteresting anomalies, aiming at minimizing the false positive rate of detected anomalies.
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Insurance riders are optional addendum to base insurance policies. In this paper we discuss the application of recommender systems to the task of matching riders to clients. This task is difficult because of the variety of possible riders, as well as the poor knowledge of the client over these riders. We focus on call centers where the agent also has limited knowledge and expertise. For such agents, discovering appropriate riders for the current client is very difficult, and automated tools that suggest such riders can play an important role in the agent-client dialogue, and may influence considerably the outcome of the interaction.
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Teratogenic drugs can cause severe fetal malformation and therefore have critical impact on the health of the fetus, yet the teratogenic risks are unknown for most approved drugs. This article proposes an explainable machine learning model for classifying pregnancy drug safety based on multimodal data and suggests an orthogonal ensemble for modeling multimodal data. To train the proposed model, we created a set of labeled drugs by processing over 100 000 textual responses collected by a large teratology information service. Structured textual information is incorporated into the model by applying clustering analysis to textual features.We report an area under the receiver operating characteristic curve (AUC) of 0.891 using cross-validation and an AUC of 0.904 for cross-expert validation. Our findings suggest the safety of two drugs during pregnancy, Varenicline and Mebeverine, and suggest that Meloxicam, an NSAID, is of higher risk; according to existing data, the safety of these three drugs during pregnancy is unknown. We also present a web-based application that enables physicians to examine a specific drug and its risk factors.The code and data is available from https://github.com/goolig/drug_safety_pregnancy_prediction.git.Supplementary data are available at Bioinformatics online.
Existing evaluations measures are insufficient when probabilistic classifiers are used for choosing objects to be included in a limited quota. This paper reviews performance measures that suit probabilistic classification and introduce two novel performance measures that can be used effectively for this task. It then investigates when to use each of the measures and what purpose each one of them serves. The use of these measures is demonstrated on a real life dataset obtained from the human resource field and is validated on set of benchmark datasets.
Research on adversarial evasion attacks primarily focuses on neural network models due to their popularity in fields such as computer vision and natural language processing, as well as their properties that facilitate the search for adversarial examples with minimal input changes. However, decision trees and tree ensembles, widely used for their high performance and interpretability in domains dominated by tabular data, are also vulnerable to adversarial attacks. A significant challenge is that existing defense strategies for tree ensembles often involve modifying the model architecture or retraining the model, which can degrade performance and may not be feasible in practice. In this research, we address the challenging problem of detecting adversarial evasion attacks on decision tree ensembles without altering the original model or compromising its performance. We introduce a novel method that leverages representation learning based on the tree structure to detect adversarial samples effectively. Our approach generates new data representations that enhance the ability to distinguish between normal and adversarial samples. Our analysis shows that this method significantly improves detection rates compared to state-of-the-art techniques and detectors trained on the original dataset representation, providing a practical solution to enhance the security of tree-based models against adversarial attacks.
