This survey investigates the persistent and evolving threat of social engineering to information system security, emphasizing the urgency of prevention strategies. With 74

Malicious actors in the Social Internet of Things (SIoT) actively exploit system vulnerabilities to manipulate trust mechanisms, leveraging the decentralized nature of interactions and the vast data generated by IoT devices. These adversaries spread deceptive information, gain unwarranted trust from users, and construct false reputations to serve harmful purposes, resulting in trust-related attacks that compromise user ratings and feedback integrity. To counter these threats, this study introduces a robust trust management framework built on blockchain technology to ensure transparency and trustworthiness. At the core of our approach is a zero-knowledge proof-based authentication layer, implemented as a smart contract, which preserves user privacy while enabling reliable verification. In addition, we develop a trust evaluation model based on a federated learning architecture tailored to the non-independent and non-identically distributed nature of data and the resource constraints of heterogeneous IoT devices. Experimental evaluations using a simulated dataset demonstrate that the proposed FedTrust model achieves up to 99.89% accuracy in detecting trust-related attacks. On-chain analysis shows that proof verification incurs approximately 390,000 gas, with costs reduced from $7.79 on Ethereum Mainnet to as low as $0.006 on Polygon, and verification latency ranging from 0.09 to 0.31 s depending on the network. These results confirm the scalability, privacy preservation, and computational efficiency of our framework, making it a viable solution for real-world SIoT deployments.

Recent strides in AI research, particularly in computer vision and natural language processing, have significantly advanced the partial automation of data labeling and annotation processes. However, there remains a notable void in applying these cutting-edge techniques to videos portraying human-centric scenarios, with scant exploration of automated solutions for multimedia data. Current research primarily focuses on visual cues, such as on-screen detections, textual cues such as named entity recognition, and auditory cues involved in speechto-text conversion. This paper proposes a methodology that leverages state-of-the-art deep learning techniques to extract multimedia cues from videos. Through evaluation across various video contexts, our methodology yields promising results, potentially charting a course for future research endeavors.

Understanding user navigation patterns from clickstream data is crucial for improving business software, yet remains challenging due to the complexity and variability of real-world environments. Unlike controlled settings, real-world clickstreams are noisy, fragmented, and often incomplete, due to session timeouts, network issues, caching, or third-party interactions-making it difficult to reconstruct coherent user journeys. Additionally, the absence of labeled data hinders the use of supervised learning, pushing researchers toward unsupervised or heuristic-based approaches that struggle to fully capture user behavior. In this paper, we present a benchmark of embedding techniques for modeling user navigation behavior on task-oriented software. We identify distinct user behaviors across three real-world case studies. Results show that Pattern2Vec outperforms Word2Vec in capturing meaningful task-based navigation patterns, confirming its suitability for clickstream analysis.

In the Social Internet of Things (SIoT), where users interact in a distributed manner, attackers exploit system vulnerabilities to manipulate trust. These attackers spread false information and services, build deceptive reputations, and gain user trust to achieve malicious goals. Such strategies, known as trust-related attacks, involve falsified ratings or manipulated feedback to artificially boost the reputation of malicious entities within the network. To counter these attacks, trust management systems play an essential role in identifying and mitigating malicious activity. Blockchain technology has revolutionized decentralized and distributed systems, providing enhanced security through various applications. Although integrating blockchain into trust management poses challenges, it significantly improves trust evaluation and strengthens the overall security framework. This work introduces a blockchain-based secure trust management system that addresses vulnerabilities through an authentication layer powered by zero-knowledge proof technology, ensuring privacy and robust validation. In addition, a trust evaluation model based on federated learning is proposed, designed to manage heterogeneous data from diverse SIoT nodes with constrained computational resources. The proposed approach is designed to detect various types of trust-related attacks, fostering trustworthy interactions within SIoT environments.

The Social Internet of Things (Social IoT) introduces a fresh approach to promote the usability of IoT networks and enhance service discovery by incorporating social contexts. However, this approach encounters various challenges that impact its performance and reliability. One of the most prominent challenges is trust, specifically trust-related attacks, where certain users engage in malicious behaviors and launch attacks to spread harmful services. To ensure a trustworthy experience for end-users and prevent such attacks in real-time, it is highly significant to incorporate a trust management mechanism within the Social IoT network. To address this challenge, we propose a novel trust management mechanism that leverages blockchain technology. By integrating this technology, we aim to prevent trust-related attacks and create a secure environment. Additionally, we introduce a new consensus protocol for the blockchain called Spark-based Proof of Trust-related Attacks (SPoTA). This protocol is designed to process stream transactions in real-time using Apache Spark, a distributed stream processing engine. To implement SPoTA, we have developed a new classifier utilizing Spark Libraries. This classifier is capable of accurately categorizing transactions as either malicious or secure. As new transaction streams are read, the classifier is employed to classify and assign a label to each stream. This label assists the SPoTA protocol in making informed decisions regarding the validation or rejection of transactions. Our research findings demonstrate the effectiveness of our classifier in predicting malicious transactions, outstripping our previous works and other approaches reported in the literature. Additionally, our new protocol exhibits improved transaction processing times.

A Language Model is a term that encompasses various types of models designed to understand and generate human communication. Large Language Models (LLMs) have gained significant attention due to their ability to process text with human-like fluency and coherence, making them valuable for a wide range of data-related tasks fashioned as pipelines. The capabilities of LLMs in natural language understanding and generation, combined with their scalability, versatility, and state-of-the-art performance, enable innovative applications across various AI-related fields, including eXplainable Artificial Intelligence (XAI), Automated Machine Learning (AutoML), and Knowledge Graphs (KG). Furthermore, we believe these models can extract valuable insights and make data-driven decisions at scale, a practice commonly referred to as Big Data Analytics (BDA). In this position paper, we provide some discussions in the direction of unlocking synergies among these technologies, which can lead to more powerful and intelligent AI solutions, driving improvements in data pipelines across a wide range of applications and domains integrating humans, computers, and knowledge.

The Social Internet of Things (SIoT) facilitates seamless interactions between IoT devices, providing users with quick and convenient services. However, this domain is vulnerable to manipulation by malicious nodes that issue false recommendations and services to inflate their reputation, leading to trust-related attacks. Developing trust models to detect these attacks in each interaction is challenging due to the complexity of the patterns and features required for accurate prediction. Furthermore, trust metrics are not consistently updated for each node, resulting in inefficiencies and unnecessary resource consumption. To address these challenges, we propose a system that analyzes the context of the current interaction and incorporates temporal factors to monitor node behavior. Our approach employs a decentralized system based on blockchain and IPFS storage, reducing costs and making the process of trust evaluation more efficient and practical for real-time scenarios. This method enhances the detection of trust-related attacks while optimizing resource allocation and execution time.

Integrating the Internet of Things (IoT) with Social Networks (SN) has given rise to a new paradigm called Social IoT, which allows users and objects to establish social relationships. Nonetheless, trust issues such as attacks have emerged. These attacks can influence service discovery results. A trust management mechanism has become a major challenge in the Social IoT to prevent these attacks and ensure qualified services. A few studies have addressed trust management issues, especially those that prevent trust attacks in Social IoT environments. However, most studies have been dedicated to detect offline attacks with or without specifying the type of attack performed. These works will not be able to prevent attacks by aborting transactions between users because their primary purpose is to detect an offline attack. In addition, they do not consider security properties. This research paper aims to provide a detailed survey on trust management mechanism to handle trust attacks in Social IoT. In this research paper, we compared the techniques and technologies whose common point is attack prevention and demonstrated that blockchain technology can play a key role in developing a trust management mechanism that can prevent trust attacks while maintaining security properties. Then, we proposed combining the Apache Spark Framework with blockchain technology to provide real-time attack prevention. This combination can assist in creating upgraded trust management mechanisms in Social IoT environments. These mechanisms aim to prevent attacks in real-time through considering the security properties. Lack of survey papers in the area of trust attack prevention in real-time stands for an important motivational factor for writing this paper. The current research paper highlights the potential of the blockchain technology and Apache Spark in terms of developing an upgraded trust management able to prevent trust attacks in real-time.This paper provides a comprehensive survey on trust management mechanisms and approaches to handle trust attacks in Social IoT. Lack of such papers increases the significance of this paper. It also offers potential future research directions in terms of real-time trust attack prevention.

This review emphasizes the fascinating convergence of Neuro-Symbolic AI (NeSy) and Compositional Generalization (CoGe), examining how these models might potentially transform AI by enabling real human-like intelligence. This research contends that NeSy's capacity to combine the advantages of neural and symbolic techniques has enormous potential for addressing the CoGe dilemma. CoGe necessitates the ability to learn and use information in unexpected settings through the flexible assembly of existing building components. NeSy architectures, with their distinct combination of symbolic reasoning and flexible learning, provide a viable option for overcoming this critical hurdle. In this paper, we highlighted some of the most important concepts of both NeSy and CoGe, showcasing the cutting-edge research trends shaping these fields, we delve into their diverse techniques and methods. Drawing upon cognitive science studies and concrete AI-based works, we illustrate the multitude of possibilities for implementing CoGe within NeSy. Finally, we discuss the results performed by these studies, their commonalities, we then present our proposition and address the open challenges that lie ahead on the path towards true CoGe with NeSy.

The digitization of all processes and the expansion of IoT devices have fostered the emergence of a new form of crime: cybercrime. This term covers a range of malicious acts, the majority of which are now carried out using social engineering strategies-a phenomenon that combines the exploitation of both "human" vulnerabilities and digital tools. The maliciousness of such attacks lies in the fact that they turn users into facilitators of cyber-attacks, making them the "weak link" in cybersecurity. These attacks have increased in both intensity and frequency, causing significant emotional and financial damage to public institutions, businesses of all sizes, and individuals. This research provides an overview of social engineering attacks, existing detection techniques, and the limited effectiveness of current countermeasures, whether due to technological or human factors. Even a robust security system can be easily bypassed by a simple social engineering attack. As current deployment policies prove insufficient, it is necessary to focus on upstream steps: learning how to anticipate attacks, identifying weak signals and outliers, detecting threats early, and reacting quickly to cybercrime. These are priority issues that require a prevention-focused and cooperative approach.

Major transformations related to information technologies affect InformationSystems (IS) that support the business processes of organizations and their actors. Deployment in a complex environment involving sensitive, massive and heterogeneous data generates risks with legal, social and financial impacts. This context of transition and openness makes the security of these IS central to the concerns of organizations. The digitization of all processes and the opening to IoT devices (Internet of Things) has fostered the emergence of a new formof crime, i.e. cybercrime.This generic term covers a number of malicious acts, the majority of which are now perpetrated using social engineering strategies, a phenomenon enabling a combined exploitation of “human” vulnerabilities and digital tools. The maliciousness of such attacks lies in the fact that they turn users into facilitators of cyber-attacks, to the point of being perceived as the “weak link” of cybersecurity.As deployment policies prove insufficient, it is necessary to think about upstream steps: knowing how to anticipate, identifying weak signals and outliers, detect early and react quickly to computer crime are therefore priority issues requiring a prevention and cooperation approach.In this overview, we propose a synthesis of literature and professional practices on this subject.

The rapid expansion of large datasets, encompassing text, images, audio, and video, presents substantial challenges for data labeling, a crucial step in machine learning and data science workflows. Large Language Models (LLMs) provide a promising approach for automating and improving the accuracy of data labeling across various modalities. However, their application in this area introduces specific challenges, such as managing diverse data types, maintaining high-quality annotations, handling computational complexity, and, critically, addressing and mitigating biases associated with automated methods. This vision paper examines the potential of LLMs to enable fair data labeling and validation in crowdsourcing environments, discussing the current landscape, and existing challenges, providing potential future research directions that can help ensure their successful integration.

Intelligent transport systems (ITS) play a pivotal role in enhancing safety, efficiency, and sustainability in modern transportation. Deep learning, a subfield of machine learning, has emerged as a powerful tool for tackling complex problems in intelligent transport applications. In this paper, we first provide a comprehensive overview of the advancements and future prospects of deep learning in intelligent transport systems. It explores various deep learning techniques, their applications, challenges, and potential solutions, contributing to the development of efficient and intelligent transportation networks. We created a hybrid prediction model based on LSTM with a Random forest algorithm to inform drivers' real-time road situations. To evaluate our proposed model, we developed a web application in which the user should subscribe to access the different services and know the real-time situation of his current used road. In the final, we showed the performance of our model by calculating the different metrics of evaluation, which are accuracy and F1_Score.