scholarly journals Perspectives on Adversarial Classification

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1957
Author(s):  
David Rios Insua ◽  
Roi Naveiro ◽  
Victor Gallego
Keyword(s):  
Machine Learning ◽  
Risk Analysis ◽  
Common Knowledge ◽  
Adversarial Classification ◽  
Game Theoretic ◽  
Classical Game

Adversarial classification (AC) is a major subfield within the increasingly important domain of adversarial machine learning (AML). So far, most approaches to AC have followed a classical game-theoretic framework. This requires unrealistic common knowledge conditions untenable in the security settings typical of the AML realm. After reviewing such approaches, we present alternative perspectives on AC based on adversarial risk analysis.

scholarly journals Perspectives on Adversarial Classification

2020 ◽  
Author(s):  
David Rios Insua ◽  
Roi Naveiro ◽  
Victor Gallego
Keyword(s):  
Machine Learning ◽  
Risk Analysis ◽  
Common Knowledge ◽  
Adversarial Classification ◽  
Game Theoretic ◽  
Classical Game

Adversarial Classification (AC) is a major subfield within the increasingly important domain of adversarial machine learning (AML). Most approaches to AC so far have followed a classical game-theoretic framework. This requires unrealistic common knowledge conditions untenable in the security settings typical of the AML realm. After reviewing such approaches, we present alternative perspectives on AC based on Adversarial Risk Analysis.

scholarly journals An Adversarial-Risk-Analysis Approach to Counterterrorist Online Surveillance

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 480 ◽  
Author(s):  
César Gil ◽  
Javier Parra-Arnau
Keyword(s):  
Risk Analysis ◽  
Common Knowledge ◽  
Satisfactory Performance ◽  
Detection Technology ◽  
Proposed Model ◽  
Powerful Means ◽  
The Status ◽  
Game Theoretic ◽  
Better Than

The Internet, with the rise of the IoT, is one of the most powerful means of propagating a terrorist threat, and at the same time the perfect environment for deploying ubiquitous online surveillance systems.This paper tackles the problem of online surveillance, which we define as the monitoring by a security agency of a set of websites through tracking and classification of profiles that are potentially suspected of carrying out terrorist attacks. We conduct a theoretical analysis in this scenario that investigates the introduction of automatic classification technology compared to the status quo involving manual investigation of the collected profiles. Our analysis starts examining the suitability of game-theoretic-based models for decision-making in the introduction of this technology. We propose an adversarial-risk-analysis (ARA) model as a novel way of approaching the online surveillance problem that has the advantage of discarding the hypothesis of common knowledge. The proposed model allows us to study the rationality conditions of the automatic suspect detection technology, determining under which circumstances it is better than the traditional human-based approach. Our experimental results show the benefits of the proposed model. Compared to standard game theory, our ARA-based model indicates in general greater prudence in the deployment of the automatic technology and exhibits satisfactory performance without having to relax crucial hypotheses such as common knowledge and therefore subtracting realism from the problem, although at the expense of higher computational complexity.

scholarly journals Validating Game-Theoretic Models of Terrorism: Insights from Machine Learning

Games ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 54
Author(s):  
James T. Bang ◽  
Atin Basuchoudhary ◽  
Aniruddha Mitra
Keyword(s):  
Machine Learning ◽  
Terror Attacks ◽  
Nonlinear Relationships ◽  
Game Theoretic ◽  
Competing Models

There are many competing game-theoretic analyses of terrorism. Most of these models suggest nonlinear relationships between terror attacks and some variable of interest. However, to date, there have been very few attempts to empirically sift between competing models of terrorism or identify nonlinear patterns. We suggest that machine learning can be an effective way of undertaking both. This feature can help build more salient game-theoretic models to help us understand and prevent terrorism.

Risk assessment for health insurance using equation modeling and machine learning

2021 ◽  
Vol 25 (2) ◽  
pp. 201-225
Author(s):  
Amrik Singh ◽  
K.R. Ramkumar
Keyword(s):  
Machine Learning ◽  
Health Insurance ◽  
Risk Analysis ◽  
Real Life ◽  
Equation Model ◽  
Least Square ◽  
Parameter Analysis ◽  
Risk Indicators ◽  
Equation Modeling ◽  
Pairwise Correlation

Due to the advancement of medical sensor technologies new vectors can be added to the health insurance packages. Such medical sensors can help the health as well as the insurance sector to construct mathematical risk equation models with parameters that can map the real-life risk conditions. In this paper parameter analysis in terms of medical relevancy as well in terms of correlation has been done. Considering it as ‘inverse problem’ the mathematical relationship has been found and are tested against the ground truth between the risk indicators. The pairwise correlation analysis gives a stable mathematical equation model can be used for health risk analysis. The equation gives coefficient values from which classification regarding health insurance risk can be derived and quantified. The Logistic Regression equation model gives the maximum accuracy (86.32%) among the Ridge Bayesian and Ordinary Least Square algorithms. Machine learning algorithm based risk analysis approach was formulated and the series of experiments show that K-Nearest Neighbor classifier has the highest accuracy of 93.21% to do risk classification.

scholarly journals Model of information deterrence between states based on the theory of reflexive games

2020 ◽  
Author(s):  
V. A. Savchenko ◽  
◽  
T. M. Dzyuba
Keyword(s):  
State Level ◽  
Soviet Period ◽  
Theoretic Approach ◽  
State Behavior ◽  
Proposed Model ◽  
Game Theoretic ◽  
Pragmatic Aspects ◽  
Classical Game ◽  
Game Theoretic Approach ◽  
Post Soviet Period

The article considers the approach to modeling the processes of information counteraction and information deterrence at the state level. The shortcomings of the game-theoretic approach to the development of formalized models of information counteraction are identified. The concept of formalization of interstate information deterrence on the basis of the theory of reflexive management of V. Lefevre is offered. Unlike classical game theory, this approach takes into account the possible irrationality of human (state) behavior in combination with moral-motivational and pragmatic aspects of choice. The adequacy of the proposed model is confirmed by the analysis of examples of information confrontation between Ukraine and Russia in the post-Soviet period.

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