HoneyCode: Automating Deceptive Software Repositories with Deep Generative Models

We propose HoneyCode, an architecture for the generation of synthetic software repositories for cyber deception. The synthetic repositories have the characteristics of real software, including language features, file names and extensions, but contain no real intellectual property. The fake software can be used as a honeypot or form part of a deceptive environment. Existing approaches to software repository generation lack scalability due to reliance on hand-crafted structures for specific languages. Our approach is language agnostic and learns the underlying representations of repository structures, filenames and file content through a novel Tree Recurrent Network (TRN) and two recurrent networks (RNN) respectively. Each stage of the sequential generation process utilises features from prior steps, which increases the honey repository’s authenticity and consistency. Experiments show TRN generates tree samples that reduce degree mean maximal distance (MMD) by 90-92% and depth MMD by 75-86% to a held out test data set in comparison to recent deep graph generators and a baseline random tree generator. In addition, our RNN models generate convincing filenames with authentic syntax and realistic file content

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. A key property of a honeyfile, enticement, is the extent to which the file can attract an intruder to interact with it. We introduce a novel metric, Topic Semantic Matching (TSM), which uses topic modelling to represent files in the repository and semantic matching in an embedding vector space to compare honeyfile text and topic words robustly. We also present a honeyfile corpus created with different Natural Language Processing (NLP) methods. Experiments show that TSM is effective in inter-corpus comparisons and is a promising tool to measure the enticement of honeyfiles. TSM is the first measure to use NLP techniques to quantify the enticement of honeyfile content that compares the essential topical content of local contexts to honeyfiles and is robust to paraphrasing

DualVAE: Controlling Colours of Generated and Real Images

Colour controlled image generation and manipulation are of interest to artists and graphic designers. Vector Quantised Variational AutoEncoders (VQ-VAEs) with autoregressive (AR) prior are able to produce high quality images, but lack an explicit representation mechanism to control colour attributes. We introduce DualVAE, a hybrid representation model that provides such control by learning disentangled representations for colour and geometry. The geometry is represented by an image intensity mapping that identifies structural features. 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. DualVAE can control the colour of generated images, and recolour existing images by transferring the colour latent representation obtained from an exemplar image. We demonstrate that DualVAE generates images with FID nearly two times better than VQ-GAN on a diverse collection of datasets, including animated faces, logos and artistic landscapes

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. However, despite being relatively cheap to deploy, the generation of realistic content at scale is very costly, due to the fact that rich, interactive deceptive technologies are largely hand-crafted. With recent improvements in Machine Learning, we now have the opportunity to bring scale and automation to the creation of realistic and enticing simulated content. In this work, we propose a framework to automate the generation of email and instant messaging-style group communications at scale. Such messaging platforms within organisations contain a lot of valuable information inside private communications and document attachments, making them an enticing target for an adversary. We address two key aspects of simulating this type of system: modelling when and with whom participants communicate, and generating topical, multi-party text to populate simulated conversation threads. We present the LogNormMix-Net Temporal Point Process as an approach to the first of these, building upon the intensity-free modeling approach of Shchur et al. to create a generative model for unicast and multi-cast communications. We demonstrate the use of fine-tuned, pre-trained language models to generate convincing multi-party conversation threads. A live email server is simulated by uniting our LogNormMix-Net TPP (to generate the communication timestamp, sender and recipients) with the language model, which generates the contents of the multi-party email threads. We evaluate the generated content with respect to a number of realism-based properties, that encourage a model to learn to generate content that will engage the attention of an adversary to achieve a deception outcome

Psychiatric Aspects of Lyme Disease in Children and Adolescents: A Community Epidemiologic Study in Westchester, New York

To date, no community study has examined the psychiatric aspects and or sequelae of Lyme disease (LO) among children. As part of a community epidemiologic study of psychiatric disorders among children ages 9 through 17 in a Lyme endemic county, parents were asked whether their child had ever been diagnosed as having LD, and 10.1% (36/357) responded yes to the LO question. Of the 36, 29 also agreed to take part in a follow-up interview. Sixteen of the 29 children had had physician-diagnosed LO as well as either an erythema migrans rash or a positive serology. Fifteen of these 16 received treatment within I month of symptom onset; none of these 15 children were symptomatic longer than 4 months. Only one child had physical symptoms at the time of the interview; she was not treated until 4 month~ after symptom onset. This child experienced 5 years of mtermittent arthritis, cognitive deficits, emotional problems, severe fatigue, and a deterioration in school performance. Courses of oral antibiotics were at first associated with a good response, followed by a resurgence of symptoms month<; later. The lifetime prevalence of LD by history among children ages 9 through 17 in an endemic area may be at least 44.8/1000. In general, when LD is diagnosed early, it responds well to treatment. Delayed diagnosis and treatment may lead to a chronic course

Prospectus, May 6, 1992

https://spark.parkland.edu/prospectus_1992/1013/thumbnail.jp

Prospectus, April 29, 1991

https://spark.parkland.edu/prospectus_1991/1007/thumbnail.jp

Climate geoengineering: issues of path-dependence and socio-technical lock-in

As academic and policy interest in climate geoengineering grows, the potential irreversibility of technological developments in this domain has been raised as a pressing concern. The literature on socio-technical lock-in and path dependence is illuminating in helping to situate current concerns about climate geoengineering and irreversibility in the context of academic understandings of historical socio-technical development and persistence. This literature provides a wealth of material illustrating the pervasiveness of positive feedbacks of various types (from the discursive to the material) leading to complex socio-technical entanglements which may resist change and become inflexible even in the light of evidence of negative impacts. With regard to climate geoengineering, there are concerns that geoengineering technologies might contribute so-called ‘carbon lock-in’, or become irreversibly ‘locked-in’ themselves. In particular, the scale of infrastructures that geoengineering interventions would require, and the issue of the so-called ‘termination effect’ have been discussed in these terms. Despite the emergent and somewhat ill-defined nature of the field, some authors also suggest that the extant framings of geoengineering in academic and policy literatures may already demonstrate features recognizable as forms of cognitive lock-in, likely to have profound implications for future developments in this area. While the concepts of path-dependence and lock-in are the subject of ongoing academic critique, by drawing analytical attention to these pervasive processes of positive feedback and entanglement, this literature is highly relevant to current debates around geoengineering

Resilience, Hardship and Social Conditions

This document is the Accepted Manuscript version of the following article: Hulya Dagdeviren, Matthew Donoghue, and Markus Promberger, ‘Resilience, Hardship and Social Conditions’, Journal of Social Policy, Vol. 45 (1), pp. 1-20, first published online 21 July 2015. The final, published version is available online at DOI: https://doi.org/10.1017/S004727941500032X © 2015 Cambridge University Press.This paper provides a critical assessment of the term ‘resilience’ – and its highly agent-centric conceptualisation – when applied to how individuals and households respond to hardship. We provide an argument for social conditions to be embedded into the framework of resilience analysis. Drawing on two different perspectives in social theory, namely the structure-agent nexus and path dependency, we aim to demonstrate that the concept of resilience, if understood in isolation from the social conditions within which it may or may not arise, can result in a number of problems. This includes misidentification of resilience, ideological exploitation of the term and inability to explain intermittence in resilience.Peer reviewedFinal Accepted Versio

Prospectus, December 9, 1991

https://spark.parkland.edu/prospectus_1991/1018/thumbnail.jp