The emergence of 6G technology is set to revolutionize connected autonomous vehicles (CAVs) by enabling hyper-connectivity, ultra-reliable low-latency communication, and seamless integration with IoT systems. These advancements enhance CAV efficiency, intelligence, and real-time data exchange for safer navigation and decision-making. However, the interconnected nature of 6G-enabled CAVs introduces significant cybersecurity risks, including adversarial AI attacks and large-scale intrusions. Traditional security methods are inadequate for addressing the complexity and sophistication of emerging threats in this dynamic ecosystem. This article aims to address these critical challenges by proposing an innovative security framework tailored for 6G-enabled CAVs. By integrating blockchain technology, reinforcement learning, and collaborative intrusion detection systems, this framework aims to secure CAV communications against malicious intrusions and ensure the reliability of their AI-driven operations. The system was evaluated on a dataset containing 2D image representations of normal and malicious network traffic. The ensemble-based IDS demonstrated high detection accuracy (99%) with low false positive rates. The Q-learning agent effectively supported trust-based consensus by reliably selecting validators and isolating malicious nodes. The blockchain layer maintained stable validation and propagation times, confirming the framework’s scalability and low-latency performance.

The advent of 6G technology has paved the way for unprecedented advancements in the Internet of Things (IoT), ushering in an era of hyper-connectivity and ubiquitous communication. However, with the proliferation of interconnected devices in 6G-enabled IoT ecosystems, the risk of malicious intrusions and new cyber threats becomes more prominent. Furthermore, the incorporation of AI into 6G networks introduces additional security concerns, such as the risk of adversarial attacks on AI models and the potential misuse of AI for cyber threats. Consequently, securing the extensive and diverse array of connected devices poses a substantial challenge in the 6G environment and needs reconsideration of prior security traditional methods. This paper aims to address these challenges by proposing a novel collaborative intrusion detection system (CIDS) that relies on AI and blockchain technologies. The collaborative nature of the proposed CIDS fosters a collective defense approach, where nodes within the IoT network actively share threat intelligence, enabling rapid response and mitigation. The effectiveness of the proposed system is evaluated through comprehensive simulations and proof-of-concept experiments. The results demonstrate the system’s ability to effectively detect and mitigate falsified and zero-day attacks, thereby fortifying the security infrastructure of 6G -enabled IoT environments.

Abstract Automatic text summarization is a field situated at the intersection of natural language processing and information retrieval. Its main objective is to automatically produce a condensed representative form of documents. This paper presents ArA*summarizer, an automatic system for Arabic single document summarization. The system is based on an unsupervised hybrid approach that combines statistical, cluster‐based, and graph‐based techniques. The main idea is to divide text into subtopics then select the most relevant sentences in the most relevant subtopics. The selection process is done by an A* algorithm executed on a graph representing the different lexical–semantic relationships between sentences. Experimentation is conducted on Essex Arabic summaries corpus and using recall‐oriented understudy for gisting evaluation, automatic summarization engineering, merged model graphs, and n‐gram graph powered evaluation via regression evaluation metrics. The evaluation results showed the good performance of our system compared with existing works.

The scale invariant feature transform (SIFT), which was proposed by David Lowe, is a powerful method that extracts and describes local features called keypoints from images. These keypoints are invariant to scale, translation, and rotation, and partially invariant to image illumination variation. Despite their robustness against these variations, strong lighting variation is a difficult challenge for SIFT‐based facial recognition systems, where significant degradation of performance has been reported. To develop a robust system under these conditions, variation in lighting must be first eliminated. Additionally, SIFT parameter default values that remove unstable keypoints and inadequately matched keypoints are not well‐suited to images with illumination variation. SIFT keypoints can also be incorrectly matched when using the original SIFT matching method. To overcome this issue, the authors propose propose a method for removing the illumination variation in images and correctly setting SIFT's main parameter values (contrast threshold, curvature threshold, and match threshold) to enhance SIFT feature extraction and matching. The proposed method is based on an estimation of comparative image lighting quality, which is evaluated through an automatic estimation of gamma correction value. Through facial recognition experiments, the authors find significant results that clearly illustrate the importance of the proposed robust recognition system.

In the today's mobile communications scenario, smartphones offer new capabilities to develop sophisticated applications that seem to make daily life easier and more convenient for users. Such applications, which may involve mobile ticketing, identification, access control operations, etc., are often accessible through social network aggregators, that assume a fundamental role in the federated identity management space. While this makes modern smartphones very powerful devices, it also makes them very attractive targets for spyware injection. This kind of malware is able to bypass classic authentication measures and steal user credentials even when a secure element is used, and can, therefore, perform unauthorized mobile access to social network services without the user's consent. Such an event allows stealing sensitive information or even a full identity theft. In this work, we address this issue by introducing BrightPass, a novel authentication mechanism based on screen brightness. BrightPass allows users to authenticate safely with a PIN-based confirmation in the presence of specific operations on sensitive data. We compare BrightPass with existing schemes, in order to show its usability and security within the social network arena. Furthermore, we empirically assess the security of BrightPass through experimentation. Our tests indicate that BrightPass protects the PIN code against automatic submissions carried out by malware while granting fast authentication phases and reduced error rates.

Key distribution is a core building block for secure communication. In group communication, key distribution is not a simple extension of two-party communication. Many approaches were proposed in the classical field. Nevertheless, they are still based on the assumption that some computational problems are hard. Based on quantum mechanics lows, new field emerges allowing to generate and share a secret and secure key between two, or more, participants. In this paper, we propose new multiparty key distribution protocol in ring-organised communication group based on the well-known quantum key distribution protocol BB84. The security of the proposed solution is based on the unconditional security of the BB84 allowed by the mechanics lows and the mathematical proved secure operation XOR. In our proposed solution, each participant collaborates with a partial key in order to obtain at the end of the protocol the same group key that can be used for encryption aims. We also analyse and verify security properties of the proposed protocol. This is done using a probabilistic symbolic model-checker, the PRISM tool.

In this paper, we propose a hybrid computational geometry-gray scale algorithm that enhances fingerprint images greatly. The algorithm extracts the local minima points that are positioned on the ridges of a fingerprint, then, it generates a Delaunay triangulation using these points of interest. This triangulation along with the local orientations give an accurate distance and orientation-based ridge frequency. Finally, a tuned anisotropic filter is locally applied and the enhanced output fingerprint image is obtained. When the algorithm is applied to rejected fingerprint images from FVC2004 DB2 database by the veryfinger application, these images pass and experimental results show that we obtain a low false and missed minutiae rate with an almost uniform distribution over the database. Moreover, the application of the proposed algorithm enables the extraction of features from all low-quality fingerprint images where the equal error rate of verification is decreased from 6.50% to 5% using nondamaged low-quality images in the database.

Due to the fact that sensor nodes are untethered and unattended, energy management is a critical issue in communication mechanism of a wireless sensor network. In this paper, we address this problem and propose a novel solution based on media access control technique. Our protocol STDMA (for Sensor-Time Division Multiple Access) exploits the inherent features of TDMA to avoid the main sources of energy wastage. It assigns sensors with consecutive time slots to reduce the frequency of state transitions. S-TDMA is carefully designed to overcome the high latency of traditional TDMA protocols. It also conserves energy when a node may not be transmitting or receiving packets. Simulation results show that the S-TDMA protocol functions well.

We propose in this paper a new method to compute indicators based on models transformations. We use the concept of modeled trace and trace-based system to design an indicator. We use traces transformations to build indicators model and to actually compute them. Effective implementation of the indicator is demonstrated through the transformations effected on a particular learning platform based on real traces, constituting so called primary modeled traces. A "path" of transformations is explicitly designed and applied both for documenting and building the indicator. We illustrate our approach on the Moodle learning platform through a concrete example describing a practical use of our contribution.

In this paper, we present our approach for UML based modeling of control/data driven embedded systems. In our case application is presented as a network of hierarchic data driven and control driven tasks that communicate via abstract channels. Hardware platform is modeled as UML structure diagram. Mapping of application on hardware platform is modeled through UML constraints. From UML models, a Maude specification is generated. We use this formal specification to formally validate system functionality against some undesirable properties and to estimate system power consumption at a high level of abstraction.

We propose a methodological approach for the formal specification and verification of RISC processor microarchitectures within a functional framework. The approach exploits only the next state function to formally specify both ISA and MA levels and proves their equivalence in a systematic way. Moreover, the proof could be performed at different architectural levels. The central idea consists of decomposing the next state function into coordinates such that to model the microarchitecture at the component level. Such decomposition allows the proof to be systematically decomposed into a set of verification conditions more simple to reason about and to verify. The potential features of the proof methodology are demonstrated over the MIPS processor within Haskell framework.

Parallelism has generated new concepts and approaches making it possible to conceive competitive systems and parallel architectural configurations. These two concepts are hard to bring altogether and require a precise definition of the structural elements of algorithm and their projection on suitable parallel architectures. The work described, tries to concretize this merge of concepts for a class of architectures called: "systolic". One starts with the description of the "Method of Projection of the Dependencies", then one applies it to the design of a systolic network for the resolution of a linea system by an iterative method: "the Gauss-Seidel method".