Deceptive files, often called honeyfiles, have become an established tool in cyber security. Advances in machine learning (ML) models for content generation now allow the synthesis of deceptive material automatically and at scale. Metrics to quantify honeyfile attributes are thus essential to creating and evaluating effective deceptions. The two critical aspects of honeyfiles for which metrics are useful are enticement and realism . Enticement is the ability to attract the attention of intruders or users with malicious intent. Realism measures the similarity of deceptive artefacts to the objects they mimic. In the honeyfile literature, metrics for these attributes have been proposed: the Common Token Count (CTC) [ 1 ], and Topic Semantic Matching (TSM) [ 2 ] scores for enticement, and coherence and cohesion [ 3 ] for realism. In this study, we compare these metrics to the perceptions of human users exposed to text samples in a simulated data breach scenario on a crowd-sourcing platform. We recruited participants to judge the realism and enticement of honeyfile text generated using several techniques. The main findings are: (i) for the enticement metrics, TSM is aligned with the perceived enticement (p-value<0.001), while for the CTC score, we find inconsistent and inconclusive results, and (ii) for the realism metrics, cohesion and coherence, do not consistently align with perceived realism.

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. Current deep learning techniques to address multiple-output problems are based on two main methodologies: (1) mixture density networks, which suffer from poor stability at high dimensions, or (2) multiple choice learning (MCL), an approach that uses M single-output functions, each only producing a point estimate hypothesis. This paper presents a Mixture of Multiple-Output functions (MoM) approach using a novel variant of dropout, Multiple Hypothesis Dropout. Unlike traditional MCL-based approaches, each multiple-output function not only estimates the mean but also the variance for its hypothesis. This is achieved through a novel stochastic winner-take-all loss which allows each multiple-output function to estimate variance through the spread of its subnetwork predictions. Experiments on supervised learning problems illustrate that our approach outperforms existing solutions for reconstructing multimodal output distributions. Additional studies on unsupervised learning problems show that estimating the parameters of latent posterior distributions within a discrete autoencoder significantly improves codebook efficiency, sample quality, precision and recall.

Quantum computers are believed to be capable of breaking the security of most classical public key cryptosystems. To mitigate future security risks, researchers have been working on a hybrid approach that uses both classical and post-quantum cryptographic techniques, with the aim of keeping the system secure as long as at least one of the cryptosystems remains secure. However, most existing hybrid cryptosystems require protocol revisions to accommodate post-quantum cryptographic algorithms, leading to extensive modifications of existing code-bases and increased complexity in the state machines. In this paper, we explore a novel generic hybrid model that requires only minimal changes to the codebase of a classical cryptosystem while maintaining the simplicity of the state machines. To illustrate the working principle and provide a benchmark for our generic hybrid model, we conduct a case study on the IKEv2 protocol using the strongS wan library. Our benchmark reveals that, in a hybrid configuration with two protocols, our generic model introduces minimal overhead compared to the combined key exchange time of both protocols. Moreover, our model design allows for the initiation of different protocols in parallel, resulting in an acceleration of the key exchange time, particularly in hybrid configurations Involving more than two protocols.

Cyber deception is the practice of deliberately introducing fake or misleading artefacts into cyber systems. It is emerging as a promising approach to defending networks and systems against attackers and data thieves. However, despite being relatively cheap to deploy [1], the generation of realistic content at scale is very costly when it is 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. The presence of an active messaging platform also enhances the realism of a deceptive network simulation, contributing both traffic and message artefacts. 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. [2] to create a generative model for unicast and multi-cast communications. We demonstrate the use of fine-tuned, pretrained 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. Our simulations run in real time, making them suitable for deployment in cyber deception as a honeypot in its own right, or as part of a larger deception environment.

We present an algorithm for 3D reconstruction of dynamic articulated structures, such as humans, from uncalibrated multiple views. The reconstruction exploits constraints associated with a dynamic articulated structure, specifically the conservation over time of length between rotational joints. These constraints admit metric reconstruction from at least two different images in each of two uncalibrated parallel projection cameras. The algorithm is based on a stratified approach, starting with affine reconstruction from factorization, followed by rectification to metric structure using the articulated structure constraints. The exploitation of these specific constraints allows reconstruction and self-calibration with fewer feature paints and views compared to standard self-calibration. The method is extended to pairs of cameras that are zooming, Where calibration of the cameras allows compensation for the changing scale factor in a scaled orthographic camera. Results are presented in the form of stick figures and animated 3D reconstructions using pairs of sequences from broadcast television. The technique shows promise as a means of creating 3D animations of dynamic activities such as sports events.

We present a simple approach to combining scene and auto-calibration constraints for the calibration of cameras from single views and stereo pairs. Calibration constraints are provided by imaged scene structure, such as vanishing points of orthogonal directions, or rectified planes. In addition, constraints are available from the nature of the cameras and the motion between views. We formulate these constraints in terms of the geometry of the imaged absolute conic and its relationship to pole-polar pairs and the imaged circular points of planes. Three significant advantages result: first, constraints from scene features, camera characteristics and auto-calibration constraints provide linear equations in the elements of the image of the absolute conic. This means that constraints may easily be combined, and their solution is straightforward. Second, the degeneracies that occur when constraints are not independent may be easily identified. Lastly, the constraints from scene planes and image planes may be treated uniformly. Examples of various cases of constraint combination and degeneracy as well as computational techniques are presented.

We present methods for creating 3D graphical models of scenes from a limited numbers of images, i.e. one or two, in situations where no scene co‐ordinate measurements are available. The methods employ constraints available from geometric relationships that are common in architectural scenes – such as parallelism and orthogonality – together with constraints available from the camera. In particular, by using the circular points of a plane simple, linear algorithms are given for computing plane rectification, plane orientation and camera calibration from a single image. Examples of image based 3D modelling are given for both single images and image pairs.

We describe the geometry constraints and algorithmic implementation for metric rectification of planes. The rectification allows metric properties, such as angles and length ratios, to be measured on the world plane from a perspective image. The novel contributions are: first, that in a stratified context the various forms of providing metric information, which include a known angle, two equal though unknown angles, and a known length ratio; can all be represented as circular constraints on the parameters of an affine transformation of the plane-this provides a simple and uniform framework for integrating constraints; second, direct rectification from right angles in the plane; third, it is shown that metric rectification enables calibration of the internal camera parameters; fourth, vanishing points are estimated using a Maximum Likelihood estimator; fifth, an algorithm for automatic rectification. Examples are given for a number of images, and applications demonstrated for texture map acquisition and metric measurements.