Publications

Abstract

The corpus of letters we are studying is located at the Archives Nationales d’Outre-Mer in Aix-en-Provence, France. These late 18th-century letters come from Saint Domingue (now Haiti), a French colony in the Caribbean Sea of which Bellecombe, the author, was governor. They were written in the context of the American War of Independence, in which France took part on the side of the Americans. We have reconstructed Bellecombe’s correspondence with the Secretary of State for the Navy, in Versailles: the archives contain hundreds of letters in clear and three encrypted letters, including some clear/cipher pages that were our lever for reconstructing part of the key, and 96% of the encrypted letter that was opaque at first. From a cryptanalytical point of view, Bellecombe used a directory-based encryption. The common use of this type of cipher in the 17th and 18th-century European countries raises the question of the method to be used (then as now!) to decode such messages.

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Graph-based intelligent cyber threat detection system

Abstract

In the wake of the generalised spread of machine learning approaches, attackers are actively considering those approaches to avoid being detected. Classification models for attack detection are foremost composed of feature-driven algorithms. Thus, primary features which are individual dimension in the original attributes of data in the input space are a prime target to compromise an AI-driven model. Additionally, adversarial examples have shown that an attacker does not need to have knowledge of detection criteria to compromise a detection model, even in the case of a black box model. Attacks behavioural changes cause features from attacks datapoints to be altered and detection performances to drop. Thus, robust features must be engineered to prevent models to be compromised in such manner. Graph-based feature engineering has recently shown promising results considering robust threat detection. We offer an overview on methods for graph-based features extraction and explain why they are relevant to robust feature engineering for threat detection purposes. We detail what we think are properties for feature space to be sustainable and efficient for their prolonged exploitation in security operating centres. Specifically, we provide key criteria for the robustness of a feature space for attack detection. Finally, we summarize the characteristics for time robust feature selection, identify current limitations specific to the distinctive type of graph-based approaches in the purposes of threat detection in large internet networks.

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Petri nets and higher-dimensional automata

Abstract

Petri nets and their variants are often considered through their interleaved semantics, i.e., considering executions where, at each step, a single transition fires. This is clearly a miss, as Petri nets are a true concurrency model. This paper revisits the semantics of Petri nets as higher-dimensional automata (HDAs) as introduced by van Glabbeek, which methodically take concurrency into account. We extend the translation to include some common features. We consider nets with inhibitor arcs, under both concurrent semantics used in the literature, and generalized self-modifying nets. Finally, we present a tool that implements our translations.

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Simplifying LTL model checking given prior knowledge

Abstract

We consider the problem of the verification of an LTL specification $\varphi$ on a system $S$ given some prior knowledge $K$, an LTL formula that $S$ is known to satisfy. The automata-theoretic approach to LTL model checking is implemented as an emptiness check of the product $S\otimes A_{\lnot\varphi}$ where $A_{\lnot\varphi}$ is an automaton for the negation of the property. We propose new operations that simplify an automaton $A_{\lnot\varphi}$ givengiven some knowledge automaton $A_K$, to produce an automaton $B$ that can be used instead of $A_{\lnot\varphi}$ for more efficient model checking.Our evaluation of these operations on a large benchmark derived from the MCC’22 competition shows that even with simple knowledge, half of the problems can be definitely answered without running an LTL model checker, and the remaining problems can be simplified significantly.

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Introducing h-leading-ones as a mixed-category benchmark problem for evolutionary algorithms

Abstract

In the wake of generative artificial intelligence and the exponential growth in the volume of data generated, the associated increase in data complexity in the sense of the quantity of different datatypes present in a single system poses a challenge to evolutionary algorithms. To allow for the development and testing of new algorithms adapted to this new data landscape, test problems are necessary as a way to both evaluate and compare algorithms per-formances. However, while recent advances extended known test problems such as the r-Leading-Ones marking the transition from binary to multi-valued variables, having different data-types coexisting in the search space is still an open question. We propose the h-Leading-Ones as an extension of the r-Leading-Ones to evaluate the ability of an algorithm to solve problems on a search space composed of multi-valued and real-valued data types. Its design with dependency between the different data-types and its continuity with the r-Leading-Ones provides a convenient new environment for benchmark and runtime analysis for mixed-category searchspaces.

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Investigation of metabelian platform groups for protocols based on (simultaneous) conjugacy search problem

Abstract

here are many group-based cryptosystems in which the security is related to the conjugacy search problem or the simultaneous conjugacy search problem in their underlying platform groups. In this article, we show that some metabelian groups do not provide strong security for these cryptosystems and so they cannot be chosen as platform groups..

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Security analysis of ZKPoK based on MQ problem in the multi-instance setting

Abstract

Bidoux and Gaborit introduced a new general technique to improve zero-knowledge (ZK) proof-of-knowledge (PoK) schemes for a large set of well-known post-quantum hard computational problems such as the syndrome decoding, the permuted kernel, the rank syndrome decoding, and the multivariate quadratic (MQ) problems. In particular, the authors’ idea in the study of Bidoux and Gaborit was to use the structure of these problems in the multi-instance setting to minimize the communication complexity of the resulting ZK PoK schemes. The security of the new schemes is then related to new hard problems. In this article, we focus on the new multivariate-based ZKPoK and the corresponding new underlying problem: the so-called DiffMQ.

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Spectral graph analysis of bipartite graphs for advanced attack detection

Abstract

Spectral graph theory offers powerful tools for understanding graph properties through spectral signatures. This work leverages the inherent link between graph topology and spectral characteristics to enhance anomaly detection in network traffic, particularly in medical IoT networks. We introduce SPECTRA, a spectral graph analysis technique designed to detect anomalies in dynamic and complex network structures. This method incorporates five spectral metrics, including the newly proposed BiFlowness metric derived from Singular Value Decomposition (SVD), which captures the f low dynamics within bipartite graph topologies. By combining these spectral metrics, SPECTRA provides a comprehensive model for detecting and analyzing advanced cyberattack patterns, such as multistep intrusions, in critical systems. Focusing on hybrid topologies that integrate star and bipartite structures, this technique applies spectral analysis to evolving networks, enabling the detection of attacks (port scanning, fingerprinting) over time. Performed experiments validate the effectiveness of SPECTRA across IoT datasets, demonstrating its superiority in identifying attack behaviors. The proposed approach aligns with the critical demands of medical IoT environments by providing a good threat detection procedure to enhance security in sensitive networks.

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An enhanced formalism for resource management policies specification and fast evaluation in pervasive systems

Abstract

Pervasive systems demand flexible, efficient resource management policies to handle heterogeneous infrastructures and varying application needs. This paper introduces an extended formalism that overcomes limitations in previous approaches by distinctly separating static properties from dynamic context elements, allowing more precise policy definitions. Mandatory and optional policies are explicitly categorized, enabling fail-fast decisions when critical conditions fail, while also supporting opportunistic executions. These design choices reduce evaluation costs—often down to O(1) in the best case—and permit large-scale environments to benefit from parallel evaluations. Practical simulations demonstrate superior performance in collaborative, multi-organization scenarios, highlighting improved adaptability, reduced overhead, and effective integration of organizational knowledge within the resource management process.

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How effective are OS-level virtualization tools for managing containers?

Abstract

As reliance on OS-level virtualization tools grows, understanding their efficiency in container management tasks is essential for optimizing performance. This study presents a comprehensive performance analysis of Docker, Podman, and LXD across key container management tasks: loading, starting, stopping, and removing containers and images. Our results indicate Docker’s consistent superiority in speed, achieving the fastest execution times across tasks but at the cost of higher CPU usage. Podman demonstrates balanced resource efficiency, though generally slower than Docker in image loading. LXD, while slower in starting containers, exhibits lower CPU usage in parallel operations, making it suitable for scenarios where resource efficiency is prioritized over speed. These findings underscore the impact of tool choice on containerized environment performance, highlighting the importance of selecting a tool based on specific deployment requirements.

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