{"id":8622,"date":"2023-07-21T10:53:53","date_gmt":"2023-07-21T10:53:53","guid":{"rendered":"https:\/\/komorebi.ai\/?page_id=8622"},"modified":"2023-09-21T14:15:13","modified_gmt":"2023-09-21T14:15:13","slug":"adversarial-machine-learning-bayesian-perspectives","status":"publish","type":"page","link":"https:\/\/komorebi.ai\/es\/adversarial-machine-learning-bayesian-perspectives\/","title":{"rendered":"Adversarial Machine Learning: Bayesian Perspectives"},"content":{"rendered":"\t\t
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Adversarial Machine Learning: Bayesian Perspectives<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Journal of the American Statistical Association<\/span>, 2023<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Abstract<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Adversarial Machine Learning (AML) is a growing field focused on defending Machine Learning (ML) systems from security threats where adversaries manipulate input data to deceive these systems.\u00a0<\/span><\/p>

While most AML work relies on game-theoretic modeling of the conflict between an ML system and its adversary, this approach makes unrealistic assumptions about each party’s knowledge of the other’s intentions and uncertainties.\u00a0\u00a0<\/span><\/p>

This<\/span> paper<\/span> proposes<\/span> a Bayesian approach <\/b>that<\/span> allows<\/span> for better modeling of uncertainties<\/b> regarding an opponent’s beliefs and interests, offering a more realistic and robust defensive method for ML-based systems.\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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A Bayesian approach to enhance defense against security threats in Machine Learning systems, applicable across cybersecurity, unsupervised learning and natural language processing.<\/p><\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Where does it apply?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The Bayesian approach to Adversarial Machine Learning is applicable in enhancing <\/span>cybersecurity <\/b>by protecting machine learning systems from attacks.\u00a0 It can be used in unsupervised learning to improve methods such as clustering, and in natural language processing to create robust language models.\u00a0\u00a0<\/span><\/p>

Other applications include<\/span> fake news detection<\/b>, development of robust algorithms for autonomous driving systems, improving counterfactual inference in observational studies, and making deep neural networks more robust for image classification.\u00a0<\/span><\/p>

In short, it can be applied anywhere that uses machine learning systems for decision-making.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Why does it matters?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Adversarial Machine Learning (AML) matters because it is crucial for <\/span>protecting ML algorithms<\/b>, which are widely used in contemporary technology applications. These systems can be manipulated by adversaries posing security threats, thus, maintaining their robustness against attacks is essential.\u00a0<\/span><\/p>

Moreover, the exploration of this approach can benefit various ML problems, leading to advancements in fields such as unsupervised learning, natural language processing, and reinforcement learning.\u00a0<\/span><\/p>

The framework also has direct applications in defending against fake news, developing robust algorithms, improving observational studies, and enhancing adversarial robustness.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Adversarial Machine Learning: Bayesian Perspectives<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Journal of the American Statistical Association<\/span>, 2023<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tVictor Gallego <\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tChief Research Officer<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Otras publicaciones<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Predicci\u00f3n de Energ\u00eda Fotovoltaica mediante Aprendizaje Autom\u00e1tico utilizando Datos Satelitales<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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F\u00edsica del Pomeron en el LHC<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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El sue\u00f1o autoinformado deficiente se asocia con factores de riesgo de enfermedades cardiovasculares<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Modelos de aprendizaje autom\u00e1tico para mejorar la predicci\u00f3n de la producci\u00f3n de energ\u00eda fotovoltaica en Espa\u00f1a, beneficiando sectores como la energ\u00eda renovable y las finanzas.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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El estudio utiliza la Teor\u00eda de Regge y el intercambio de Pomeron para mejorar la comprensi\u00f3n de las colisiones de hadrones. A largo plazo, influyendo en avances en \u00e1reas como la computaci\u00f3n cu\u00e1ntica, la tecnolog\u00eda m\u00e9dica, la defensa y la energ\u00eda.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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El estudio indica un v\u00ednculo entre el sue\u00f1o deficiente y un mayor riesgo de enfermedades cardiovasculares, enfatizando la necesidad de mejoras en estrategias del sue\u00f1o y pol\u00edticas de salud.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tRenewable energy, Predictive Analytics<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Saber m\u00e1s >><\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Saber m\u00e1s >><\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Saber m\u00e1s >><\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Adversarial Machine Learning: Bayesian Perspectives Journal of the American Statistical Association, 2023 Abstract Adversarial Machine Learning (AML) is a growing field focused on defending Machine Learning (ML) systems from security threats where adversaries manipulate input data to deceive these systems. While most AML work relies on game-theoretic modeling of the conflict between an ML system […]<\/p>\n","protected":false},"author":1,"featured_media":8730,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"yoast_head":"\nAdversarial Machine Learning: Bayesian Perspectives - Komorebi AI<\/title>\n<meta name=\"description\" content=\"Bayesian approach boosts defense against security threats in Machine Learning systems, relevant to cybersecurity and language processing.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/komorebi.ai\/es\/adversarial-machine-learning-bayesian-perspectives\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Adversarial Machine Learning: Bayesian Perspectives - Komorebi AI\" \/>\n<meta property=\"og:description\" content=\"Bayesian approach boosts defense against security threats in Machine Learning systems, relevant to cybersecurity and language processing.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/komorebi.ai\/es\/adversarial-machine-learning-bayesian-perspectives\/\" \/>\n<meta property=\"og:site_name\" content=\"Komorebi AI\" \/>\n<meta property=\"article:modified_time\" content=\"2023-09-21T14:15:13+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/komorebi.ai\/wp-content\/uploads\/2023\/07\/ciberseguridad-2.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"2000\" \/>\n\t<meta property=\"og:image:height\" content=\"1334\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Tiempo de lectura\" \/>\n\t<meta name=\"twitter:data1\" content=\"4 minutos\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/komorebi.ai\/adversarial-machine-learning-bayesian-perspectives\/\",\"url\":\"https:\/\/komorebi.ai\/adversarial-machine-learning-bayesian-perspectives\/\",\"name\":\"Adversarial Machine Learning: Bayesian Perspectives - 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