Security threats should be identified in the early phases of a project so that design solutions can be explored and mitigating requirements specified. In this paper, we present a crowd-sourcing approach for creating Personae non Gratae (PnGs), which model attack goals and techniques of unwanted, potentially malicious users. We present a proof of concept study that takes a diverse collection of potentially redundant PnGs and merges them into a single set. Our approach combines machine learning techniques and visualization. It is illustrated and evaluated using a collection of PnGs collected from undergraduate students for a drone-based rescue scenario. Lessons learned from the proof of concept study are discussed and lay the foundations for ...
This thesis pursues the objective of leveraging social media data and Artificial Intelligence (AI) t...
International audienceThreat modeling is recognized as one of the most important activities in softw...
This thesis looks at how to characterize weaknesses in machine learning models that are used for det...
Security threats should be identified in the early phases of a project so that design solutions can ...
In FY 2016, the research team evaluated Security Cards, STRIDE (Spoofing identity, Tampering with da...
COVID-19 scourge has made it challenging to combat digital crimes due to the complexity of attributi...
Crowd-powered systems have become a popular way to augment the capabilities of automated systems in ...
This article proposes a unified threat landscape for participatory crowd sensing (P-CS) systems. Spe...
Crowd analysis has become an extremely famous research point in the territory of computer vision. Co...
This paper aims to present a tool-supported approach for visualising personas as social goal models,...
The aim of this thesis is to leverage machine learning algorithms to introduce adversarial cloaks, o...
Crowdsourcing systems enable new opportunities for requesters with limited funds to accomplish vario...
We are applying machine learning and crowdsourcing to cybersecurity, with the purpose to develop a t...
This paper describes an open-source project called RATCHET whose goal is to create software that can...
Elizabeth Ditton investigated whether machine learning, specifically clustering algorithms, could be...
This thesis pursues the objective of leveraging social media data and Artificial Intelligence (AI) t...
International audienceThreat modeling is recognized as one of the most important activities in softw...
This thesis looks at how to characterize weaknesses in machine learning models that are used for det...
Security threats should be identified in the early phases of a project so that design solutions can ...
In FY 2016, the research team evaluated Security Cards, STRIDE (Spoofing identity, Tampering with da...
COVID-19 scourge has made it challenging to combat digital crimes due to the complexity of attributi...
Crowd-powered systems have become a popular way to augment the capabilities of automated systems in ...
This article proposes a unified threat landscape for participatory crowd sensing (P-CS) systems. Spe...
Crowd analysis has become an extremely famous research point in the territory of computer vision. Co...
This paper aims to present a tool-supported approach for visualising personas as social goal models,...
The aim of this thesis is to leverage machine learning algorithms to introduce adversarial cloaks, o...
Crowdsourcing systems enable new opportunities for requesters with limited funds to accomplish vario...
We are applying machine learning and crowdsourcing to cybersecurity, with the purpose to develop a t...
This paper describes an open-source project called RATCHET whose goal is to create software that can...
Elizabeth Ditton investigated whether machine learning, specifically clustering algorithms, could be...
This thesis pursues the objective of leveraging social media data and Artificial Intelligence (AI) t...
International audienceThreat modeling is recognized as one of the most important activities in softw...
This thesis looks at how to characterize weaknesses in machine learning models that are used for det...