Pub Date : 2025-02-16DOI: 10.1016/j.comnet.2025.111123
Siyi Xiao , Lejun Zhang , Zhihong Tian , Shen Su , Jing Qiu , Ran Guo
Phishing scams pose significant risks to Ethereum, the second-largest blockchain-based cryptocurrency platform. Traditional methods for identifying phishing activities, such as machine learning and network representation learning, struggle to capture the temporal and repetitive transaction patterns inherent in Ethereum’s transaction network. To address these limitations, we propose a Pheromone-based Graph Embedding Algorithm (PGEA), which leverages pheromone mechanisms and a taboo list inspired by ant colony behavior to enhance subgraph sampling. This approach improves the identification of phishing activities by ensuring subgraph homogeneity and isomorphism during the sampling process. In our methodology, Ethereum transaction data is collected from known phishing addresses to construct a transaction network graph. The PGEA guides subgraph sampling, producing sequences that are transformed into node embeddings using word2vec. These embeddings are then classified using a Support Vector Machine (SVM) to distinguish between legitimate and malicious nodes. Experimental results demonstrate the superiority of our model over existing methods. PGEA achieves an accuracy of 87.18%, precision of 91.01%, recall of 84.82%, and F1 score of 86.91%, outperforming baseline approaches such as Deepwalk, Node2vec, and Graph2vec. These results highlight the efficacy of PGEA in detecting phishing addresses, contributing to a more secure Ethereum ecosystem.
{"title":"Pheromone-based graph embedding algorithm for Ethereum phishing detection","authors":"Siyi Xiao , Lejun Zhang , Zhihong Tian , Shen Su , Jing Qiu , Ran Guo","doi":"10.1016/j.comnet.2025.111123","DOIUrl":"10.1016/j.comnet.2025.111123","url":null,"abstract":"<div><div>Phishing scams pose significant risks to Ethereum, the second-largest blockchain-based cryptocurrency platform. Traditional methods for identifying phishing activities, such as machine learning and network representation learning, struggle to capture the temporal and repetitive transaction patterns inherent in Ethereum’s transaction network. To address these limitations, we propose a Pheromone-based Graph Embedding Algorithm (PGEA), which leverages pheromone mechanisms and a taboo list inspired by ant colony behavior to enhance subgraph sampling. This approach improves the identification of phishing activities by ensuring subgraph homogeneity and isomorphism during the sampling process. In our methodology, Ethereum transaction data is collected from known phishing addresses to construct a transaction network graph. The PGEA guides subgraph sampling, producing sequences that are transformed into node embeddings using word2vec. These embeddings are then classified using a Support Vector Machine (SVM) to distinguish between legitimate and malicious nodes. Experimental results demonstrate the superiority of our model over existing methods. PGEA achieves an accuracy of 87.18%, precision of 91.01%, recall of 84.82%, and F1 score of 86.91%, outperforming baseline approaches such as Deepwalk, Node2vec, and Graph2vec. These results highlight the efficacy of PGEA in detecting phishing addresses, contributing to a more secure Ethereum ecosystem.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"260 ","pages":"Article 111123"},"PeriodicalIF":4.4,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-15DOI: 10.1016/j.comnet.2025.111121
Oumaima Fadi , Adil Bahaj , Karim Zkik , Abdellatif El Ghazi , Mounir Ghogho , Mohammed Boulmalf
Smart contracts are digital agreements automating the execution of transactions in a decentralized manner. Although they offer many advantages, smart contracts are prone to multiple security vulnerabilities that might cause severe financial losses. Traditional anomaly detection methods, including Machine Learning and Deep Learning, struggle to capture the complexity of smart contract features. Recent advancements have utilized graph neural networks (GNNs) by transforming smart contracts into graphs. However, these approaches face robustness challenges due to small data sizes and model overparameterization. To address these issues, this paper proposes ACAD (Adaptive Contrastive Learning for Smart Contract Attack Detection), a novel framework employing a two-phase training process for smart contract classification. After converting smart contract codes to representative graphs, the task-agnostic features are learned using graph contrastive learning with adaptive augmentations. Next, these features are utilized for smart contract vulnerability classification in a downstream task. Unlike previous works, which rely on a single-phase GNN-based approach, ACAD leverages contrastive learning to improve robustness and generalization. This approach effectively overcomes data scarcity while capturing richer and more distinctive representations. Extensive experiments demonstrate that ACAD outperforms baseline models, achieving 95.7% accuracy and 92.44% precision in reentrancy attack detection, which represents an improvement of 5.78% in accuracy and 6.19% in precision compared to the best-performing baseline model.
{"title":"Smart contract anomaly detection: The Contrastive Learning Paradigm","authors":"Oumaima Fadi , Adil Bahaj , Karim Zkik , Abdellatif El Ghazi , Mounir Ghogho , Mohammed Boulmalf","doi":"10.1016/j.comnet.2025.111121","DOIUrl":"10.1016/j.comnet.2025.111121","url":null,"abstract":"<div><div>Smart contracts are digital agreements automating the execution of transactions in a decentralized manner. Although they offer many advantages, smart contracts are prone to multiple security vulnerabilities that might cause severe financial losses. Traditional anomaly detection methods, including Machine Learning and Deep Learning, struggle to capture the complexity of smart contract features. Recent advancements have utilized graph neural networks (GNNs) by transforming smart contracts into graphs. However, these approaches face robustness challenges due to small data sizes and model overparameterization. To address these issues, this paper proposes <strong>ACAD</strong> (<strong>A</strong>daptive <strong>C</strong>ontrastive Learning for Smart Contract <strong>A</strong>ttack <strong>D</strong>etection), a novel framework employing a two-phase training process for smart contract classification. After converting smart contract codes to representative graphs, the task-agnostic features are learned using graph contrastive learning with adaptive augmentations. Next, these features are utilized for smart contract vulnerability classification in a downstream task. Unlike previous works, which rely on a single-phase GNN-based approach, ACAD leverages contrastive learning to improve robustness and generalization. This approach effectively overcomes data scarcity while capturing richer and more distinctive representations. Extensive experiments demonstrate that ACAD outperforms baseline models, achieving 95.7% accuracy and 92.44% precision in reentrancy attack detection, which represents an improvement of 5.78% in accuracy and 6.19% in precision compared to the best-performing baseline model.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"260 ","pages":"Article 111121"},"PeriodicalIF":4.4,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-14DOI: 10.1016/j.comnet.2025.111120
Zhen Chen , Jianqiang Yu , Shuang Fan , Jing Zhao , Dianlong You
In the cloud era, cloud API, as one of the best carriers for data output, capability replication, and service delivery, has become one of the core elements of service-oriented software development and operation. However, with the significant challenge posed by a rapidly increasing number of cloud APIs, quality of service (QoS)-aware cloud API recommender system plays a crucial role in guiding users to select the most suitable APIs. Nevertheless, due to the profit-driven nature of cloud APIs and the openness of network environments, QoS-aware cloud API recommender systems are particularly susceptible to data poisoning attacks. These attacks manipulate recommendation outcomes to align with the attacker’s objectives, causing severe disruption to the cloud API ecosystem. Existing data poisoning methods for QoS-aware cloud API recommender systems have evolved from traditional heuristic-based approaches to generative adversarial network based methods. Although this evolution has improved attack performance, it remains challenging to strike an effective balance between attack effectiveness and invisibility. To address this issue, this paper proposes a data poisoning attack method based latent diffusion model. Firstly, real user-cloud API interaction data is compressed into latent feature space by multiple autoencoders to mitigate the limitation of data sparsity on model training. The diffusion model is then utilized to learn the distribution of real user interaction data with cloud APIs within this latent space. Furthermore, an attack loss is designed for model training in order to generate high-quality fake user data that is difficult to detect and aggressive in nature. Experimental results on the real-world dataset WS-DREAM demonstrate that the latent diffusion model-based data poisoning attack method outperforms baseline methods in terms of attack effectiveness, invisibility, and generalizability. This paper aims to raise awareness of cloud API recommendation security from an attack to defend perspective, providing a foundation for defenders to develop effective defense strategies and advancing the development of trustworthy QoS-aware cloud API recommender systems. The source code of the LDM implementation is publicly available at: https://github.com/yjq012/LDM.
{"title":"Latent diffusion model-based data poisoning attack against QoS-aware cloud API recommender system","authors":"Zhen Chen , Jianqiang Yu , Shuang Fan , Jing Zhao , Dianlong You","doi":"10.1016/j.comnet.2025.111120","DOIUrl":"10.1016/j.comnet.2025.111120","url":null,"abstract":"<div><div>In the cloud era, cloud API, as one of the best carriers for data output, capability replication, and service delivery, has become one of the core elements of service-oriented software development and operation. However, with the significant challenge posed by a rapidly increasing number of cloud APIs, quality of service (QoS)-aware cloud API recommender system plays a crucial role in guiding users to select the most suitable APIs. Nevertheless, due to the profit-driven nature of cloud APIs and the openness of network environments, QoS-aware cloud API recommender systems are particularly susceptible to data poisoning attacks. These attacks manipulate recommendation outcomes to align with the attacker’s objectives, causing severe disruption to the cloud API ecosystem. Existing data poisoning methods for QoS-aware cloud API recommender systems have evolved from traditional heuristic-based approaches to generative adversarial network based methods. Although this evolution has improved attack performance, it remains challenging to strike an effective balance between attack effectiveness and invisibility. To address this issue, this paper proposes a data poisoning attack method based latent diffusion model. Firstly, real user-cloud API interaction data is compressed into latent feature space by multiple autoencoders to mitigate the limitation of data sparsity on model training. The diffusion model is then utilized to learn the distribution of real user interaction data with cloud APIs within this latent space. Furthermore, an attack loss is designed for model training in order to generate high-quality fake user data that is difficult to detect and aggressive in nature. Experimental results on the real-world dataset WS-DREAM demonstrate that the latent diffusion model-based data poisoning attack method outperforms baseline methods in terms of attack effectiveness, invisibility, and generalizability. This paper aims to raise awareness of cloud API recommendation security from an attack to defend perspective, providing a foundation for defenders to develop effective defense strategies and advancing the development of trustworthy QoS-aware cloud API recommender systems. The source code of the LDM implementation is publicly available at: <span><span>https://github.com/yjq012/LDM</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"260 ","pages":"Article 111120"},"PeriodicalIF":4.4,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-14DOI: 10.1016/j.comnet.2025.111122
Ferhat Arat , Sedat Akleylek
Routing Protocol for Low-Power and Lossy Networks (RPL) is a widely used routing protocol in the Internet of Things (IoT) environments due to its suitability for resource-constrained devices and lossy network conditions. However, the inherent vulnerabilities in RPL pose significant security risks to IoT deployments. To address these challenges, we propose a security-aware routing approach that modifies objective functions (OFs) in terms of risk and vulnerability aspects. Among these vulnerabilities, rank attacks are particularly critical, as they exploit RPL’s core mechanism for route optimization by manipulating rank values to disrupt network performance. Detecting such attacks is crucial, as they can lead to suboptimal routing, increased energy consumption, and network instability, severely impacting IoT operations. To the best of our knowledge, it is the first approach to making the RPL algorithm security-aware by conducting a risk- and vulnerability-focused routing. In our approach, a step-by-step vulnerability-oriented security model is applied. First, we establish the IoT topology using communication range metrics to connect devices. We identify and analyze potential security vulnerabilities in RPL using established databases such as the National Vulnerability Database (NVD) and the Common Vulnerability Scoring System (CVSS). Based on these analyses, a set of OFs is defined to guide RPL routing decisions. The OF formulation incorporates factors such as CVSS values, vulnerability severity, and network topology. Risk levels are measured at device, path, and network-levels, leveraging these OFs. The proposed security-aware routing procedure dynamically adapts routing behavior based on the defined OFs, integrating risk assessment mechanisms into the routing process. This enables the protocol to prioritize routes with lower security risks while avoiding those vulnerable to potential attacks. Additionally, rank attacks are detected by identifying malicious nodes that manipulate rank values. To evaluate the proposed method, comparisons are made with existing procedures regarding running time and asymptotic complexity. The results demonstrate better performance in parent selection and rank attack detection, highlighting the effectiveness of our approach in enhancing the security of RPL-based IoT networks.
{"title":"Security-aware RPL: Designing a novel objective function for risk-based routing with rank evaluation","authors":"Ferhat Arat , Sedat Akleylek","doi":"10.1016/j.comnet.2025.111122","DOIUrl":"10.1016/j.comnet.2025.111122","url":null,"abstract":"<div><div>Routing Protocol for Low-Power and Lossy Networks (RPL) is a widely used routing protocol in the Internet of Things (IoT) environments due to its suitability for resource-constrained devices and lossy network conditions. However, the inherent vulnerabilities in RPL pose significant security risks to IoT deployments. To address these challenges, we propose a security-aware routing approach that modifies objective functions (OFs) in terms of risk and vulnerability aspects. Among these vulnerabilities, rank attacks are particularly critical, as they exploit RPL’s core mechanism for route optimization by manipulating rank values to disrupt network performance. Detecting such attacks is crucial, as they can lead to suboptimal routing, increased energy consumption, and network instability, severely impacting IoT operations. To the best of our knowledge, it is the first approach to making the RPL algorithm security-aware by conducting a risk- and vulnerability-focused routing. In our approach, a step-by-step vulnerability-oriented security model is applied. First, we establish the IoT topology using communication range metrics to connect devices. We identify and analyze potential security vulnerabilities in RPL using established databases such as the National Vulnerability Database (NVD) and the Common Vulnerability Scoring System (CVSS). Based on these analyses, a set of OFs is defined to guide RPL routing decisions. The OF formulation incorporates factors such as CVSS values, vulnerability severity, and network topology. Risk levels are measured at device, path, and network-levels, leveraging these OFs. The proposed security-aware routing procedure dynamically adapts routing behavior based on the defined OFs, integrating risk assessment mechanisms into the routing process. This enables the protocol to prioritize routes with lower security risks while avoiding those vulnerable to potential attacks. Additionally, rank attacks are detected by identifying malicious nodes that manipulate rank values. To evaluate the proposed method, comparisons are made with existing procedures regarding running time and asymptotic complexity. The results demonstrate better performance in parent selection and rank attack detection, highlighting the effectiveness of our approach in enhancing the security of RPL-based IoT networks.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"260 ","pages":"Article 111122"},"PeriodicalIF":4.4,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-13DOI: 10.1016/j.comnet.2025.111118
João Paulo Esper , Luciano de S. Fraga , Aline C. Viana , Kleber Vieira Cardoso , Sand Luz Correa
Next-generation touristic services will rely on the advanced mobile networks’ high bandwidth and low latency and the Multi-access Edge Computing (MEC) paradigm to provide fully immersive mobile experiences. As an integral part of travel planning systems, recommendation algorithms devise personalized tour itineraries for individual users considering the popularity of a city’s Points of Interest (POIs) as well as the tourist preferences and constraints. However, in the context of next-generation touristic services, recommendation algorithms should also consider the applications (e.g., social network, mobile video streaming, mobile augmented reality) the tourist will consume in the POIs and the quality in which the MEC infrastructure will deliver such applications. In this paper, we address the joint problem of recommending personalized tour itineraries for tourists and efficiently allocating MEC resources for advanced touristic applications. We formulate an optimization problem that maximizes the itinerary of individual tourists while optimizing the resource allocation at the network edge. We then propose an exact algorithm that quickly solves the problem optimally, considering instances of realistic size. Using a real-world location-based photo-sharing database, we conduct and present an exploratory analysis to understand preferences and users’ visiting patterns. Using this understanding, we propose a methodology to identify user interest in applications. Finally, we evaluate our algorithm using this dataset. Results show that our algorithm outperforms a modified version of a state-of-the-art solution for personalized tour itinerary recommendation, demonstrating gains up to 11 for resource allocation efficiency and 40 for user experience. In addition, our algorithm performs similarly to the modified state-of-the-art solution regarding traditional itinerary recommendation metrics.
{"title":"+Tour: Recommending personalized itineraries for smart tourism","authors":"João Paulo Esper , Luciano de S. Fraga , Aline C. Viana , Kleber Vieira Cardoso , Sand Luz Correa","doi":"10.1016/j.comnet.2025.111118","DOIUrl":"10.1016/j.comnet.2025.111118","url":null,"abstract":"<div><div>Next-generation touristic services will rely on the advanced mobile networks’ high bandwidth and low latency and the Multi-access Edge Computing (MEC) paradigm to provide fully immersive mobile experiences. As an integral part of travel planning systems, recommendation algorithms devise personalized tour itineraries for individual users considering the popularity of a city’s Points of Interest (POIs) as well as the tourist preferences and constraints. However, in the context of next-generation touristic services, recommendation algorithms should also consider the applications (e.g., social network, mobile video streaming, mobile augmented reality) the tourist will consume in the POIs and the quality in which the MEC infrastructure will deliver such applications. In this paper, we address the joint problem of recommending personalized tour itineraries for tourists and efficiently allocating MEC resources for advanced touristic applications. We formulate an optimization problem that maximizes the itinerary of individual tourists while optimizing the resource allocation at the network edge. We then propose an exact algorithm that quickly solves the problem optimally, considering instances of realistic size. Using a real-world location-based photo-sharing database, we conduct and present an exploratory analysis to understand preferences and users’ visiting patterns. Using this understanding, we propose a methodology to identify user interest in applications. Finally, we evaluate our algorithm using this dataset. Results show that our algorithm outperforms a modified version of a state-of-the-art solution for personalized tour itinerary recommendation, demonstrating gains up to 11<span><math><mtext>%</mtext></math></span> for resource allocation efficiency and 40<span><math><mtext>%</mtext></math></span> for user experience. In addition, our algorithm performs similarly to the modified state-of-the-art solution regarding traditional itinerary recommendation metrics.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"260 ","pages":"Article 111118"},"PeriodicalIF":4.4,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-13DOI: 10.1016/j.comnet.2025.111100
Parvinder Singh, Rajinder Vir
Wireless sensor networks (WSNs), one of the emerging technologies in the Internet of Things (IoT) field, have a wide range of applications, including healthcare, environmental biology, facility monitoring, and many more. These networks collect, process, and transmit environmental data by connecting a wireless network of small, resource-constrained sensors to other sensors. One of the main challenges in running wireless sensor networks is controlling the movement of mobile sinks. Mobile sinks are movable and can be used as data gathering sites in a network. Nevertheless, this movement has some problems, including a lot of route and energy changes. The history of the network shows that inadequate routing control, inconsistent energy use, and poor service quality have all been made worse by sink migration. These extra features need the use of more sophisticated and clever techniques for routing data to mobile sinks. Therefore, this study proposes an upgraded intelligent routing protocol for a mobile wireless sensor network based on intelligent monitoring for mobile sinks (MSs). Compared to previous mobile sparse techniques, the simulations show a 38% increase in network throughput and a 42% improvement in end-to-end latency. Moreover, the energy consumption and control overhead dictate the network's lifespan.
{"title":"Enhanced energy-aware routing protocol with mobile sink optimization for wireless sensor networks","authors":"Parvinder Singh, Rajinder Vir","doi":"10.1016/j.comnet.2025.111100","DOIUrl":"10.1016/j.comnet.2025.111100","url":null,"abstract":"<div><div>Wireless sensor networks (WSNs), one of the emerging technologies in the Internet of Things (IoT) field, have a wide range of applications, including healthcare, environmental biology, facility monitoring, and many more. These networks collect, process, and transmit environmental data by connecting a wireless network of small, resource-constrained sensors to other sensors. One of the main challenges in running wireless sensor networks is controlling the movement of mobile sinks. Mobile sinks are movable and can be used as data gathering sites in a network. Nevertheless, this movement has some problems, including a lot of route and energy changes. The history of the network shows that inadequate routing control, inconsistent energy use, and poor service quality have all been made worse by sink migration. These extra features need the use of more sophisticated and clever techniques for routing data to mobile sinks. Therefore, this study proposes an upgraded intelligent routing protocol for a mobile wireless sensor network based on intelligent monitoring for mobile sinks (MSs). Compared to previous mobile sparse techniques, the simulations show a 38% increase in network throughput and a 42% improvement in end-to-end latency. Moreover, the energy consumption and control overhead dictate the network's lifespan.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"261 ","pages":"Article 111100"},"PeriodicalIF":4.4,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-13DOI: 10.1016/j.comnet.2025.111101
Aymen Omri , Javier Hernandez Fernandez , Roberto Di Pietro
This paper introduces a new frequency domain index modulation (FD-IM) technique for a general orthogonal frequency-division multiplexing (OFDM)-based communication system. The proposed technique has been designed to enhance both the spectral and the energy efficiencies of OFDM-based communication systems. In particular, we propose a novel coding scheme for the symbols to be transmitted that leverages the absence of transmission itself to encode a symbol. To the best of our knowledge, this is the first usage of such a coding scheme in the FD-IM OFDM domain. The expected benefits of the proposed solution are as follows: (i) It enhances the spectral efficiency, by increasing the total number of transmit bits for a given set of subcarriers; and, (ii) It improves the energy efficiency. To evaluate and compare the advantages of the proposed FD-IM technique with a baseline subcarrier-index modulated (SIM)-OFDM method, we first have derived the closed-form expressions of the energy gain and the transmit bit gain for both techniques, with respect to the equivalent standard modulation. The theoretical results show a significant enhancement in terms of improving both the spectral and the energy efficiencies of a general OFDM-based communication system. Moreover, we run an extensive experimental campaign to support our findings. Results are striking. For instance, with a binary phase-shift keying (BPSK) modulation, an energy gain of 57% and a transmit bit gain of 58% are experimentally observed. These promising results pave the way to improve the different extension versions of the SIM-OFDM technique that have been presented in the literature. Finally, we also pointed out some further applications of our proposed encoding to the general field of information processing.
{"title":"A Spectral and Energy Efficient Transmission Scheme for OFDM-based Communication Systems","authors":"Aymen Omri , Javier Hernandez Fernandez , Roberto Di Pietro","doi":"10.1016/j.comnet.2025.111101","DOIUrl":"10.1016/j.comnet.2025.111101","url":null,"abstract":"<div><div>This paper introduces a new frequency domain index modulation (FD-IM) technique for a general orthogonal frequency-division multiplexing (OFDM)-based communication system. The proposed technique has been designed to enhance both the spectral and the energy efficiencies of OFDM-based communication systems. In particular, we propose a novel coding scheme for the symbols to be transmitted that leverages the absence of transmission itself to encode a symbol. To the best of our knowledge, this is the first usage of such a coding scheme in the FD-IM OFDM domain. The expected benefits of the proposed solution are as follows: (i) It enhances the spectral efficiency, by increasing the total number of transmit bits for a given set of subcarriers; and, (ii) It improves the energy efficiency. To evaluate and compare the advantages of the proposed FD-IM technique with a baseline subcarrier-index modulated (SIM)-OFDM method, we first have derived the closed-form expressions of the energy gain and the transmit bit gain for both techniques, with respect to the equivalent standard modulation. The theoretical results show a significant enhancement in terms of improving both the spectral and the energy efficiencies of a general OFDM-based communication system. Moreover, we run an extensive experimental campaign to support our findings. Results are striking. For instance, with a binary phase-shift keying (BPSK) modulation, an energy gain of 57% and a transmit bit gain of 58% are experimentally observed. These promising results pave the way to improve the different extension versions of the SIM-OFDM technique that have been presented in the literature. Finally, we also pointed out some further applications of our proposed encoding to the general field of information processing.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"260 ","pages":"Article 111101"},"PeriodicalIF":4.4,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-13DOI: 10.1016/j.comnet.2025.111117
Wei Luo, Ziyi Lv, Chengzhe Lai, Tengfei Yang
As the Industrial Internet undergoes swift growth, secure data sharing has become a crucial issue. However, currently most schemes are mainly applied to single-domain environments, and with the increasing demand for data sharing among various parties, the need for cross-domain data interaction becomes increasingly urgent. To solve the problem, we propose an efficient and secure industrial data cross-domain sharing scheme based on traceable CP-ABE in this paper. Firstly, a traceable and efficient CP-ABE is proposed, called TE-CP-ABE, which utilizes key conversion and key sanity check to reduce the computational complexity of the client and enable the tracking of malicious users, respectively. Secondly, based on TE-CP-ABE and proxy re-encryption technology, we design a traceable and secure cross-domain data sharing scheme for Industrial Internet. This scheme introduces domain proxies for cross-domain authentication and employs proxy re-encryption technology to facilitate policy transformation, breaking down attribute differences between different domains. TE-CP-ABE has been proven to achieve IND-CPA security under the decisional q-BDHE problem, and it efficiently prevents malicious users from abusing their keys. Finally, the proposed scheme is compared with the existing schemes in terms of theoretical analysis and experimental simulation. The results show that the proposed scheme has certain advantages in terms of computing and storage overhead.
{"title":"Efficient and secure cross-domain data sharing scheme with traceability for Industrial Internet","authors":"Wei Luo, Ziyi Lv, Chengzhe Lai, Tengfei Yang","doi":"10.1016/j.comnet.2025.111117","DOIUrl":"10.1016/j.comnet.2025.111117","url":null,"abstract":"<div><div>As the Industrial Internet undergoes swift growth, secure data sharing has become a crucial issue. However, currently most schemes are mainly applied to single-domain environments, and with the increasing demand for data sharing among various parties, the need for cross-domain data interaction becomes increasingly urgent. To solve the problem, we propose an efficient and secure industrial data cross-domain sharing scheme based on traceable CP-ABE in this paper. Firstly, a traceable and efficient CP-ABE is proposed, called TE-CP-ABE, which utilizes key conversion and key sanity check to reduce the computational complexity of the client and enable the tracking of malicious users, respectively. Secondly, based on TE-CP-ABE and proxy re-encryption technology, we design a traceable and secure cross-domain data sharing scheme for Industrial Internet. This scheme introduces domain proxies for cross-domain authentication and employs proxy re-encryption technology to facilitate policy transformation, breaking down attribute differences between different domains. TE-CP-ABE has been proven to achieve IND-CPA security under the decisional q-BDHE problem, and it efficiently prevents malicious users from abusing their keys. Finally, the proposed scheme is compared with the existing schemes in terms of theoretical analysis and experimental simulation. The results show that the proposed scheme has certain advantages in terms of computing and storage overhead.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"260 ","pages":"Article 111117"},"PeriodicalIF":4.4,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-12DOI: 10.1016/j.comnet.2025.111111
Tianqi Gao , Yuanzhi Ni , Hongfeng Tao , Zhuocheng Du , Zhenshu Zhu
In the last decade, Intelligent Transportation System (ITS) has benefited from the rapid development of advanced computing and communication technology. From the perspective of the service operator, connected-vehicles are encouraged to participate in model training to improve the intelligence of vehicular edge networks. However, the data exchanges without proper management will raise the users’ privacy concern. In addition, multi-users participating in the model training require an efficient and distributed mechanism to avoid the waste of operation resources. Therefore, it is valuable to study how to select appropriate vehicles to facilitate the model training and data transmission while protecting users’ privacy. In this paper, the vehicular edge network is built with RSU as the task publishers and users as participants. We contextualize Federated Learning in vehicular edge networks with multi-channel transmission. Therefore, it is valuable to study how to select appropriate vehicles to facilitate the model training and data transmission while maintaining the operation efficiency. In this paper, we contextualize Federated Learning in vehicular edge networks with multi-channel transmission. A vehicle selection strategy based on Stackelberg game is designed to identify the vehicles participating in the model training. Furthermore, a sub-channel scheduling strategy is proposed based on Chaos Game Optimization (CGO) for efficient data transmission. Finally, the simulation verifies the service efficiency and operation effectiveness of the proposed strategies in terms of the operating costs, model accuracy and loss.
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The extensive deployment of Internet of Things (IoT) nodes has led to the emergence of various novel application scenarios, one of which is the Internet of Vehicles (IoV). Prior to efficacious data communication, IoT nodes must initially be equipped with a globally distinctive IPv6 address. Nevertheless, resource constraints and security vulnerabilities impose significant challenges to the addressing process. Existing addressing paradigms have been incapable of simultaneously meeting the requirements for security, scalability of the routing communication network, and address privacy protection of the terminal node. Consequently, they are not appropriate for utilization in IoV scenarios. To address these issues, this paper formulates a network model combining a static backbone network and a dynamic mobile network based on the characteristics of the IoV scenarios and designs a secure and dynamic fusion addressing scheme (SD-FAC). In SD-FAC, the backbone nodes utilize a lightweight security-enhanced neighbor discovery protocol based on location information to effectuate address registration, resisting message forgery, modification, and replay attacks, enhancing the security of the routing communication network, while supporting network scalability and automatic route establishment. The mobile nodes employ the congruence class approach to pre-construct independent address spaces and promptly complete association addressing upon entering the backbone network area. We analyze the addressing performance of the overall network model from eight indicators. Experimental simulation results demonstrate that the addressing latency, resource overhead, and energy consumption of the mobile network are all superior to those of the relevant addressing schemes.
{"title":"A secure and dynamic fusion addressing scheme for Internet of Vehicles scenarios","authors":"Chao Liu, Fulong Chen, Taochun Wang, Chuanxin Zhao, Dong Xie, Peng Hu","doi":"10.1016/j.comnet.2025.111112","DOIUrl":"10.1016/j.comnet.2025.111112","url":null,"abstract":"<div><div>The extensive deployment of Internet of Things (IoT) nodes has led to the emergence of various novel application scenarios, one of which is the Internet of Vehicles (IoV). Prior to efficacious data communication, IoT nodes must initially be equipped with a globally distinctive IPv6 address. Nevertheless, resource constraints and security vulnerabilities impose significant challenges to the addressing process. Existing addressing paradigms have been incapable of simultaneously meeting the requirements for security, scalability of the routing communication network, and address privacy protection of the terminal node. Consequently, they are not appropriate for utilization in IoV scenarios. To address these issues, this paper formulates a network model combining a static backbone network and a dynamic mobile network based on the characteristics of the IoV scenarios and designs a secure and dynamic fusion addressing scheme (SD-FAC). In SD-FAC, the backbone nodes utilize a lightweight security-enhanced neighbor discovery protocol based on location information to effectuate address registration, resisting message forgery, modification, and replay attacks, enhancing the security of the routing communication network, while supporting network scalability and automatic route establishment. The mobile nodes employ the congruence class approach to pre-construct independent address spaces and promptly complete association addressing upon entering the backbone network area. We analyze the addressing performance of the overall network model from eight indicators. Experimental simulation results demonstrate that the addressing latency, resource overhead, and energy consumption of the mobile network are all superior to those of the relevant addressing schemes.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"260 ","pages":"Article 111112"},"PeriodicalIF":4.4,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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