arXiv subjects

Hervé Debar

Publications and source records attributed to Hervé Debar.

6 recordsLinked to original sources

arXiv2026

A Study of Kernel Telemetry Options for Security-Oriented Provenance

Provenance aims to capture the origins, transformations, and interactions of system objects for security and forensic applications. Existing provenance capture approaches still face major challenges and are not yet ready for production environments. In this paper, we first analyze the main kernel telemetry capture approaches, identifying eBPF as the most promising, and complement this analysis with micro benchmarks to assess its performance overhead and the filtering mechanisms used to achieve capture granularity, such as restricting capture to individual containers. Building on this foundation, we then classify, according to the studied capture approaches and filtering methods, eight provenance systems and five capture agents that could serve as their capture layers, collectively referred to as tools. Our study reveals that these tools are built on highly heterogeneous capture layers, most of which cannot guarantee the integrity and availability of the captured events, completely failing to meet the requirements of security-oriented use cases.

cs.CR↗

arXiv2026

Monitoring Vulnerabilities in Next-Generation Automotive Operating Systems

Software-defined vehicles (SDVs) are revolutionizing transportation by integrating complex, interconnected hardware, and software systems. This evolution introduces significant security challenges. We present a comprehensive security analysis for SDVs, focusing on software vulnerabilities. We note that existing vulnerability assessment tools fall short in addressing operating systems vulnerabilities, particularly when it comes to efficiently analyzing diverse software stacks in realistic environments. We present and release a vulnerability assessment solution that efficiently addresses these limitations. Our approach combines systematic vulnerability discovery, leveraging public Common Vulnerabilities and Exposures (CVE) databases, within a dockerized development environment that evaluates exploitability risks. The results reveal both breadth of potential threats and the practical constraints we faced during exploitation. We discuss the implications for industry and research, and propose directions for building more resilient SDVs.

cs.CR↗

arXiv2021

CVSS-BERT: Explainable Natural Language Processing to Determine the Severity of a Computer Security Vulnerability from its Description

When a new computer security vulnerability is publicly disclosed, only a textual description of it is available. Cybersecurity experts later provide an analysis of the severity of the vulnerability using the Common Vulnerability Scoring System (CVSS). Specifically, the different characteristics of the vulnerability are summarized into a vector (consisting of a set of metrics), from which a severity score is computed. However, because of the high number of vulnerabilities disclosed everyday this process requires lot of manpower, and several days may pass before a vulnerability is analyzed. We propose to leverage recent advances in the field of Natural Language Processing (NLP) to determine the CVSS vector and the associated severity score of a vulnerability from its textual description in an explainable manner. To this purpose, we trained multiple BERT classifiers, one for each metric composing the CVSS vector. Experimental results show that our trained classifiers are able to determine the value of the metrics of the CVSS vector with high accuracy. The severity score computed from the predicted CVSS vector is also very close to the real severity score attributed by a human expert. For explainability purpose, gradient-based input saliency method was used to determine the most relevant input words for a given prediction made by our classifiers. Often, the top relevant words include terms in agreement with the rationales of a human cybersecurity expert, making the explanation comprehensible for end-users.

cs.CL↗

arXiv2019

Anomalous Communications Detection in IoT Networks Using Sparse Autoencoders

Nowadays, IoT devices have been widely deployed for enabling various smart services, such as, smart home or e-healthcare. However, security remains as one of the paramount concern as many IoT devices are vulnerable. Moreover, IoT malware are constantly evolving and getting more sophisticated. IoT devices are intended to perform very specific tasks, so their networking behavior is expected to be reasonably stable and predictable. Any significant behavioral deviation from the normal patterns would indicate anomalous events. In this paper, we present a method to detect anomalous network communications in IoT networks using a set of sparse autoencoders. The proposed approach allows us to differentiate malicious communications from legitimate ones. So that, if a device is compromised only malicious communications can be dropped while the service provided by the device is not totally interrupted. To characterize network behavior, bidirectional TCP flows are extracted and described using statistics on the size of the first N packets sent and received, along with statistics on the corresponding inter-arrival times between packets. A set of sparse autoencoders is then trained to learn the profile of the legitimate communications generated by an experimental smart home network. Depending on the value of N, the developed model achieves attack detection rates ranging from 86.9% to 91.2%, and false positive rates ranging from 0.1% to 0.5%.

cs.CR↗

arXiv2017

An n-sided polygonal model to calculate the impact of cyber security events

This paper presents a model to represent graphically the impact of cyber events (e.g., attacks, countermeasures) in a polygonal systems of n-sides. The approach considers information about all entities composing an information system (e.g., users, IP addresses, communication protocols, physical and logical resources, etc.). Every axis is composed of entities that contribute to the execution of the security event. Each entity has an associated weighting factor that measures its contribution using a multi-criteria methodology named CARVER. The graphical representation of cyber events is depicted as straight lines (one dimension) or polygons (two or more dimensions). Geometrical operations are used to compute the size (i.e, length, perimeter, surface area) and thus the impact of each event. As a result, it is possible to identify and compare the magnitude of cyber events. A case study with multiple security events is presented as an illustration on how the model is built and computed.

cs.CR↗

arXiv2014

Combining Technical and Financial Impacts for Countermeasure Selection

Research in information security has generally focused on providing a comprehensive interpretation of threats, vulnerabilities, and attacks, in particular to evaluate their danger and prioritize responses accordingly. Most of the current approaches propose advanced techniques to detect intrusions and complex attacks but few of these approaches propose well defined methodologies to react against a given attack. In this paper, we propose a novel and systematic method to select security countermeasures from a pool of candidates, by ranking them based on the technical and financial impact associated to each alternative. The method includes industrial evaluation and simulations of the impact associated to a given security measure which allows to compute the return on response investment for different candidates. A simple case study is proposed at the end of the paper to show the applicability of the model.

cs.CR↗