Existing fact-checking datasets suffer from limitations, such as lack of metadata annotations or insufficient evidence. In this paper, we introduce a comprehensive dataset comprising approximately 5,000 real-world claims, collected from Politifact and Snopes, with the corresponding evidence scraped for each claim. Our dataset uniquely includes detailed metadata such as source credibility metrics (domain age, top level domain score, page rank, bias rating score and factual rating score). Additionally, each claim is segmented into standalone statements, generated using a Large Language Model in order to facilitate efficient evidence retrieval. We further provide an impact analysis score that represents the potential societal influence of each claim. We also develop a baseline automated fact checking pipeline using the proposed dataset.

Given the escalating global threat of phishing emails, it is imperative to develop effective solutions to mitigate their potentially devastating impacts on society. This study endeavours to construct a federated multilingual spam detection system employing logistic regression, specifically targeting English, French, and Russian emails. This is the first work to the best of our knowledge which considers a non-deep learning setting for federated learning, and combines federated learning with multilingual phishing detection. Evaluation of the models is based on accuracy metrics which are compared with a most frequent class baseline. Our findings indicate that an optimal configuration comprises 10 clients undergoing 100 epochs of training with 100 rounds of federated learning, resulting in superior performance. Notably, this approach significantly outperforms the baseline, achieving an accuracy of $89.46 \%$ compared to $70 \%$.

Ransomware attacks are a growing threat to organizations worldwide, with sensitive data encrypted and held hostage for ransom. Although traditional ransomware detection methods, such as signature-based and heuristic detection, are effective to some extent, they struggle to identify ransomware before encryption begins due to evasion techniques like code obfuscation and polymorphism. This work aims to address this gap by developing a detection method that can identify ransomware early by analyzing static and dynamic features. The system tests ransomware samples using static analysis of Portable Executable (PE) files without execution and dynamic analysis in a sandbox environment to capture pre-encryption behaviors and features. A set of state-of-the-art machine learning algorithms is employed to classify ransomware activity based on these behavioral patterns. The goal is to identify ransomware activity before it executes encryption. By establishing a framework for early ransomware detection, this study provides a pathway for scalable detection systems that can adapt to evolving ransomware threats.

Electric Vehicles (EVs) are considered the predominant method of decreasing fossil fuels as well as greenhouse gas emissions. With the drastic growth of EVs, the future smart grid is expected to extensively incorporate dynamic wireless charging (DWC) systems, a significant advancement over traditional charging methods. DWC, offering the unique ability to charge vehicles in motion, introduces new infrastructures, complex network models and consequently, a massive attack surface. To accomplish the goal of such an enormous smart grid accompanying DWCs, the security of EV charging infrastructures has become a deciding factor. EV charging is vulnerable to cyberattacks as it has many attack vectors and many challenges to combat. Unlike the traditional charging services provided in a typical static charging station, the DWC has a complex network architecture which makes it vulnerable to many forms of cyberattacks. Authentication plays a crucial role in safeguarding the frontline security of this ecosystem. However, within the domain of DWC, the current academic landscape has seen limited attention dedicated to authentication protocols. This background signifies the necessity of a comprehensive survey to cover the authentication protocols of dynamic wireless EV charging environments. This review paper examines the security requirements and the network model of the DWC, providing comprehensive insights into existing authentication protocols by scrutinizing a proper classification. Furthermore, the paper addresses existing challenges in authentication schemes within DWC and explores potential future research tendencies aiming to strengthen the security framework of this emerging technology.

In recent years, smart grid-based Electric Vehicle (EV) charging systems have increasingly faced vulnerabilities to Distributed Denial of Service (DDoS) attacks, especially through malicious authentication failures. These attacks typically involve monopolizing the Grid Server (GS), thereby hindering the authentication process for legitimate EVs. Despite the severity of this issue, no research (to the best of our knowledge) has focused on detecting DDoS attacks exploiting weaknesses in EV authentication. This study introduces a DDoS attack detection model specifically designed for EV authentication. The approach involves developing a machine learning model involving unique feature selection and combination. The proposed approach has been evaluated using a new DDOS attack dataset. The model is engineered to optimize feature combination, aiming for high sampling resolution, minimal information loss, and robust performance under 16 distinct attack scenarios. The feature combination used in this study shows improved accuracy over traditional DDoS detection methods based on access time variation while minimizing information loss.

Extensive studies have indicated that fake news has become one of the major threats to our social system (e.g., influencing public opinion, financial markets, journalism, and health system), and its impact cannot be understated, particularly in our current socially and digitally connected society. In the past years, this problem has been investigated from different perspectives and various disciplines, such as computer science, political science, information science, and linguistics. Even though such efforts have proposed many helpful solutions, it remains challenging to detect fake news in its early phases of dissemination. Based on previously reported studies, detecting fake news early after its propagation is a very tough task due to the unavailability of context-based features within the first hours of spreading and the ineffectiveness of merely content-based features methods. To address this challenge, we propose a new framework for detecting fake news in the early stages of its propagation. The first three components of the proposed framework convert each news article’s propagation network into a sequence of nodes after preprocessing and feature extraction. The last module of our framework leverages a self-attention mechanism-based encoder. Self-attention technique is the core of the well-known transformer model, which has achieved promising results in different areas, especially in complex tasks such as language translation. In the module, a new representation of the input sequence is generated, which is mapped to a label for the news article by a binary classifier. We evaluated our method on two datasets and achieved promising results. The achieved F1 scores by the proposed model on the GossipCop and PolitiFact datasets are higher than the best baseline model by 9% and 6%, respectively.

Fake news is a major threat to democracy (e.g., influencing public opinion), and its impact cannot be understated particularly in our current socially and digitally connected society. Researchers from different disciplines (e.g., computer science, political science, information science, and linguistics) have also studied the dissemination, detection, and mitigation of fake news; however, it remains challenging to detect and prevent the dissemination of fake news in practice. In addition, we emphasize the importance of designing artificial intelligence (AI)-powered systems that are capable of providing detailed, yet user-friendly, explanations of the classification / detection of fake news. Hence, in this article, we systematically survey existing state-of-the-art approaches designed to detect and mitigate the dissemination of fake news, and based on the analysis, we discuss several key challenges and present a potential future research agenda, especially incorporating AI explainable fake news credibility system.

Denial-of-Service (DoS) or Distributed DoS (DDoS) attacks are on the rise in smart grid-based electric vehicle (EV) charging facilities. To develop effective mitigation solutions against such attacks, a dataset containing different attack scenarios is of vital importance. There is no such comprehensive dataset available as of now. To fill this research gap, in this work, we have created a new dataset, namely CICEV2023, which contains four different attack scenarios on EVs within smart grid infrastructure. To achieve this, we developed a simulator that establishes an authentication protocol on EV charging infrastructure and launches DDoS attacks related to EV authentication.

With phishing emails being a major problem worldwide which is only getting larger by the year, there needs to exist solutions to combat them as they can cause tremendous harm to society. This research aims to compare numerous machine learning models and transformers for multilingual spam detection using English, French, and Russian emails. We evaluate the models using accuracy as two of the three experiments have nearly balanced test data. Our results show that, on average, XLM-Roberta performs the best out of all of the tested models in terms of accuracy.

Fault diagnostics involving the Internet-of-Things (IoT) sensors and edge devices is a challenging task due to their limited energy and computational capabilities. Another challenge concerning IoT sensors or devices is the incursion of noise when used in an industrial environment. The noisy samples affect the decision support system that could lead to financial and operational losses. This article proposes a noisy encoder using artificial intelligence of things (NEAT) architecture for fault diagnosis in IoT edge devices. NEAT combines autoencoders and Inception module to co-train the clean and noisy samples for solving the said problem. Experimental results on benchmark data sets reveal that the NEAT architecture is noise resilient in comparison to the existing works. Furthermore, we also show that the NEAT architecture has lightweight characteristics as it yields a lower number of parameters, weight storage, training, and testing times that support its real-life applicability in an Industrial IoT environment.

Healthcare big data (HBD) allows medical stakeholders to analyze, access, retrieve personal and electronic health records (EHR) of patients. Mostly, the records are stored on healthcare cloud and application (HCA) servers, and thus, are subjected to end-user latency, extensive computations, single-point failures, and security and privacy risks. A joint solution is required to address the issues of responsive analytics, coupled with high data ingestion in HBD and secure EHR access. Motivated from the research gaps, the paper proposes a scheme, that integrates blockchain (BC)-based confidentiality-privacy (CP) preserving scheme, CP-BDHCA, that operates in two phases. In the first phase, elliptic curve cryptographic (ECC)-based digital signature framework, HCA-ECC is proposed to establish a session key for secure communication among different healthcare entities. Then, in the second phase, a two-step authentication framework is proposed that integrates Rivest-Shamir-Adleman (RSA) and advanced encryption standard (AES), named as HCA-RSAE that safeguards the ecosystem against possible attack vectors. CP-BDAHCA is compared against existing HCA cloud applications in terms of parameters like response time, average delay, transaction and signing costs, signing and verifying of mined blocks, and resistance to DoS and DDoS attacks. We consider 10 BC nodes and create a real-world customized dataset to be used with SEER dataset. The dataset has 30,000 patient profiles, with 1000 clinical accounts. Based on the combined dataset the proposed scheme outperforms traditional schemes like AI4SAFE, TEE, Secret, and IIoTEED, with a lower response time. For example, the scheme has a very less response time of 300 ms in DDoS. The average signing cost of mined BC transactions is 3,34 seconds, and for 205 transactions, has a signing delay of 1405 ms, with improved accuracy of ≈ 12% than conventional state-of-the-art approaches.

The Intelligent Transportation System (ITS) is said to revolutionize the travel experience by making it safe, secure, and comfortable for the people. Although vehicles have been automated up to a certain extent, it still has critical security issues that require thorough study and advanced solutions. The security vulnerabilities of ITS allows the attacker to steal the vehicle. Therefore, the identification of drivers is required in order to develop a safe and secure system so that the vehicles can be protected from theft. There are two ways in which a driver can be identified: 1) face recognition of the driver, and 2) based on driving behavior. Face recognition includes image processing of 2-D images and learning of the features, which require high computational power. Drivers are known to have unique driving styles, whose data can be captured by the sensors. Therefore, the second method identifies drivers based on the analysis of the sensor data and it requires comparatively lesser computational power. In this paper, an optimized deep learning model is trained on the sensor data to correctly identify the drivers. The Long Short-Term Memory (LSTM) deep learning model is optimized for better performance. The novelty of the approach in this work is the inclusion of hyperparameter tuning using a nature-inspired optimization algorithm, which is an important and essential step in discovering the optimal hyperparameters for training the model which in turn increases the accuracy. The CAN-BUS dataset is used for experimentation and evaluation of the training model. Evaluation parameters such as accuracy, precision score, F1 score, and ROC AUC curve are considered to evaluate the performance of the model.

Social Internet of Things (SIoT) is an extension of the Internet of Things (IoT) that converges with social networking concepts to create social networks of interconnected smart objects. This convergence allows the enrichment of the two paradigms, resulting into new ecosystems. While IoT follows two interaction paradigms, human to human (H2H) and thing to thing (T2T), SIoT adds on human-to-thing (H2T) interactions. SIoT enables smart “social objects” that intelligently mimic the social behavior of human in the daily life. These social objects (SOs) are equipped with social functionalities capable of discovering other SOs in the surroundings and establishing social relationships. They crawl through the social network of objects for the sake of searching for services and information of interest. The notion of trust and trustworthiness in social communities formed in SIoT is still new and in an early stage of investigation. In this article, our contributions are threefold. First, we present the fundamentals of SIoT and trust concepts in SIoT, clarifying the similarities and differences between IoT and SIoT. Second, we categorize the trust management solutions proposed so far in the literature for SIoT over the last six years and provide a comprehensive review. We then perform a comparison of the state-of-the-art trust management schemes devised for SIoT by performing comparative analysis in terms of trust management process. Third, we identify and discuss the challenges and requirements in the emerging new wave of SIoT, and also highlight the challenges in developing trust and evaluating trustworthiness among the interacting SOs.

Summary In the competing era of online industries, understanding customer feedback and satisfaction is one of the important concern for any business organization. The well‐known social media platforms like Twitter are a place where customers share their feedbacks. Analyzing customer feedback is beneficial, as it provides an advantage way of unveiling customer interests. The proposed system, namely Senti‐eSystem, aims at the development of sentiment‐based eSystem using hybridized Fuzzy and Deep Neural Network for Measuring Customer Satisfaction to assist business organizations for improving the quality of their services and products. The proposed approach initially deploys a Bidirectional Long Short Term Memory with attention mechanism to predict the sentiment polarity that is positive and negative, followed by Fuzzy logic approach to determine the customer satisfaction level, which further strengthens the capabilities of the proposed approach. The system achieves an accuracy of 92.86%, outperforming the previous state‐of‐art lexicon‐based approaches. Moreover, the effectiveness of the proposed system is also validated by applying the statistical test.

With the emergence of wearable technology, IoT, and Edge computing, the nature of healthcare is rapidly shifting towards digital health aided by these ICT technologies. At the same time, consumer devices, such as smart, wearable fitness watches are gaining market share as a way to monitor physical activity and wellness. Despite these advances, and their ability to capture longitudinal behavioural patterns, these devices have yet to be fully leveraged within the healthcare system. If the user-generated data from such devices could be collected without com-promising an individual’s privacy, these insights could comprise part of a more holistic and preventative healthcare solution. In this article, we propose an Edge-assisted data analytics frame-work that uses Federated Learning to re-train local machine learning models using user-generated data. This framework could leverage pre-trained models to extract user-customized insights while preserving privacy and Cloud resources. We also identify some potential application scenarios and discuss research challenges to be explored within the proposed framework.