International audience

The Internet of Things (IoT) designates an entire ecosystem including a wide variety of physical objects capable of collecting data through sensors (i.e., connected objects), sharing and transmitting data through network and platforms that analyzes data. Thanks to IoT technology a significant growth in IoT devices are connected to each other and a plethora of economic opportunities can be created for different industries. This innovative technology holds promise for various fields ranging from health to energy, from manufacturing to transportation, from child care to protection of the elderly. With smart solutions including customer-facing technologies like mobile applications, complexity of operations can be reduced, lowering costs, and accelerating the time to market requirements. Therefore, IoT security is imperative, and new cyber-security issues have been proposed. This paper presents a review of Iot network layers threats that is the preliminary part of a growing need for effective solutions to assess the security of IoT equipment. By taking an innovative approach and leveraging best practices, we expect to make a meaningful contribution to secure the IoT ecosystem and building user confidence in these ever-evolving technologies.

The increasing vulnerability of Industrial Internet of Things (IIoT) systems to cyber-attacks necessitates the development of dedicated datasets for robust intelligent security measures. This paper introduces the HiTar-23 dataset, designed to expedite the detection of cyber-attacks in industrial environments. The latter outlines the detailed steps involved in creating the HiTar-23 dataset, emphasizing meticulous methodologies for construction and validation threw the application of supervised machine learning (ML) techniques, specifically leveraging the WEKA 3.9.6 tool, to assess detection capabilities within a simulated industrial framework facilitated by the Arezzo environment. The holistic approach underscores careful dataset construction and sophisticated evaluation methods, providing insights to strengthen IIoT system security.

Home health care (HHC) defines the set of services that are provided to a patient at home. They have the advantage of being less expensive and less stressful for patients. However, this practice requires in most cases the displacement of a medical staff for a patient's home intervention, which generates the problem of effective management at lower cost of these visits. Internet of Things (IoT) technologies is enabling the medical sector as well as the transportation sector to witness a huge change in the way they operate. The implementation of intelligent solutions, building a bridge linking the Health 4.0 sector to that of Intelligent Transport Systems (ITS), allows the resolution of new problems and guarantees an improvement in the quality of services. This article provides a vehicle routing solution for home healthcare based on CPLEX solver. The proposed solution provides efficient route optimization for vehicles to assist patients in their treatment.

Physical site security is a strategy for protecting against threats and mitigating the potential negative effects of security incidents. Potential threats must be identified and prioritized according to their criticality and potential impact. The abnormal activity analysis in video scene is very difficult due to several real world constraints. AI-based video surveillance systems are able to analyze video streams in real time and automatically detect suspicious behavior. Using machine learning, these systems can recognize normal behavior patterns and identify abnormal behavior indicating a threat or suspicious activity. This paper develops and discusses a solution for video surveillance based on artificial intelligence (AI), underlying deep learning implementation technology involved. The proposed solution meets the security requirements and allows security operators to react quickly to potential incidents and prevent dangerous situations.

Data corpus tend to be heterogeneous presenting a significant challenge in extracting meaningful knowledge from, especially with the rapid growth of digital data. Traditional approaches lack when it comes to handling enormous volumes of unstructured data existing across various sources. Knowledge Graphs, becoming a more and more trendy topic, offer advanced modelization that can help cover such gap. Knowingly, data labeled graph and RDF triplestores are data management approaches that are built on modeling, storing, and querying graph-like data. Despite this fundamental idea, each have unique characteristics that hamper database interoperability. While some methods exist to convert databases to RDF graph or to property graphs and vice versa, they still lack consistency and solid formal foundation. This paper describes Novel Approach for Extracting Summarized RDF Graph from Heterogeneous Corpus.

The Internet’s vast amount of content has led to the creation of recommendation systems that help users find what they need. Among these systems, there is a growing field known as expert recommendation systems. These systems aim to identify highly knowledgeable individuals in specific topics by analyzing their activities and the content associated with them. When a user enters a topic or query, the system generates a ranked list of people who are experts in that area.In this context, we present in this paper a new approach for automatically experts recommendation based on a hybrid machine learning approach.

The integration of software-defined network (SDN) and Internet of Things (IoT) networks offers solutions to IoT network issues. However, this integration also introduces new security challenges and increases the attack surface of IoT networks. Existing studies on the security of SD-IoT networks lack structure and real-world descriptions of attack vectors. To address these limitations, our paper proposes a formal methodology for evaluating SD-IoT framework security through threat modeling. Our approach classifies attack vectors using the STRIDE model, describes their Tactics, Techniques, and Procedures (TTPs) using the Mitre ATT&CK framework, and proposes countermeasures. We demonstrate the potential of our methodology by applying it to a use case of modeling security for Software-Defined Vehicle Networks (SDVNs) in intelligent transport systems (ITS). Our preliminary findings are very promising towards a formal methodology for evaluating SDIoT framework security, which could serve as a foundation for developing a new SD-IoT security standard.

In this paper, a new bi-objective model is proposed to deal with the Home Health Care Routing and Scheduling Problem. The considered problem combined the Vehicle Routing Problem with the Personnel scheduling Problem. Two well-known multi-objective Evolutionary algorithms are suggested to solved it with test instances taking from the literature. The obtained results show the effectiveness and the suitability of evolutionary algorithms to solve the problem.

Due to the enormous volume of data on the web, it is hard for the user to retrieve effective and useful information within the right time. Thus, it has become a need to generate a brief summary from a large amount of textual data according to the user profile. In this context, text summarization is used to identify important information within text documents. It aims to generate shorter versions of the source text, by including only the relevant and salient information. In recent years, the research on summarization techniques based on topic modeling techniques has become a hot topic among researchers thanks to their ability to classify, understand a large text corpora and extract important topics on the text. However, existing studies do not provide the support of personalization when generating summaries because they need to know not only which documents are most helpful to the users, but also which topics and keywords are more or less related to the user' interests. Thus, existing studies lack of the support of adaptive user modeling for user applications in the emerging areas of automatic summarization, topic modeling and visualization. In this context, we propose a new approach of automated text summarization based on topic modeling techniques and taking into account the user's profile which helps to semantically extract relevant topics of textual documents, summarizing information according to the user' topics interests and finally visualize them through a hyper-graph Experiments have been conducted to measure the effectiveness of our solution compared to existing summarizing approaches based on text content. The results show the superiority of our approach.

With the emerging Internet of Things (IoT), the real-time applications are increasingly deployed on IoT systems using Multi Processing System On Chip (MPSoC). Real-time applications have strict temporal constraints, and are not fault-tolerant. Any defective IoT device can lead to serious faults such as data loss or even application failure. It is possible to predict the defective IoT device using a predictive model to prevent faults. Data on CPU load, consumption and thermal state of devices are correlated with the state of the device. In this paper, we show how to classify the state of MPSoC IoT Devices using a decision tree with ID3 algorithm as a split heuristic based on hardware measurement features.

With the advent of the Internet of Things (IoT), various interconnected objects can be used to improve the collection and the process of vital signs with partially or fully automatized methods in smart hospital environment. The vital signs data are used to evaluate patient health status using heuristic approaches, such as the early warning scoring (EWS) approach. Several applications have been proposed based on the early warning scores approach to improve the recognition of patients at risk of deterioration. However, there is a lack of efficient tools that enable a personalized monitoring depending on the patient situations. This paper explores the publish-subscribe pattern to provide a self-adaptative early warning score system in smart hospital context. We propose an adaptative configuration of the vital sings monitoring process depending on the patient health status variation and the medical staff decisions.

Traditional access control models aim to prevent data leakage via direct accesses.A direct access occurs when a requester poses his query directly on the desired object.However, these models fail to protect sensitive data from being accessed with inference channels.An inference channel is produced by the combination of the legitimate response which a user receives from the system and metadata.Detecting and removing inference in database systems guarantee a highquality design in terms of data secrecy and privacy.Parting from the fact that data distribution exacerbates inference problem, we give in this paper a survey of the current and emerging research on the inference problem in both centralized and distributed database systems and highlighting research directions in this field.

In this paper, we propose a novel constant-size threshold attribute-based signcryption scheme for securely \nsharing data through public clouds. Our proposal has several advantages. First, it provides flexible cryptographic access control, while preserving users’ privacy as the identifying information for satisfying the access \ncontrol policy are not revealed. Second, the proposed scheme guarantees both data origin authentication and \nanonymity thanks to the novel use of attribute based signcryption mechanism, while ensuring the unlinkability \nbetween the different access sessions. Third, the proposed signcryption scheme has efficient computation cost \nand constant communication overhead whatever the number of involved attributes. Finally, our scheme satisfies strong security properties in the random oracle model, namely Indistinguishability against the Adaptive \nChosen Ciphertext Attacks (IND-CCA2), Existential Unforgeability against Chosen Message Attacks (EUFCMA) and privacy preservation of the attributes involved in the signcryption process, based on the assumption \nthat the augmented Multi-Sequence of Exponents Decisional Diffie-Hellman (aMSE-DDH) problem and the \nComputational Diffie Hellman Assumption (CDH) are hard.

International audience

In recent years, with the massive use of Golomb rulers in various fields of engineering, new optimal rulers have become an important subject of search. Many different approaches have been proposed to tackle the Golomb ruler problem such as exact methods, constraint programming, local searches and evolutionary algorithms. This paper describes an hybrid evolutionary algorithm to find optimal or near-optimal Golomb rulers. The obtained results are promising: we are capable of solving large rulers for up to 23 marks.

H.264/AVC (Advanced Video Codec) is a new video coding standard developed by a joint effort of the ITU-TVCEG and ISO/IEC MPEG. This standard provides higher coding efficiency relative to former standards at the expense of higher computational requirements. Implementing the H.264 video encoder for an embedded System-on-Chip (SoC) is a big challenge. For an efficient implementation, we motivate the use of multiprocessor platforms for the execution of a parallel model of the encoder. In this paper, we propose a high-level independent target-architecture parallelization methodology for the development of an optimized parallel model of a H.264/AVC encoder. This methodology is used independently of the architectural issues of any target platform. It is based on the exploration of the task and data levels forms of parallelism simultaneously, and the use of the parallel Kahn Process Network (KPN) model of computation and the YAPI programming C++ runtime library. The encoding performances of the obtained parallel model have been evaluated by system-level simulations targeting multiple multiprocessors platforms.

H.264/AVC (advanced video codec) is a new video coding standard developed by a joint effort of the ITU-TVCEG and ISO/IEC MPEG. This standard provides higher coding efficiency relative to former standards at the expense of higher computational requirements. Implementing the H.264 video encoder for an embedded system-on-chip (SoC) is a big challenge. For an efficient implementation, we motivate the use of multiprocessor platforms for the execution of a parallel model of the encoder. In this paper, we propose a high-level independent target-architecture parallelization methodology for the development of an optimized parallel model of a H.264/AVC encoder (i.e. a processes network model balanced in communication and computation workload).

This paper deals with integrating an interactive simulator within a behavioral synthesis tool, thereby allowing concurrent synthesis and simulation. The resulting environment provides a cycle based simulation of a behavioral module under synthesis. The simulator and the behavioral synthesis are based on a single model that allows one to link the behavioral description and the architecture produced by synthesis. The basic simulation-synthesis model is extended in order to allow for concurrent architectural simulation of several modules under synthesis. This paper also discusses an implementation of this concept resulting in a simulator, called AMIS. This tool assists the designer for understanding the results of behavioral synthesis and for architecture exploration. It may also be used to debug the behavioral specification.

This paper introduces one way to integrate an interactive simulator within a behavioral synthesis tool, thereby allowing concurrent synthesis and simulation. Such a simulator performs dynamic analysis and time evaluation. This paper also discusses an implementation of this concept resulting in a simulator, called AMIS. This tool assists the designer for understanding the results of behavioral synthesis and for architecture exploration.