arXiv subjects

David Rios Insua

Publications and source records attributed to David Rios Insua.

14 recordsLinked to original sources

arXiv2025

Incentivising Personalised Colorectal Cancer Screening: an Adversarial Risk Analysis Approach

This paper presents a framework for incentivising colorectal cancer (CRC) screening programs from the perspective of policymakers and under the assumption that the citizens participating in the program have misaligned objectives. To do so, it leverages tools from adversarial risk analysis to propose an optimal incentive scheme under uncertainty. The work relies on previous work on modeling CRC risk and optimal screening strategies and provides use cases regarding individual and group-based optimal incentives based on a simple financial scheme.

stat.AP↗

arXiv2025

Computational adversarial risk analysis for general security games

This paper provides an efficient computational scheme to handle general security games from an adversarial risk analysis perspective. Two cases in relation to single-stage and multi-stage simultaneous defend-attack games motivate our approach to general setups which uses bi-agent influence diagrams as underlying problem structure and augmented probability simulation as core computational methodology. Theoretical convergence and numerical, modeling, and implementation issues are thoroughly discussed. A disinformation war case study illustrates the relevance of the proposed approach.

cs.GT↗

arXiv2024

Adversarial Machine Learning: Bayesian Perspectives

Adversarial Machine Learning (AML) is emerging as a major field aimed at protecting machine learning (ML) systems against security threats: in certain scenarios there may be adversaries that actively manipulate input data to fool learning systems. This creates a new class of security vulnerabilities that ML systems may face, and a new desirable property called adversarial robustness essential to trust operations based on ML outputs. Most work in AML is built upon a game-theoretic modelling of the conflict between a learning system and an adversary, ready to manipulate input data. This assumes that each agent knows their opponent's interests and uncertainty judgments, facilitating inferences based on Nash equilibria. However, such common knowledge assumption is not realistic in the security scenarios typical of AML. After reviewing such game-theoretic approaches, we discuss the benefits that Bayesian perspectives provide when defending ML-based systems. We demonstrate how the Bayesian approach allows us to explicitly model our uncertainty about the opponent's beliefs and interests, relaxing unrealistic assumptions, and providing more robust inferences. We illustrate this approach in supervised learning settings, and identify relevant future research problems.

cs.AI↗

arXiv2024

Forecasting Adversarial Actions Using Judgment Decomposition-Recomposition

In domains such as homeland security, cybersecurity and competitive marketing, it is frequently the case that analysts need to forecast adversarial actions that impact the problem of interest. Standard structured expert judgement elicitation techniques may fall short as they do not explicitly take into account intentionality. We present a decomposition technique based on adversarial risk analysis followed by a behavioral recomposition using discrete choice models that facilitate such elicitation process and illustrate its performance through behavioral experiments.

stat.ME↗

arXiv2024

A Cybersecurity Risk Analysis Framework for Systems with Artificial Intelligence Components

The introduction of the European Union Artificial Intelligence Act, the NIST Artificial Intelligence Risk Management Framework, and related norms demands a better understanding and implementation of novel risk analysis approaches to evaluate systems with Artificial Intelligence components. This paper provides a cybersecurity risk analysis framework that can help assessing such systems. We use an illustrative example concerning automated driving systems.

cs.AI↗

arXiv2020

Protecting Classifiers From Attacks

In multiple domains such as malware detection, automated driving systems, or fraud detection, classification algorithms are susceptible to being attacked by malicious agents willing to perturb the value of instance covariates to pursue certain goals. Such problems pertain to the field of adversarial machine learning and have been mainly dealt with, perhaps implicitly, through game-theoretic ideas with strong underlying common knowledge assumptions. These are not realistic in numerous application domains in relation to security and business competition. We present an alternative Bayesian decision theoretic framework that accounts for the uncertainty about the attacker's behavior using adversarial risk analysis concepts. In doing so, we also present core ideas in adversarial machine learning to a statistical audience. A key ingredient in our framework is the ability to sample from the distribution of originating instances given the, possibly attacked, observed ones. We propose an initial procedure based on approximate Bayesian computation usable during operations; within it, we simulate the attacker's problem taking into account our uncertainty about his elements. Large-scale problems require an alternative scalable approach implementable during the training stage. Globally, we are able to robustify statistical classification algorithms against malicious attacks.

stat.ML↗

arXiv2020

Stochastic Gradient MCMC with Repulsive Forces

We propose a unifying view of two different Bayesian inference algorithms, Stochastic Gradient Markov Chain Monte Carlo (SG-MCMC) and Stein Variational Gradient Descent (SVGD), leading to improved and efficient novel sampling schemes. We show that SVGD combined with a noise term can be framed as a multiple chain SG-MCMC method. Instead of treating each parallel chain independently from others, our proposed algorithm implements a repulsive force between particles, avoiding collapse and facilitating a better exploration of the parameter space. We also show how the addition of this noise term is necessary to obtain a valid SG-MCMC sampler, a significant difference with SVGD. Experiments with both synthetic distributions and real datasets illustrate the benefits of the proposed scheme.

stat.ML↗

arXiv2020

Variationally Inferred Sampling Through a Refined Bound for Probabilistic Programs

A framework to boost the efficiency of Bayesian inference in probabilistic programs is introduced by embedding a sampler inside a variational posterior approximation. We call it the refined variational approximation. Its strength lies both in ease of implementation and automatically tuning of the sampler parameters to speed up mixing time using automatic differentiation. Several strategies to approximate \emph{evidence lower bound} (ELBO) computation are introduced. Experimental evidence of its efficient performance is shown solving an influence diagram in a high-dimensional space using a conditional variational autoencoder (cVAE) as a deep Bayes classifier; an unconditional VAE on density estimation tasks; and state-space models for time-series data.

cs.LG↗

arXiv2019

Insider threat modeling: An adversarial risk analysis approach

Insider threats entail major security issues in geopolitics, cyber risk management and business organization. The game theoretic models proposed so far do not take into account some important factors such as the organisational culture and whether the attacker was detected or not. They also fail to model the defensive mechanisms already put in place by an organisation to mitigate an insider attack. We propose two new models which incorporate these settings and hence are more realistic. %Most earlier work in the field has focused on %standard game theoretic approaches to find the solutions. We use the adversarial risk analysis (ARA) approach to find the solution to our models. ARA does not assume common knowledge and solves the problem from the point of view of one of the players, taking into account their knowledge and uncertainties regarding the choices available to them, to their adversaries, the possible outcomes, their utilities and their opponents' utilities. Our models and the ARA solutions are general and can be applied to most insider threat scenarios. A data security example illustrates the discussion.

cs.CR↗

arXiv2019

Protecting from Malware Obfuscation Attacks through Adversarial Risk Analysis

Malware constitutes a major global risk affecting millions of users each year. Standard algorithms in detection systems perform insufficiently when dealing with malware passed through obfuscation tools. We illustrate this studying in detail an open source metamorphic software, making use of a hybrid framework to obtain the relevant features from binaries. We then provide an improved alternative solution based on adversarial risk analysis which we illustrate describe with an example.

cs.CR↗

arXiv2019

Opponent Aware Reinforcement Learning

In certain reinforcement learning (RL) scenarios there are adversaries trying to interfere with the underlying reward process for their own benefit. We introduce Threatened Markov Decision Processes (TMDPs) as a framework to support an agent against potential opponents in an RL context as well as schemes resulting in novel learning approaches to deal with TMDPs. After introducing our framework and deriving theoretical results, empirical evidence is given via extensive experiments, showing the importance for an RL agent of acknowledging adversarial awareness.

cs.LG↗

arXiv2019

Reinforcement Learning under Threats

In several reinforcement learning (RL) scenarios, mainly in security settings, there may be adversaries trying to interfere with the reward generating process. In this paper, we introduce Threatened Markov Decision Processes (TMDPs), which provide a framework to support a decision maker against a potential adversary in RL. Furthermore, we propose a level-$k$ thinking scheme resulting in a new learning framework to deal with TMDPs. After introducing our framework and deriving theoretical results, relevant empirical evidence is given via extensive experiments, showing the benefits of accounting for adversaries while the agent learns.

cs.LG↗

arXiv2019

An Adversarial Risk Analysis Framework for Cybersecurity

Cyber threats affect all kinds of organisations. Risk analysis is an essential methodology for cybersecurity as it allows organisations to deal with the cyber threats potentially affecting them, prioritise the defence of their assets and decide what security controls should be implemented. Many risk analysis methods are present in cybersecurity models, compliance frameworks and international standards. However, most of them employ risk matrices, which suffer shortcomings that may lead to suboptimal resource allocations. We propose a comprehensive framework for cybersecurity risk analysis, covering the presence of both adversarial and non-intentional threats and the use of insurance as part of the security portfolio. A case study illustrating the proposed framework is presented, serving as template for more complex cases.

cs.CR↗

arXiv2014

A Graphical Adversarial Risk Analysis Model for Oil and Gas Drilling Cybersecurity

Oil and gas drilling is based, increasingly, on operational technology, whose cybersecurity is complicated by several challenges. We propose a graphical model for cybersecurity risk assessment based on Adversarial Risk Analysis to face those challenges. We also provide an example of the model in the context of an offshore drilling rig. The proposed model provides a more formal and comprehensive analysis of risks, still using the standard business language based on decisions, risks, and value.

cs.CR↗