Statistics Weekly Seminar Series - Fabrizio Ruggeri
Advances in Adversarial Risk Analysis
2:00 pm –
3:00 pm
Hardin Hall - North Wing, HARH 49
Room: HARH 49
3310 Holdrege Street
Lincoln NE 68583-0963
Lincoln NE 68583-0963
Directions: HARH 49 is located in the basement level of Hardin Hall in the North Wing.
Virtual Location:
Zoom
Target Audiences:
Contact:
Department of Statistics, statistics@unl.edu
Abstract:
In the talk I will present some of my recent works in the field of Adversarial Risk Analysis. In the first part I will talk about Adversarial Classification. 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 in search of 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. We present an alternative statistical framework that accounts for the lack of knowledge about the attacker’s behavior using adversarial risk analysis concepts.
In the second part I will discuss about an adversarial risk analysis framework for the software release problem. A major issue in software engineering is the decision of when to release a software product to the market. This problem is complex due to, among other things, the uncertainty surrounding the software quality and its faults, the various costs involved, and the presence of competitors. A general adversarial risk analysis framework is proposed to support a software developer in deciding when to release a product and showcased with an example.
About the Speaker:
Fabrizio Ruggeri is an Italian statistician at the Italian National Research Council in Milano. His work focusses on Bayesian methods, specifically robustness and stochastic process inference. He has done innovative work on the sensitivity of Bayesian methods and incompletely specified priors. He has worked on Bayesian wavelet methods, and on a vast variety of applications to industrial problems. His publications include well over 150 refereed papers and book chapters, as well as five books.
Fabrizio received his B.Sc. in Mathematics at the University of Milan, an M.Sc. in Statistics at Carnegie Mellon University, and his Ph.D. at Duke University.
In the talk I will present some of my recent works in the field of Adversarial Risk Analysis. In the first part I will talk about Adversarial Classification. 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 in search of 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. We present an alternative statistical framework that accounts for the lack of knowledge about the attacker’s behavior using adversarial risk analysis concepts.
In the second part I will discuss about an adversarial risk analysis framework for the software release problem. A major issue in software engineering is the decision of when to release a software product to the market. This problem is complex due to, among other things, the uncertainty surrounding the software quality and its faults, the various costs involved, and the presence of competitors. A general adversarial risk analysis framework is proposed to support a software developer in deciding when to release a product and showcased with an example.
About the Speaker:
Fabrizio Ruggeri is an Italian statistician at the Italian National Research Council in Milano. His work focusses on Bayesian methods, specifically robustness and stochastic process inference. He has done innovative work on the sensitivity of Bayesian methods and incompletely specified priors. He has worked on Bayesian wavelet methods, and on a vast variety of applications to industrial problems. His publications include well over 150 refereed papers and book chapters, as well as five books.
Fabrizio received his B.Sc. in Mathematics at the University of Milan, an M.Sc. in Statistics at Carnegie Mellon University, and his Ph.D. at Duke University.
https://www.mi.imati.cnr.it/fabrizio/
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This event originated in Statistics Seminar.