Conceptualising an Anti-Digital Forensics Kill Chain for Smart Homes

The widespread integration of Internet of Things (IoT) devices in households generates extensive digital footprints, notably within Smart Home ecosystems. These IoT devices, brimming with data about residents, inadvertently offer insights into human activities, potentially embodying even criminal acts, such as a murder. As technology advances, so does the concern for criminals seeking to exploit various techniques to conceal evidence and evade investigations. This paper delineates the application of Anti-Digital Forensics (ADF) in Smart Home scenarios and recognises its potential to disrupt (digital) investigations. It does so by elucidating the current challenges and gaps and by arguing, in response, the conceptualisation of an ADF Kill Chain tailored to Smart Home ecosystems. While seemingly arming criminals, the Kill Chain will allow a better understanding of the distinctive peculiarities of Anti-Digital Forensics in Smart Home scenario. This understanding is essential for fortifying the Digital Forensics process and, in turn, developing robust countermeasures against malicious activities.Comment: Accepted in 10th International Conference on Information Systems Security and Privacy (ICISSP 2024

Up-to-date Threat Modelling for Soft Privacy on Smart Cars

Physical persons playing the role of car drivers consume data that is sourced from the Internet and, at the same time, themselves act as sources of relevant data. It follows that citizens' privacy is potentially at risk while they drive, hence the need to model privacy threats in this application domain. This paper addresses the privacy threats by updating a recent threat-modelling methodology and by tailoring it specifically to the soft privacy target property, which ensures citizens' full control on their personal data. The methodology now features the sources of documentation as an explicit variable that is to be considered. It is demonstrated by including a new version of the de-facto standard LINDDUN methodology as well as an additional source by ENISA which is found to be relevant to soft privacy. The main findings are a set of 23 domain-independent threats, 43 domain-specific assets and 525 domain-dependent threats for the target property in the automotive domain. While these exceed their previous versions, their main value is to offer self-evident support to at least two arguments. One is that LINDDUN has evolved much the way our original methodology already advocated because a few of our previously suggested extensions are no longer outstanding. The other one is that ENISA's treatment of privacy aboard smart cars should be extended considerably because our 525 threats fall in the same scope.Comment: Accepted in 7th International Workshop on SECurity and Privacy Requirements Engineering (SECPRE 2023). arXiv admin note: substantial text overlap with arXiv:2306.0422

WebSocket Integration in Django

Nowadays Web technologies have become more common as they improve the work of astronomers by easing, for example, the monitoring and analysing of data. The Django Python framework is one of the most widely used libraries for developing Web applications as it offers several advantages. However, the necessity of continuously deal with data in real time, such as tracking atmospheric parameters, analysing the evolution of the light curve during a transient event, displaying inline vector graphics for interactive plots and representation, has constantly grown in Astronomy and Astrophysics, and this has naturally involved in new challenges. Nevertheless the WebSocket protocol represents the best option to manage real-time data, but it is not supported by Django natively. This report provides an overview of the WebSocket protocol and advances the integration of a WebSocket server as a loosely coupled service within a Django application by illustrating a simple and non-invasive methodology, within a proof-of-concept using open source software, which avoid switching to new deployment architectures, with all its consequences. Such proposed technique can be applied to any generic scenarios, such as done for the TMSS project included in the report as use case example

Toward porting Astrophysics Visual Analytics Services to the European Open Science Cloud

The European Open Science Cloud (EOSC) aims to create a federated environment for hosting and processing research data to support science in all disciplines without geographical boundaries, such that data, software, methods and publications can be shared as part of an Open Science community of practice. This work presents the ongoing activities related to the implementation of visual analytics services, integrated into EOSC, towards addressing the diverse astrophysics user communities needs. These services rely on visualisation to manage the data life cycle process under FAIR principles, integrating data processing for imaging and multidimensional map creation and mosaicing, and applying machine learning techniques for detection of structures in large scale multidimensional maps

The Gaia AVU-GSR parallel solver: preliminary porting with OpenACC parallelization language of a LSQR-based application in perspective of exascale systems

The Gaia Astrometric Verification Unit-Global Sphere Reconstruction (AVU-GSR) Parallel Solver aims to find the positions and the proper motions for ~10^8 stars in our galaxy, besides the attitude and the instrumental settings of the Gaia satellite, and the global parameter of the post Newtonian formalism. To find these parameters, the code solves a system of linear equations, × = , where the coefficient matrix is large, containing ~10^11 x 10^8 elements, and sparse. The system of equations is solved with a customized implementation of the iterative preconditioned (PC)-LSQR algorithm and is parallelized on the CPU with MPI+OpenMP, where the computation related to different horizontal portions of the coefficient matrix is assigned to different MPI processes and it is further parallelized on the OpenMP threads. To improve the code performance, we explored the feasibility of a porting of this application on a GPU environment, by replacing the OpenMP directives with the OpenACC correspondent ones. In this preliminary porting, the ~95% of the data is copied from the host (CPU) to the device (GPU) before the entire cycle of iterations, making the code compute bound rather than data-transfers bound. The OpenACC code accelerates of a factor of ~1.5 compared to the OpenMP code. The OpenACC application runs on multiple GPUs and it was tested on the CINECA SuperComputer Marconi100, with 4 V100 GPUs per node having 16 GB of memory each. A following porting, where the OpenACC language is replaced with CUDA, was performed, optimizing the preliminary porting with OpenACC. The CUDA code has just been put into production on Marconi100 and we plan to run it on the future pre-exascale platform Leonardo of CINECA, with 4 next-generation A100 GPUs per node