Search Results for author:
Found 8 papers, 1 papers with code
Contextual Chart Generation for Cyber Deception
Honeyfiles are a type of honeypot that mimic real, sensitive documents, creating the illusion of the presence of valuable data.
Multiple Hypothesis Dropout: Estimating the Parameters of Multi-Modal Output Distributions
In many real-world applications, from robotics to pedestrian trajectory prediction, there is a need to predict multiple real-valued outputs to represent several potential scenarios.
DualVAE: Controlling Colours of Generated and Real Images
The disentangled representation is obtained by two novel mechanisms: (i) a dual branch architecture that separates image colour attributes from geometric attributes, and (ii) a new ELBO that trains the combined colour and geometry representations.
Deception for Cyber Defence: Challenges and Opportunities
Deception is rapidly growing as an important tool for cyber defence, complementing existing perimeter security measures to rapidly detect breaches and data theft.
TSM: Measuring the Enticement of Honeyfiles with Natural Language Processing
Honeyfile deployment is a useful breach detection method in cyber deception that can also inform defenders about the intent and interests of intruders and malicious insiders.
Can pre-trained Transformers be used in detecting complex sensitive sentences? -- A Monsanto case study
We experimented with four different categories of documents in the Monsanto dataset and observed that BERT achieves better F2 scores by 24. 13\% to 65. 79\% for GHOST, 30. 14\% to 54. 88\% for TOXIC, 39. 22\% for CHEMI, 53. 57\% for REGUL compared to existing sensitive information detection models.
SchemaDB: Structures in Relational Datasets
In this paper we introduce the SchemaDB data-set; a collection of relational database schemata in both sql and graph formats.
Modelling Direct Messaging Networks with Multiple Recipients for Cyber Deception
Cyber deception is emerging as a promising approach to defending networks and systems against attackers and data thieves.
