Publications

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

Go2Pins: A framework for the LTL verification of Go programs

Abstract

We introduce Go2Pins, a tool that takes a program written in Go and links it with two model-checkers: LTSMin [19] and Spot [7]. Go2Pins is an effort to promote the integration of both formal verifica- tion and testing inside industrial-size projects. With this goal in mind, we introduce black-box transitions, an efficient and scalable technique for handling the Go runtime. This approach, inspired by hardware ver- ification techniques, allows easy, automatic and efficient abstractions. Go2Pins also handles basic concurrent programs through the use of a dedicated scheduler. In this paper we demonstrate the usage of Go2Pins over benchmarks inspired by industrial problems and a set of LTL formulae. Even if Go2Pins is still at the early stages of development, our results are promising and show the the benefits of using black-box transitions.

Continue reading

ICDAR 2021 competition on historical map segmentation

Abstract

This paper presents the final results of the ICDAR 2021 Competition on Historical Map Segmentation (MapSeg), encouraging research on a series of historical atlases of Paris, France, drawn at 1/5000 scale between 1894 and 1937. The competition featured three tasks, awarded separately. Task 1 consists in detecting building blocks and was won by the L3IRIS team using a DenseNet-121 network trained in a weakly supervised fashion. This task is evaluated on 3 large images containing hundreds of shapes to detect. Task 2 consists in segmenting map content from the larger map sheet, and was won by the UWB team using a U-Net-like FCN combined with a binarization method to increase detection edge accuracy. Task 3 consists in locating intersection points of geo-referencing lines, and was also won by the UWB team who used a dedicated pipeline combining binarization, line detection with Hough transform, candidate filtering, and template matching for intersection refinement. Tasks 2 and 3 are evaluated on 95 map sheets with complex content. Dataset, evaluation tools and results are available under permissive licensing at https://icdar21-mapseg.github.io/.

Continue reading

Revisiting the Coco panoptic metric to enable visual and qualitative analysis of historical map instance segmentation

Abstract

Segmentation is an important task. It is so important that there exist tens of metrics trying to score and rank segmentation systems. It is so important that each topic has its own metric because their problem is too specific. Does it? What are the fundamental differences with the ZoneMap metric used for page segmentation, the COCO Panoptic metric used in computer vision and metrics used to rank hierarchical segmentations? In this paper, while assessing segmentation accuracy for historical maps, we explain, compare and demystify some the most used segmentation evaluation protocols. In particular, we focus on an alternative view of the COCO Panoptic metric as a classification evaluation; we show its soundness and propose extensions with more “shape-oriented” metrics. Beyond a quantitative metric, this paper aims also at providing qualitative measures through precision-recall maps that enable visualizing the success and the failures of a segmentation method.

Continue reading