Next-generation industrial networks incorporate diverse devices and technologies, including cloud- and fog-based systems and large-scale Industrial Internet of Things (IIoT) applications. These heterogeneous environments comprise thousands of sensors, actuators, controllers, and geographically distributed zones, posing significant challenges for authentication mechanisms that must scale while remaining secure and practical under real industrial conditions. Current blockchain-based decentralized identity-management systems are characterized by high transaction overhead, reliance on computationally intensive cryptographic operations, and partial dependence on trusted entities, all of which limit their scalability and practical deployment. To address these issues, in this paper, we propose a lightweight, blockchain-based identity-management solution that integrates epoch- and batch-based registration with sparse Merkle trees (SMTs). Device registrations are grouped into fixed epochs, and cryptographic costs are amortized across multiple devices to substantially reduce per-device overhead. The SMT structure generates verifiable, reusable proofs that preserve privacy while enabling efficient verification; sensitive device information is never revealed during authentication. Fog nodes act as decentralized verifiers, supporting distributed trust without continuous interaction with the blockchain. Experimental results show that the system can be practically deployed on Raspberry Pi and ESP32 devices, reliably perform identification with around 1000 total messages, and operate at the tested upper bound of 7000 devices distributed over 20 fog nodes, all while keeping the blockchain storage overhead under 5 MB.

Identifying attacks on Internet of Things (IoT) systems through anomaly detection is an effective approach and remains a crucial area of research. The core method involves collecting system-related data during normal operation to establish a baseline of typical behavior and then continuously monitoring for deviations from this baseline. Using system call sequences for anomaly detection is a well-established and important field. System call sequences effectively capture the behavior of a target system at a low level, allowing identification of any changes in this behavior; however, these approaches face several challenges, including high false-positive rates, the need for segmentation of long sequences, and the difficulty of detecting anomalies when the system call data comes from multiple processes. This work presents a novel anomaly-detection approach that uses a position-specific scoring mechanism to analyze the content and structural properties of system call sequences. The proposed approach addresses key challenges in this field, including fixed-length segmentation of system call sequences, predetermined anomaly-detection thresholds, the detection of anomalies in both single and multiple processes, and high false-positive rates. We extensively evaluated the proposed approach using system-call-specific public datasets (ADFA-LD and UNM) of a diverse nature. The performance of the proposed content-based, structure-based, and combined content- and structure-based anomaly-detection methods was evaluated using ten-fold cross-validation. The proposed anomaly-detection approach achieves an impressive detection rate of 1.0, along with exceptionally low false-positive rates of 0.001 and 0.017 when evaluated on the UNM and ADFA-LD datasets, respectively.

Cardiovascular disease (CVD) is a leading cause of death globally. The unpredictability and severity of CVDs, such as sudden cardiac arrests, necessitate real-time monitoring and prediction using the Internet of Things (IoT) and artificial intelligence (AI) for timely intervention. Existing AI models and IoT frameworks for CVD prediction often lack integration of diverse data and fail to provide transparency in predictions, reducing user confidence and treatment effectiveness. We propose an explainable-IoT framework leveraging eXplainable AI (XAI) in the cloud layer and a mobile application to swiftly communicate predictions and explainability to patients, healthcare providers, and other users, facilitating proactive management and informed decisions. The framework integrates sensor data with cloud-based medical records to improve cardiovascular care and build user trust. Using support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and deep neural network (DNN), we develop and evaluate CVD prediction models on two datasets: a heart disease dataset (D1) and the Cleveland dataset (D3). Additionally, we use a synthetic dataset (D2) for comparative analysis. The models are evaluated based on accuracy, precision, recall, area under the curve, time, and F1 score. For D3, SVM achieved the best accuracy (84.62%), while RF performed best on D1 and D2 (92.44% and 98.06%, respectively), comparable to state-of-the-art works. Our results highlight the importance of underrepresented physiological features in CVD datasets and the need for comprehensive datasets to enhance CVD model development. Furthermore, while synthetic data (D2) is effective for initial modeling, it requires validation with real-world data for reliable CVD prediction. • Developed an AI-IoT framework for real-time CVD prediction using diverse datasets. • Compared real and synthetic datasets, emphasizing physiological features for CVD accuracy. • Integrated a mobile app for real-time data and improved CVD prevention and management.

Over the past two decades, the rapid growth of the Internet of Things (IoT) has begun to transform traditional healthcare systems into intelligent systems; however, hospitals have encountered challenges in securely storing patient data within centralized architectures due to their lack of efficiency and security features. Blockchain technology offers a secure and reliable decentralized framework for storing and sharing healthcare data among various stakeholders, including patients, doctors, nurses, insurance companies, and pharmaceutical firms. In this article, we propose a blockchain-based data-protection scheme deployed at edge nodes. The proposed scheme uses the interplanetary file system (IPFS) model to address storage and data-protection issues in an IoT-edge-enabled smart healthcare system. First, the security issues in smart healthcare systems are identified, and the impact of these issues on patient privacy and hospital infrastructure is considered. Then, a technique based on the 128-bit Advanced Encryption Standard is proposed to encrypt patient information and store it in an IPFS-based decentralized network. Edge-computing techniques are used to perform computations at the edge level within a decentralized architecture, thereby addressing the computational challenges associated with cloud computing. Lastly, the encryption keys are stored using blockchain technology to address the issue of restricted computational power on low-end devices through off-chain and on-chain business processes. The experimental results demonstrate that the proposed scheme achieves a key management time of 0.2 ms, file retrieval time of 0.57 s, throughput of 0.11 Mb/s, encryption time of 1.96 ms, and decryption time of 1.91 ms. These findings indicate that the proposed scheme outperforms previously reported approaches with respect to key management time, file retrieval efficiency, and its potential for edge deployment and off-chain capabilities. Consequently, the proposed scheme is highly suited for efficiently securing patient data within IoT-enabled smart healthcare systems.

Identifying attacks on Internet of Things (IoT) systems through anomaly detection remains a critical area of research. One common and effective strategy in this field involves monitoring system-related data during normal operation to establish a baseline of expected behavior, followed by continuous monitoring to identify deviations from this baseline. System call sequences, which provide a low-level representation of the behavior of a system, are widely regarded as a valuable resource for anomaly detection; however, challenges such as the categorical nature of system call data, inconsistencies in sequence lengths, repeating patterns, and the diversity of activities across single-and multi-process environments complicate the effectiveness of existing methods. To address these challenges, we propose a centroid-based anomaly detection approach that transforms IoT system call data into word vectors, creating a central vector to represent normal behavior. A weighted vector-space model is then used to set a threshold distance for distinguishing between normal and malicious sequences. The effectiveness of the proposed method is evaluated across three distinct datasets: the Australian Defense Force Academy Linux Dataset (ADFA-LD) and the University of New Mexico (UNM) datasets, including UNM-Sendmail and UNM-Line Printer Remote (LPR). The method surpasses existing approaches on the ADFA-LD dataset, achieving an accuracy of 99.02%, a false-positive rate (FPR) of 1.96%, and an area under the receiver operating characteristic curve (AUC) of 0.9923. For the UNM datasets, the performance metrics indicate a detection accuracy of 99.7%, an FPR of 0.28%, and an AUC of 0.9983. The average processing time was measured as 1–3 ms. The experimental results and subsequent analysis reveal promising performance, demonstrating the generalizability of the proposed method across various datasets.

Next-generation industrial networks are designed to integrate a wide range of devices, services, and applications spanning multiple technologies, such as cloud platforms, edge computing, and the Internet of Things. With the growing adoption of applications such as “Industry 4.0,” high security and low latency are becoming unavoidable requirements for these networks. Traditional virtual private networks (VPNs) generally experience performance, latency, and security issues, especially when supporting secure remote access for Industry 4.0 and ehealth applications. To address these issues, this study introduces a novel zero-trust network-access framework for next-generation industrial networks called Secure Transmission (SecT). SecT is a User Datagram Protocol (UDP)-based solution, ensuring speed and effectiveness, with role-based access control. It uses a centralized management interface that can adapt to various network environments, providing secure access to mission-critical applications and increasing operational agility. SecT aims to meet the emerging demands of modern industrial networks, offering secure access with improved performance. The results of a comparative analysis show that SecT outperforms traditional VPNs in both capability and flexibility, adapting well to new network conditions.

The Internet of Things (IoT) has recently gained significance as a means of connecting various physical devices to the Internet, enabling various innovative applications. However, the security of IoT networks is a significant concern due to the large volume of data generated and transmitted over them. The limited resources of IoT devices, along with their mobility and diverse characteristics, pose significant challenges for maintaining security in routing protocols, such as the Routing Protocol for Low-Power and Lossy Networks (RPL). This lacks effective defense mechanisms against routing attacks, including Sybil and rank attacks. Various techniques have been proposed to address this issue, including cryptography and intrusion-detection systems. The use of these techniques on IoT nodes is limited by their low power and lossy nature, primarily due to the significant computational overhead they involve. In addition, conventional trust-management systems for addressing security concerns need to be improved due to their high computation, memory, and energy costs. Therefore, this paper presents a novel, Lightweight, and Efficient Trust-based Mechanism (LETM-IoT) for resource-limited IoT networks to mitigate Sybil attacks. We conducted extensive simulations in Cooja, the Contiki OS simulator, to assess the efficacy of the proposed LETM-IoT against three types of Sybil attack (A, B, and C). A comparison was also made with standard RPL and state-of-the-art approaches. The experimental findings show that LETM-IoT outperforms both of these in terms of average packet-delivery ratio by 0.20 percentage points, true-positive ratio by 1.34 percentage points, energy consumption by 2.5%, and memory utilization by 19.42%. The obtained results also show that LETM-IoT consumes increased storage by 5.02% compared to the standard RPL due to the existence of an embedded security module.

The Federal Aviation Administration (FAA) recently introduced a new standard, namely, remote identification, to improve accountability for unmanned aerial vehicles (UAVs) operations. This rule requires UAV operators to broadcast messages revealing sensitive data, such as identity and location on the wireless channel. However, this leads to security and privacy concerns among UAV operators. Unauthorized parties may easily discover the location and identity of a UAV flying in a specific area and launch attacks on it such as using wireless jamming or tracking its activity. This review investigates and systematizes the main weaknesses affecting the Remote ID capability required of modern UAVs, and the approaches through which attackers can exploit these weaknesses to disrupt safety and accountability. Moreover, this article analyzes current solutions that mitigate privacy issues associated with Remote ID. Finally, we identify multiple challenges that require to be addressed by both industry and academia, and we propose future research directions to improve the security and privacy of UAVs.

The Internet of Things (IoT) has become an enabler paradigm for different applications, such as healthcare, education, agriculture, smart homes, and recently, enterprise systems. Significant advances in IoT networks have been hindered by security vulnerabilities and threats, which, if not addressed, can negatively impact the deployment and operation of IoT-enabled systems. This article addresses IoT security and presents an intelligent two-layer intrusion detection system for IoT. The system's intelligence is driven by machine learning techniques for intrusion detection, with the two-layer architecture handling flow-based and packet-based features. By selecting significant features, the time overhead is minimized without affecting detection accuracy. The uniqueness and novelty of the proposed system emerge from combining machine learning and selection modules for flow-based and packet-based features. The proposed intrusion detection works at the network layer, and hence, it is device and application transparent. In our experiments, the proposed system had an accuracy of 99.15% for packet-based features with a testing time of 0.357 μs. The flow-based classifier had an accuracy of 99.66% with a testing time of 0.410 μs. A comparison demonstrated that the proposed system outperformed other methods described in the literature. Thus, it is an accurate and lightweight tool for detecting intrusions in IoT systems.

In recent years, mobile devices have become commonly used not only for voice communications but also to play a major role in our daily activities. Accordingly, the number of mobile users and the number of mobile applications (apps) have increased exponentially. With a wide user base exceeding 2 billion users, Android is the most popular operating system worldwide, which makes it a frequent target for malicious actors. Adware is a form of malware that downloads and displays unwanted advertisements, which are often offensive and always unsolicited. This paper presents a machine learning-based system (AdStop) that detects Android adware by examining the features in the flow of network traffic. The design goals of AdStop are high accuracy, high speed, and good generalizability beyond the training dataset. A feature reduction stage was implemented to increase the accuracy of Adware detection and reduce the time overhead. The number of relevant features used in training was reduced from 79 to 13 to improve the efficiency and simplify the deployment of AdStop. In experiments, the tool had an accuracy of 98.02% with a false positive rate of 2% and a false negative rate of 1.9%. The time overhead was 5.54 s for training and 9.36 µs for a single instance in the testing phase. In tests, AdStop outperformed other methods described in the literature. It is an accurate and lightweight tool for detecting mobile adware.

There is a paradigm shift from traditional power distribution systems to smart grids (SGs) due to advances in information and communication technology. An advanced metering infrastructure (AMI) is one of the main components in an SG. Its relevance comes from its ability to collect, process, and transfer data through the internet. Although the advances in AMI and SG techniques have brought new operational benefits, they introduce new security and privacy challenges. Security has emerged as an imperative requirement to protect an AMI from attack. Currently, ensuring security is a major challenge in the design and deployment of an AMI. This study provides a systematic survey of the security of AMI systems from diverse perspectives. It focuses on attacks, mitigation approaches, and future visions. The contributions of this article are fourfold: First, the vulnerabilities that may exist in all components of an AMI are described and analyzed. Second, it considers attacks that exploit these vulnerabilities and the impact they can have on the performance of individual components and the overall AMI system. Third, it discusses various countermeasures that can protect an AMI system. Fourth, it presents the open challenges relating to AMI security as well as future research directions. The uniqueness of this review is its comprehensive coverage of AMI components with respect to their security vulnerabilities, attacks, and countermeasures. The future vision is described at the end.

Remote Patient Monitoring (RPM) is a form of telehealth or virtual health that strengthens online medical services and allows delivering healthcare remotely. Nowadays, remote patient monitoring systems (RPMS) are widely used by healthcare providers to remotely monitor the vital signs of patients. As the RPM field expands, concerns about efficient and secure medical data transmission are raised. This kind of patient medical data is collected by the mean of Internet of Healthcare Things (IoHT) or sometimes denoted as Internet of Medical Things (IoMT) devices. The collected data needs to be stored and retrieved with highly assured levels of security and privacy as the data comprises private and critical patients’ information. To secure medical data, this paper proposes a blockchain-based architecture to manage access control to medical data and to preserve patient’s data privacy. The blockchain-based system is built on Hyperledger Fabric, a permissioned distributed ledger solution, and the ledgers and transactions are stored in the cloud. The proposed architecture is designed to contribute to the robustness of the RPM systems and to avoid recorded security limitations in commonly used permissioned blockchains methods. Performance evaluation has proved the robustness and superiority of the proposed system in terms of data confidentiality, integrity, availability, traceability, scalability, and data privacy while integrated with the RPM services.

The Internet of Things (IoT) paradigm serves as an enabler technology in several domains. Healthcare is one of the domains in which the IoT plays a vital role in increasing quality of life. On the one hand, the Internet of Healthcare Things (IoHT) creates smart environments and increases the efficiency and intelligence of the provided services. On the other hand, unfortunately, it suffers from security vulnerabilities inside and outside. There are various techniques used to identify, access, and securely manage IoT devices. Additionally, sensors, monitoring, key confidentiality management, integrity, and sensitive data accessibility are required. This study focuses on the IoT perception layer and offers a comprehensive review of the IoHT or the Internet of Medical Things (IoMT). The paper covers the current trends and open challenges in IoHT device authentication mechanisms, such as the physically unclonable function (PUF) and blockchain-based techniques. In addition, IoT simulators and verification tools are included. Finally, a future vision regarding the evolution of IoHT device authentication in terms of the utilization of different technologies, such as artificial intelligence, cloud computing, and 5G, is provided at end of this review.

The rapid development of the Industrial Internet of Things (IIoT) and artificial intelligence (AI) brings new security threats by exposing secret and private data. Thus, information security has become a major concern in the communication environment of IIoT and AI, where security and privacy must be ensured for the messages between a sender and the intended recipient. In this article, we propose a method called Harris hawks optimization-integer wavelet transform (HHO-IWT) for covert communication and secure data in the IIoT environment based on digital image steganography. The method embeds secret data in the cover images using a metaheuristic optimization algorithm called HHO to efficiently select image pixels that can be used to hide bits of secret data within integer wavelet transforms. The HHO-based pixel selection operation uses an objective function evaluation depending on the following two phases: exploitation and exploration. The objective function is employed to determine an optimal encoding vector to transform secret data into an encoded form generated by the HHO algorithm. Several experiments are conducted to validate the performance of the proposed method with respect to visual quality, payload capacity, and security against attacks. The obtained results reveal that the HHO-IWT method achieves higher levels of security than the state-of-the-art methods and that it resists various forms of steganalysis. Thus, utilizing this approach can keep unauthorized individuals away from the transmitted information and solve some security challenges in the IIoT.

The purpose of this special issue is to assemble a selection of best research articles that were presented within the 6th International Conference on Innovative Network Systems and Applications (iNetSApp'18), 1 which was organized under the Federated Conference on Computer Science and Information Systems 2018 (FedCSIS'18), 2 which was held in the Polish city Pozna ń.According to the FedCSIS policy, 23% acceptance rate was kept in all regular paper submissions with the help of the well-structured and experienced conference program committee.Since only the little amount of papers met the quality for the publication in this special issue, other selected best papers from the 18th International Conference on Innovations for Community Services (I4CS 2018), 3 which was held in Žilina, Slovakia, were selected for the publication.For this special issue, only the papers with best review score were selected, thus the quality of the CPE series can be conserved.This special issue focuses on different applications and algorithm developments in the area of modern network systems which encompass a wide range of solutions and technologies, including wireless and wired networks, network systems, services, and applications.The scope of this special issue is broad, aiming at different results in numerous active research areas oriented toward various technical, scientific, and social aspects of network systems and applications.This is supplied by the recent advances in the theory and practice in a wide range of aspects of Internet community services, especially of how community services can be used in many areas and how they have been deployed.The authors in Reference 4 focused on the improvements of the convergence time in IP networks.They introduced a new kind of IP fast re-route (IPFRR) mechanism called the multicast repair (MREP) IPFRR mechanism, which provides an advanced fast reroute technique for Internet service providers' core networks.The M-REP IPFRR mechanism is based on IP multicast and utilizes Protocol Independent Multicast-Dense Mode with the modification of an internal reverse path forwarding check.The M-REP IPFRR mechanism does not depend on any particular routing protocol ORCID

Network traces are a valuable source of information for modeling and analysis of network behavior and for the evaluation of network protocols. These crucial activities should be supported with reliable traffic traces. Reliable packet capturing facilities should be devoted to avoid losing packets and at the very last, report when packets have been lost with the highest accuracy possible. This paper describes a methodological approach to maximize the accuracy in the packet loss monitoring. The approach lies on the monitoring of appropriate statistical indicators directly from Ethernet hardware. These indicators become the enablers for the collection of metadata traces in parallel with the standard trace collections. The resulting meta-traces are proposed as a mechanism to monitor packet loss accuracy at the packet level as they provide the means to identify the exact location and amount of the packet loss in the collected traces files. We describe an evaluation use case of our approach with commodity hard- and software. The conclusion drawn from our experimental set up configuration is that it is possible to maximize the accuracy of the packet loss monitoring for reliable trace collections at affordable costs.

Collection of packet traces for future analysis is a very meticulous work that must guarantee accurate traces in order for these traces to be valuable for analysis. Current platforms do not provide a means to measure this accuracy. This paper describes a real-time monitoring method to measure the quality of a collected trace. The method takes a system architecture approach monitoring different points of the system to account for all potential drops of the packet journey. A set of metadata is stored in metatraces to be analyzed together with the trace after the capturing. The primary information is taken from standard Ethernet counters which are available in all commodity hardware and therefore performs very well without expensive specific hardware. The paper presents the evaluation of the real-time monitoring method concluding that the processing overhead does not produce significant performance degradation and that it improves packet loss detection up to orders of magnitude depending on different scenarios.