ABSTRACT Counterfeit medical devices pose a threat to patient safety, necessitating a secure device authentication system for medical applications. Resource‐constrained sensory nodes are vulnerable to hacking, prompting the need for robust security measures. Token‐based authentication schemes, such as one‐time passwords (OTPs), smart cards, key fobs, and mobile authentication apps, along with certificate‐based authentication methods, such as client and code‐signing, employ cryptographic frameworks like elliptical curve cryptography (ECC) and physical unclonable functions (PUF). However, these methods face challenges, including block sequence issues and susceptibility to side‐channel attacks. To address these issues, we propose a framework for mutual authentication using private Ethereum. This framework integrates private Ethereum and cryptographic techniques for encrypting and decrypting data using mathematical algorithms to overcome block sequence issues and side‐channel attacks. Similarly, fog nodes are utilised to enhance local computing, storage, and networking capabilities for sensors. The framework is evaluated using metrics such as communication costs, execution costs, and computation costs based on Ethereum gas consumption. The performance of the LightAuth framework is compared with that of the Smart Contracts Against Counterfeit IoMT (SCACIoMT) framework, designed for Internet of Medical Things (IoMT) devices. The effectiveness of LightAuth is verified through formal security analysis using BAN logic.

Abstract The rapid evolution of electronic media in recent decades has exponentially amplified the propagation of fake news, resulting in widespread confusion and misunderstanding among the masses, especially concerning critical topics like the COVID‐19 pandemic. Consequently, detecting fake news on social media has emerged as a prominent area of research, attracting significant attention. This article introduces a novel cascaded group multi‐head attention (CGMHA) model for COVID‐19 fake news detection. Our research collected Twitter datasets with accurate and fake tweets in Urdu. The novel CGMHA model and depth‐wise convolution capture local and global contextual information by employing multiple attention heads in a cascaded fashion, enabling a comprehensive understanding of fake news. While achieving state‐of‐the‐art performance, we also highlight challenges such as language variations and misinformation nuances in the detection process, contributing to a more comprehensive understanding of the complexities involved in combatting fake news. Our proposed model surpasses the performance of state‐of‐the‐art models in classifying fake news and achieves accuracy, F1 score, precision, and recall of 0.98, 0.96, 0.95, and 0.95, respectively.

Abstract In security defense of MSN (MSN), attackers often impersonate themselves as other users, making it difficult to detect network user attacks based on user behavior. Multi-order Markov chains can consider the front-to-back correlation of user behavior, thereby more accurately identifying disguised users. Therefore, this paper proposes a user impersonation attack detection method based on multi-order Markov chains. First, the relevance coefficient method is used to determine the order of the multi-order Markov chain, and by defining appropriate multi-order Markov chain states to capture key features in user behavior, a multi-order Markov chain is established. Then, through the multi-order Markov chain combined with Shell commands, the normal behavior profile of legitimate users is established, and based on this, the probability of occurrence of the state sequence is calculated to complete the detection of userimpersonation attacks. The experimental results show that the similarity between the results of the proposed method and the actual situation in detecting impersonation attacks is more than 97%, indicating that this method can detect MSN user impersonation attacks with high accuracy.

Summary The rapid growth of the Internet of Things (IoT) has led to its widespread adoption in various industries, enabling enhanced productivity and efficient services. Integrating IoT systems with existing enterprise application systems has become common practice. However, this integration necessitates reevaluating and reworking current Enterprise Architecture (EA) models and Expert Systems (ES) to accommodate IoT and cloud technologies. Enterprises must adopt a multifaceted view and automate various aspects, including operations, data management, and technology infrastructure. Machine Learning (ML) is a powerful IoT and smart automation tool within EA. Despite its potential, a need for dedicated work focuses on ML applications for IoT services and systems. With IoT being a significant field, analyzing IoT‐generated data and IoT‐based networks is crucial. Many studies have explored how ML can solve specific IoT‐related challenges. These mutually reinforcing technologies allow IoT applications to leverage sensor data for ML model improvement, leading to enhanced IoT operations and practices. Furthermore, ML techniques empower IoT systems with knowledge and enable suspicious activity detection in smart systems and objects. This survey paper conducts a comprehensive study on the role of ML in IoT applications, particularly in the domains of automation and security. It provides an in‐depth analysis of the state‐of‐the‐art ML approaches within the context of IoT, highlighting their contributions, challenges, and potential applications.

Due to the increase in world population, arable land has been reduced. Consequently, the concept of urban greenhouses is on the rise. Smart greenhouses need to monitor physical parameters for the healthy growth of plants from remote locations. A digital twin is a representation of physical assets in the digital world, and this emerging technology has opened up opportunities for efficient system development for Industry 4.0. The digital twin receives real-time operational data to monitor the asset in the digital domain. It performs real-time processing, data analysis, and machine learning to predict optimized decisions. In the era of next-generation mobile networks, IoT devices can communicate and perform their remote operations in a timely manner. In smart greenhouse technology, the digital twin could be a revolutionary substitute for real-time remote monitoring and process management. However, there has been limited work on digital twin-driven smart greenhouse technology. In this paper, a process management framework is developed that can be interpreted as a machine learning and cloud-based data-driven digital twin for smart greenhouses. The proposed framework consists of three layers: the physical, fog, and cloud layers. The physical greenhouse measurements are monitored using a highly immersive cloud-based, real-time 3D environment. We present an example architecture using commercial cloud and open-source tools to verify the proof of concept. Additionally, different ML techniques are utilized to predict the operational requirements for smart greenhouses.

In recent years, the Internet has facilitated the emergence of social media platforms as significant channels for individuals to express their thoughts and engage in instantaneous interactions. However, the reliance on online reviews has also given rise to deceptive practices, where anonymous spammers generate fake reviews to manipulate the perception of a product. Ensuring the integrity of the online review system requires identifying and mitigating fake reviews. While existing machine learning (ML)- and neural network (NN)-based sentiment analysis methods can detect deceptive reviews, they often suffer from long training times, high computational resource requirements, and memory constraints. This study aims to overcome these limitations by introducing a transformer-based “deceptive bidirectional encoder representations from transformers (DeceptiveBERT) model.” This model utilizes contextual representations to enhance the precision of deceptive review identification. Transfer learning is employed to leverage knowledge from a pre-existing BERT base-uncased word embedding model, enabling efficient feature extraction. The proposed model incorporates a combination of classification layers to categorize reviews into two distinct categories: deceptive and truthful. Additionally, the study addresses the challenge of imbalanced datasets by utilizing three separate datasets and implementing appropriate methodologies for dataset curation. The effectiveness of the DeceptiveBERT model was evaluated through experimentation. The results demonstrate its efficacy, with the model achieving accuracy rates of 75%, 84.79%, and 81.08% on the Ott, YelpNYC, and YelpZip datasets, respectively.

Abstract Every individual in our technologically evolved world needs proper data security. The procedure of exchanging medical information is increasingly concerned with data privacy. Many techniques have been offered for preserving data security. These techniques use approaches such as ‐anonymity, ‐diversity, and others. However, such solutions are vulnerable to attribute disclosure, homogeneity, and background knowledge risks due to their syntactic nature. In this work, we describe a safe and secure architecture and semantic approach for data sharing that is based on blockchain, local differential privacy (LDP), and federated learning (FL). The proposed framework generates an atmosphere devoid of trust in which data owners are no longer required to have trust in the controllers. The FL models enable the whole network to decentralize its data‐driven learning. Interplanetary file system (IPFS) is used to provide data security in a distributed environment because each file in IPFS has a digital fingerprint that is computed using a cryptographic hash function on the file's whole contents. Due to the rigorous privacy guarantee, data owners no longer need to be worried about the security of their data. The proposed model's assessment parameters include latency, throughput, privacy, and accuracy. The data privacy of the proposed model is protected via LDP and FL, and its latency and throughput communication transactions on permissioned blockchain are calculated and compared with those of the benchmark model. The findings indicate that the proposed model delivers 85% more accurate privacy than the benchmark model.

Recent developments in the Internet of Vehicles (IoV) and vehicular adhoc networks (VANET) have revolutionized our infrastructure, making it safer, more convenient, and efficient. VANET provide smart traffic control, event allocation, and real-time information. Existing vehicles in VANET are now equipped with intelligent navigation, entertainment, and emergency applications. However, the highly connected nature of these vehicles poses a significant safety and security risk to drivers and assets which can result in life-threatening consequences. Location privacy is critical, and robust network security techniques should be used to counter threats in VANET environments. Existing schemes like obfuscation, mix-zones, and silent periods have preserved location privacy to some extent but have poor Quality of Service (QoS) and lack both efficiency and security. To address these issues, a shadowing scheme is introduced, which is an improvement of earlier schemes used for location privacy. This approach ensures better service to the vehicle by allowing precise location-based service (LBS) requests to the LBS server and uses blockchain technology for storing vehicular certificates. The inclusion of a group leader significantly reduces the time taken for implementing the scheme, improving efficiency and scalability. The anonymity set size increases over time, offering better privacy protection especially in densely populated areas. The proposed scheme overcomes drawbacks of existing techniques which includes reduced location accuracy and low-quality service in spatial obfuscation techniques, limited applicability and high tracking rate in shadow-based approaches, and reduced utility in distance-based schemes. Moreover, single point of failure and resource-intensive group formation in group-based schemes, and dependency on additional infrastructure in mix-zone-based schemes are also overcome. The proposed scheme’s experimental results validate it, showing that it outperforms current state-of-the-art schemes based on metrics, such as anonymity set size, entropy, and tracking success ratio.

Data publication and sharing are critical components of assessing network infrastructures in the Internet of Things for quality-of-service enhancement. Especially, the advancement in communication technology (e.g., 5G/6G) enables the improvement of the current bottlenecks in the Industrial Internet of Things. Recent approaches remove raw data and their source to achieve a privacy guarantee. However, the data are already anonymized; these still reveal the victim’s extra information using linkage attacks. When data are updated and combined or noise is introduced as part of conventional privacy protection approaches, such as$k$-anonymity, l-diversity, or differential privacy, the usefulness of the released data is diminished, however, posing data utility and computation constraints. In recent years, lightweight privacy-preservation techniques have been proposed for these reasons. However, most of the focus is on syntactic privacy instead of semantic privacy guarantee. Therefore, this article proposes a lightweight semantic privacy-preservation framework for maintaining privacy with high utility efficiency. The proposed paradigm ensures semantic privacy by combining probabilistic random sampling with Instant_Anonymity. Compared to$k$-anonymity, the suggested model demonstrates improved data utility with lower utility errors of 0.00036 and 0.41 for Kullback–Leibler divergence and query error, respectively. The classification accuracy is improved by 0.2%. In addition, the proposed approach is simpler to implement in computation time than the existing state-of-the-art lightweight privacy-preserving strategies.

Smart health-care is the innovation that leads to enhanced diagnostic tools, improved patient treatment, and gadgets that ease the quality of life for majority of people. Textual clinical documents about an individual contain sensitive and semantically corelated terms. Most privacy-preserving approaches are not designed to prevent confidentiality threats. Although, recent approaches improved the utility of published output with generalized terms retrieved from several medical and general-purpose knowledge bases like SNOMED-CT and MASH. However, these models work on predefined sensitive terms using Wikipedia articles instead of authentic benchmarks. These Information Content-based methods are not capable to achieve the best balance between privacy and utility. The existing approaches guarantee syntactic privacy by sanitization but lack semantic privacy for textual clinical data. Therefore, it is imperative to design a confidentiality-aware framework to overcome these problems. Our proposed Confidentiality aware Textual Clinical Data Framework use preprocessed combinations of the terms instead of all combinations and perform automatic detection and sanitization of the sensitive and semantically correlated terms. The probabilistic sampling-based method guarantees the semantic privacy. We use high-level Petri nets to perform formal modeling of our proposed approach. Furthermore, we have also performed a detailed complexity analysis of the proposed framework.

Due to the Internet of Things evolution, the clinical data is exponentially growing and using smart technologies. The generated big biomedical data is confidential, as it contains a patient’s personal information and findings. Usually, big biomedical data is stored over the cloud, making it convenient to be accessed and shared. In this view, the data shared for research purposes helps to reveal useful and unexposed aspects. Unfortunately, sharing of such sensitive data also leads to certain privacy threats. Generally, the clinical data is available in textual format (e.g., perception reports). Under the domain of natural language processing, many research studies have been published to mitigate the privacy breaches in textual clinical data. However, there are still limitations and shortcomings in the current studies that are inevitable to be addressed. In this article, a novel framework for textual medical data privacy has been proposed as Deep-Confidentiality . The proposed framework improves Medical Entity Recognition (MER) using deep neural networks and sanitization compared to the current state-of-the-art techniques. Moreover, the new and generic utility metric is also proposed, which overcomes the shortcomings of the existing utility metric. It provides the true representation of sanitized documents as compared to the original documents. To check our proposed framework’s effectiveness, it is evaluated on the i2b2-2010 NLP challenge dataset, which is considered one of the complex medical data for MER. The proposed framework improves the MER with 7.8% recall, 7% precision, and 3.8% F1-score compared to the existing deep learning models. It also improved the data utility of sanitized documents up to 13.79%, where the value of the k is 3.

The growing use of electronic health records in the medical domain results in generating a large amount of medical data that is stored in the form of clinical notes. These clinical notes are enriched with clinical entities like disease, treatment, tests, drugs, genes, and proteins. The extraction of clinical entities from clinical notes is a challenging task as clinical notes are written in the form of natural language. The extraction of clinical entities has many useful applications such as clinical notes analysis, medical data privacy, decision support systems, and disease analysis. Although various machine learning and deep learning models are developed to extract clinical entities from clinical notes, developing an accurate model is still challenging. This study presents a novel deep learning-based technique to extract the clinical entities from clinical notes. The proposed model uses local and global context to extract clinical entities in contrast to existing models that use only global context. The combination of CNN, Bi-LSTM, and CRF with non-complex embedding (proposed model) outperforms existing models by a margin of 4-10% and 5-12% in terms of F1-score on i2b2-2010 and i2b2-2012 data. The accurate detection of clinical entities can be helpful in the privacy preservation of medical data that increases the user's and medical organization's trust in sharing medical data.