Publications

Strong Euler wellcomposedness

Abstract

In this paper, we define a new flavour of well-composedness, called strong Euler well-composedness. In the general setting of regular cell complexes, a regular cell complex of dimension $n$ is strongly Euler well-composed if the Euler characteristic of the link of each boundary cell is $1$, which is the Euler characteristic of an $(n-1)$-dimensional ball. Working in the particular setting of cubical complexes canonically associated with $n$-D pictures, we formally prove in this paper that strong Euler well-composedness implies digital well-composedness in any dimension $n\geq 2$ and that the converse is not true when $n\geq 4$.

Continue reading

Continuous well-composedness implies digital well-composedness in $n$-D

Abstract

In this paper, we prove that when a $n$-D cubical set is continuously well-composed (CWC), that is, when the boundary of its continuous analog is a topological $(n-1)$-manifold, then it is digitally well-composed (DWC), which means that it does not contain any critical configuration. We prove this result thanks to local homology. This paper is the sequel of a previous paper where we proved that DWCness does not imply CWCness in 4D.

Continue reading

Automation of binary analysis: From open source collection to threat intelligence

Abstract

Many open sources of binaries, including malware, have emerged in the landscape in recent years. Their quality compares very favourably with commercial sources, as emphasised by Thibaud Binetruy (Twitter influencer under a pseudonym, Société Générale CERT, 2020): “Integrating operational threat intelin your defense mechanisms doesn’t mean buying Threat Intel. You can start by using the [mass] of open source indicators available for free.” Some are provided by official sources (Abuse.ch, with data supplied by the Swiss national CERT, among others), while others are made available in more obscure ways, sometimes anonymously (VirusShare, VX-Underground, etc.). Our examination of these sources underlines the wide disparity in quality and quantity between them. We have had to take this diversity into account in our research, designing a dedicated platform that enables us to supply information to our binary analysis products and to conduct daily analyses of correlations between and within malware families on a large scale. This work can then be applied to concrete cases such as Babuk, Ryuk and Conti. We have been able to highlight links for these families by immediately identifying correlations, with additional manual analysis then confirming the genealogy of the samples precisely.

Continue reading

A secure blockchain-based architecture for the COVID-19 data network

Abstract

The COVID-19 pandemic has impacted the world economy and mainly all activities where social distancing cannot be respected. In order to control this pandemic, screening tests such as PCR have become essential. For example, in the case of a trip, the traveler must carry out a PCR test within 72 hours before his departure and if he is not a carrier of the COVID-19, he can therefore travel by presenting, during check-in and boarding, the negative result sheet to the agent. The latter will then verify the presented sheet by trusting: (a) the medical biology laboratory, (b) the credibility of the traveler for not having changed the PCR result from “positive to negative”. Therefore, this confidence and this verification are made without being based on any mechanism of security and integrity, despite the great importance of the PCR test results to control the COVID-19 pandemic. Consequently, we propose in this paper a blockchain-based decentralized trust architecture that aims to guarantee the integrity, immutability and traceability of COVID-19 test results. Our proposal also aims to ensure the interconnection between several organizations (airports, medical laboratories, cinemas, etc.) in order to access COVID-19 test results in a secure and decentralized manner.

Continue reading

Evaluation of anomaly detection for cybersecurity using inductive node embedding with convolutional graph neural networks

Abstract

In the face of continuous cyberattacks, many scientists have proposed machine learning-based network anomaly detection methods. While deep learning effectively captures unseen patterns of Euclidean data, there is a huge number of applications where data are described in the form of graphs. Graph analysis have improved detecting anomalies in non-Euclidean domains, but it suffered from high computational cost. Graph embeddings have solved this problem by converting each node in the network into low dimensional representation, but it lacks the ability to generalize to unseen nodes. Graph convolution neural network methods solve this problem through inductive node embedding (inductive GNN). Inductive GNN shows better performance in detecting anomalies with less complexity than graph analysis and graph embedding methods.

Continue reading

Relational and algebraic methods in computer science - 19th international conference, RAMiCS 2021, marseille, france, november 2-5, 2021, proceedings

Abstract

Continue reading

VizNN: Visual data augmentation with convolutional neural networks for cybersecurity investigation

Abstract

One of the key challenges of Security Operating Centers (SOCs) is to provide rich information to the security analyst to ease the investigation phase in front of a cyberattack. This requires the combination of supervision with detection capabilities. Supervision enables the security analysts to gain an overview on the security state of the information system under protection. Detection uses advanced algorithms to extract suspicious events from the huge amount of traces produced by the system. To enable coupling an efficient supervision with performance detection, the use of visualisation-based analysis is a appealing approach, which into the bargain provides an elegant solution for data augmentation and thus improved detection performance. We propose VizNN, a Convolutional Neural Networks for analysing trace features through their graphical representation. VizNN enables to gain a visual overview of the traces of interests, and Convolutional Neural Networks leverage a scalability capability. An evaluation of the proposed scheme is performed against reference classifiers for detecting attacks, XGBoost and Random Forests

Continue reading

Debug packages in OpenBSD

Abstract

Continue reading

Towards better heuristics for solving bounded model checking problems

Abstract

This paper presents a new way to improve the performance of the SAT-based bounded model checking problem by exploiting relevant information identified through the characteristics of the original problem. This led us to design a new way of building interesting heuristics based on the structure of the underlying problem. The proposed methodology is generic and can be applied for any SAT problem. This paper compares the state-of-the-art approach with two new heuristics: Structure-based and Linear Programming heuristics and show promising results.

Continue reading

VerSe: A vertebrae labelling and segmentation benchmark for multi-detector CT images

Abstract

Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://osf.io/nqjyw/, https://osf.io/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VerSe content and code can be accessed at: https://github.com/anjany/verse.

Continue reading