J. Sens. Actuator Netw., Volume 12, Issue 1 (February 2023) – 18 articles

Device-to-device (D2D) communication will play a meaningful role in future wireless networks and standards, since it ensures ultra-low latency for communication among near devices. D2D transmissions can take place together with the actual cellular communications, so handling the interference is very important. In this paper, we consider a D2D couple operating in the uplink band in an underlaid mode, and, using the stochastic geometry, we propose a cumulative distribution function (CDF) of the D2D transmit power under $\kappa$-$\mu$ shadowed fading. Then, we derive some special cases for some fading channels, such as Nakagami and Rayleigh environments, and for the interference-limited scenario. Moreover, we propose a radio frequency energy harvesting, where the D2D users can harvests ambient RF energy from cellular users. Finally, the analytical results are validated via simulation. Full article

One of the challenges in securing wireless sensor networks (WSNs) is the key distribution; that is, a single shared key must first be known to a pair of communicating nodes before they can proceed with the secure encryption and decryption of the data. In 1984, Blom proposed a scheme called the symmetric key generation system as one method to solve this problem. Blom’s scheme has proven to be $\lambda$-secure, which means that a coalition of $\lambda +1$ nodes can break the scheme. In 2021, a novel and intriguing scheme based on Blom’s scheme was proposed. In this scheme, elliptic curves over a finite field are implemented in Blom’s scheme for the case when $\lambda =1$. However, the security of this scheme was not discussed. In this paper, we point out a mistake in the algorithm of this novel scheme and propose a way to fix it. The new fixed scheme is shown to be applicable for arbitrary $\lambda$. The security of the proposed scheme is also discussed. It is proven that the proposed scheme is also $\lambda$-secure with a certain condition. In addition, we also discuss the application of this proposed scheme in distributed ledger technology (DLT). Full article

In decentralized environments, such as mobile ad hoc networks (MANETs) and wireless sensor networks (WSNs), traditional reputation management systems are not viable due to their dependence on a central authority that is both accessible and trustworthy for all participants. This is particularly challenging in light of the dynamic nature of these networks. To overcome these limitations, our proposed solution utilizes blockchain technology to maintain global reputation information while remaining fully decentralized, and to secure multiparty computation to ensure privacy. Our system is not limited to specific settings, such as buyer/seller or provider/client scenarios, where only a subset of the network are raters while the others are ratees. Instead, it allows all nodes to participate in both rating and being rated. In terms of security, the system maintains feedback privacy in the semi-honest model, even in the presence of up to $n-2$ dishonest parties, while requiring only $O\left(n\right)$ messages and having an $O\left(n\right)$ computation overhead. Furthermore, the adopted techniques enable the system to achieve unique characteristics such as accessibility, consistency, and verifiability, as supported by the security analysis provided. Full article

Federated learning (FL) provides convenience for cross-domain machine learning applications and has been widely studied. However, the original FL is still vulnerable to poisoning and inference attacks, which will hinder the landing application of FL. Therefore, it is essential to design a trustworthy federation learning (TFL) to eliminate users’ anxiety. In this paper, we aim to provide a well-researched picture of the security and privacy issues in FL that can bridge the gap to TFL. Firstly, we define the desired goals and critical requirements of TFL, observe the FL model from the perspective of the adversaries and extrapolate the roles and capabilities of potential adversaries backward. Subsequently, we summarize the current mainstream attack and defense means and analyze the characteristics of the different methods. Based on a priori knowledge, we propose directions for realizing the future of TFL that deserve attention. Full article

Currently, many governments are gearing up to promote the development of smart cities in their countries. A smart city is an urban area using different types of sensors to collect data, which will then be used to manage assets and resources efficiently. Through smart technology, the quality of living and performance of urban services are enhanced. Recent works addressed a set of platforms aimed to support the development of smart city applications. It seems that most of them involved dealing with collecting, managing, analyzing, and correlating data to extract new information useful to a city, but they do not integrate a diversified set of services and react to events on the fly. Moreover, the application development facilities provided by them seem to be limited and might even increase the complexity of this task. We propose an event-based architecture with components that meet important requirements for smart city platforms, supporting increased demand for scalability, flexibility, and heterogeneity in event processing. We implement such architecture and data representation models, handling different data formats, and supporting a semantics-based data model. Finally, we discuss the effectiveness of a S mart Event-based Middleware (SEMi) and present empirical results regarding a performance evaluation of SEMi. Full article

This work solves the problem of identification of the machine code architecture in cyberphysical devices. A basic systematization of the Executable and Linkable Format and Portable Executable formats of programs, as well as the analysis mechanisms used and the goals achieved, is made. An ontological model of the subject area is constructed, introducing the basic concepts and their relationships. The specificity of the machine code is analyzed, and an analytical record of the process of identifying the architecture of the machine code (MC) processor is obtained. A method for identifying the MC architecture has been synthesized, which includes three successive phases: unpacking the OS image (for a set of identified architectures); building signatures of architectures (their “digital portraits” from the position of MC instructions); identification of the MC architecture for the program under test (using the collected architecture signatures), implemented using four operating modes. A software tool for identifying the MC architecture has been developed in the form of a separate utility that implements the algorithms of the method. The principle of operation of the utility is presented in the form of functional and informational diagrams. Basic testing of the identification utility has been conducted. As a result, a probabilistic assessment of the utility’s work was obtained by assigning various programs to the Top-16 selected architectures. Full article

As an extension of the wired network, the use of the wireless communication network has considerably boosted users’ productivity at work and in their daily lives. The most notable aspect of the wireless communication network is that it overcomes the constraints of the wired network, reduces the amount of cost spent on wire maintenance, and distributes itself in a manner that is both more extensive and flexible. Combining wireless communication with the Internet of Things (IoT) can be used in several applications, including smart cities, smart traffic, smart farming, smart drones, etc. However, when exchanging data, wireless communication networks use an open network, allowing unauthorized users to engage in communication that is seriously destructive. Therefore, authentication through a digital signature will be the best solution to tackle such problems. Several digital signatures are contributing to the authentication process in a wireless communication network; however, they are suffering from several problems, including forward security, key escrow, certificate management, revocations, and high computational and communication costs, respectively. Keeping in view the above problems, in this paper we proposed an efficient certificateless forward-secure signature scheme for secure deployments in wireless communication networks. The security analysis of the proposed scheme is carried out using the random oracle model (ROM), which shows that it is unforgeable against type 1 and type 2 adversaries. Moreover, the computational and communication cost analyses are carried out by using major operations, major operations cost in milliseconds, and extra communication bits. The comparative analysis with the existing scheme shows that the proposed scheme reduces the computational cost from 19.23% to 97.54% and the communication overhead from 11.90% to 83.48%, which means that the proposed scheme is efficient, faster, and more secure for communication in the wireless communication network. Full article

As one of the essential modules in intelligent surveillance systems, loitering detection plays an important role in reducing theft incidents by analyzing human behavior. This paper introduces a novel strategy for detecting the loitering activities of humans in the monitoring area for an intelligent surveillance system based on a vision sensor. The proposed approach combines spatial and temporal information in the feature extraction stage to decide whether the human movement can be regarded as loitering. This movement has been previously tracked using human detectors and particle filter tracking. The proposed method has been evaluated using our dataset consisting of 20 videos. The experimental results show that the proposed method could achieve a relatively good accuracy of 85% when utilizing the random forest classifier in the decision stage. Thus, it could be integrated as one of the modules in an intelligent surveillance system. Full article

The move towards renewable energy and technological advancements in the generation, distribution and transmission of electricity have increased the popularity of microgrids. The popularity of these decentralised applications has coincided with advancements in the field of telecommunications allowing for the efficient implementation of these applications. This convenience has, however, also coincided with an increase in the attack surface of these systems, resulting in an increase in the number of cyber-attacks against them. Preventative network security mechanisms alone are not enough to protect these systems as a critical design feature is system resilience, so intrusion detection and prevention system are required. The practical consideration for the implementation of the proposed schemes in practice is, however, neglected in the literature. This paper attempts to address this by generalising these considerations and using the lessons learned from water distribution systems as a case study. It was found that the considerations are similar irrespective of the application environment even though context-specific information is a requirement for effective deployment. Full article

Malware complexity is rapidly increasing, causing catastrophic impacts on computer systems. Memory dump malware is gaining increased attention due to its ability to expose plaintext passwords or key encryption files. This paper presents an enhanced classification model based on One class SVM (OCSVM) classifier that can identify any deviation from the normal memory dump file patterns and detect it as malware. The proposed model integrates OCSVM and Principal Component Analysis (PCA) for increased model sensitivity and efficiency. An up-to-date dataset known as “MALMEMANALYSIS-2022” was utilized during the evaluation phase of this study. The accuracy achieved by the traditional one-class classification (TOCC) model was 55%, compared to 99.4% in the one-class classification with the PCA (OCC-PCA) model. Such results have confirmed the improved performance achieved by the proposed model. Full article

Online advertising is a marketing approach that uses numerous online channels to target potential customers for businesses, brands, and organizations. One of the most serious threats in today’s marketing industry is the widespread attack known as click fraud. Traffic statistics for online advertisements are artificially inflated in click fraud. Typical pay-per-click advertisements charge a fee for each click, assuming that a potential customer was drawn to the ad. Click fraud attackers create the illusion that a significant number of possible customers have clicked on an advertiser’s link by an automated script, a computer program, or a human. Nevertheless, advertisers are unlikely to profit from these clicks. Fraudulent clicks may be involved to boost the revenues of an ad hosting site or to spoil an advertiser’s budget. Several notable attempts to detect and prevent this form of fraud have been undertaken. This study examined all methods developed and published in the previous 10 years that primarily used artificial intelligence (AI), including machine learning (ML) and deep learning (DL), for the detection and prevention of click fraud. Features that served as input to train models for classifying ad clicks as benign or fraudulent, as well as those that were deemed obvious and with critical evidence of click fraud, were identified, and investigated. Corresponding insights and recommendations regarding click fraud detection using AI approaches were provided. Full article

The Internet of Things (IoT) system’s ever-expanding attack surface calls for a new intrusion detection system (IDS). These systems may include thousands of wireless devices that need to be protected from cyberattacks. Recent research efforts used machine learning to analyze and identify various attacks and abnormal behavior on IoT systems. Most of these techniques are characterized by low accuracy and they do not scale to today’s IoT-enabled smart cities applications. This article proposes a secure automatic two-levels intrusion detection system (SATIDS) which utilizes the minimum redundancy maximum relevance (MRMR) feature selection technique and an enhanced version of long short-term memory (LSTM) based on an artificial recurrent neural network (RNN) to enhance the IDS performance. SATIDS aims at detecting traffic anomalies with greater accuracy while also reducing the time it takes to perform this task. The proposed algorithm was trained and evaluated using two of the most recent datasets based on realistic data: ToN-IoT and InSDN datasets. The performance analysis of the proposed system proves that it can differentiate between attacks and normal traffic, identify the attack category, and finally define the type of sub-attack with high accuracy. Comparing the performance of the proposed system with the existing IDSs reveals that it outperforms its best rivals from the literature in detecting many types of attacks. It improves accuracy, detection rates, F1-score, and precision. Using 500 hidden and two LSTM layers achieves accuracy of 97.5%, precision of 98.4%, detection rate of 97.9%, and F1-score of 98.05% on ToN-IoT dataset, and precision of 99%, detection rate of 99.6%, and F1-score of 99.3% on InSDN dataset. Finally, SATIDS was applied to an IoT network which utilizes the energy harvesting real-time routing protocol (EHRT). EHRT optimizes the low-energy adaptive clustering hierarchy (LEACH) routing technique using a modified artificial fish swarm algorithm. The integration between the optimized LEACH and the proposed IDS enhances the network lifetime, energy consumption, and security. Full article

Remote health monitoring systems play an important role in the healthcare sector. Edge computing is a key enabler for realizing these systems, where it is required to collect big data while providing real-time guarantees. In this study, we focus on remote cuff-less blood pressure (BP) monitoring through electrocardiogram (ECG) as a case study to evaluate the benefits of edge computing and compression. First, we investigate the state-of-the-art algorithms for BP estimation and ECG compression. Second, we develop a system to measure the ECG, estimate the BP, and store the results in the cloud with three different configurations: (i) estimation in the edge, (ii) estimation in the cloud, and (iii) estimation in the cloud with compressed transmission. Third, we evaluate the three approaches in terms of application latency, transmitted data volume, and power usage. In experiments with batches of 64 ECG samples, the edge computing approach has reduced average application latency by 15%, average power usage by 19%, and total transmitted volume by 85%, confirming that edge computing improves system performance significantly. Compressed transmission proved to be an alternative when network bandwidth is limited and edge computing is impractical. Full article

The ultra-wideband (UWB)-based real-time localization system (RTLS) is a promising technology for locating and tracking assets and personnel in real-time within a defined indoor environment since it provides high-ranging accuracy. However, its performance can be affected by the underlying antenna delays of UWB nodes, which act as a source of error during range estimations. Usually, measurement of the antenna delays is performed separately as a dedicated standalone procedure. Such an additional measurement procedure makes the UWB-based RTLS more tedious with manual interventions. Moreover, the air-time occupancy during the transmission and reception of signaling messages for range estimations between UWB node pairs also limits the serviceable capability of these networks. In this regard, we present a novel simultaneous ranging scheme that requires limited air-time occupancy during range estimations between UWB node pairs and also compensates for the error from the antenna delays. This paper provides a detailed mathematical modeling, system design, and implementation procedure of the proposed scheme. The effectiveness of the proposed scheme for locating a mobile node in an indoor environment is validated through experimental analysis. The results show that, compared to the state-of-the-art two-way ranging (TWR) method, the proposed scheme eliminates the requirement of dedicated standalone antenna delay measurement procedures of the nodes, increases air efficiency through the provision of simultaneous ranging, and provides relative root-mean-square errors (RMSEs) improvement for range and position estimations of approximately $54.52\%$ and $39.96\%$, respectively. Full article