International Journal of Network Management最新文献

{"title":"An IoT Intrusion Detection Approach Based on Salp Swarm and Artificial Neural Network","authors":"Omar A. Alzubi,&nbsp;Jafar A. Alzubi,&nbsp;Issa Qiqieh,&nbsp;Ala' M. Al-Zoubi","doi":"10.1002/nem.2296","DOIUrl":"10.1002/nem.2296","url":null,"abstract":"<div>\u0000 \u0000 <p>The Internet of Things has emerged as a significant and influential technology in modern times. IoT presents solutions to reduce the need for human intervention and emphasizes task automation. According to a Cisco report, there were over 14.7 billion IoT devices in 2023. However, as the number of devices and users utilizing this technology grows, so does the potential for security breaches and intrusions. For instance, insecure IoT devices, such as smart home appliances or industrial sensors, can be vulnerable to hacking attempts. Hackers might exploit these vulnerabilities to gain unauthorized access to sensitive data or even control the devices remotely. To address and prevent this issue, this work proposes integrating intrusion detection systems (IDSs) with an artificial neural network (ANN) and a salp swarm algorithm (SSA) to enhance intrusion detection in an IoT environment. The SSA functions as an optimization algorithm that selects optimal networks for the multilayer perceptron (MLP). The proposed approach has been evaluated using three novel benchmarks: Edge-IIoTset, WUSTL-IIOT-2021, and IoTID20. Additionally, various experiments have been conducted to assess the effectiveness of the proposed approach. Additionally, a comparison is made between the proposed approach and several approaches from the literature, particularly SVM combined with various metaheuristic algorithms. Then, identify the most crucial features for each dataset to improve detection performance. The SSA-MLP outperforms the other algorithms with 88.241%, 93.610%, and 97.698% for Edge-IIoTset, IoTID20, and WUSTL, respectively.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"35 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142177490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intrusion Detection for Blockchain-Based Internet of Things Using Gaussian Mixture–Fully Convolutional Variational Autoencoder Model","authors":"C. U. Om Kumar,&nbsp;Suguna Marappan,&nbsp;Bhavadharini Murugeshan,&nbsp;P. Mercy Rajaselvi Beaulah","doi":"10.1002/nem.2295","DOIUrl":"10.1002/nem.2295","url":null,"abstract":"<div>\u0000 \u0000 <p>The Internet of Things (IoT) is an evolving paradigm that has dramatically transformed the traditional style of living into a smart lifestyle. IoT devices have recently attained great attention due to their wide range of applications in various sectors, such as healthcare, smart home devices, smart industries, smart cities, and so forth. However, security is still a challenging issue in the IoT environment. Because of the disparate nature of IoT devices, it is hard to detect the different kinds of attacks available in IoT. Various existing works aim to provide a reliable intrusion detection system (IDS) technique. But they failed to work because of several security issues. Thus, the proposed study presents a blockchain-based deep learning model for IDS. Initially, the input data are preprocessed using min-max normalization, converting the raw input data into improved quality. In order to detect the presented attacks in the provided dataset, the proposed work introduced Gaussian mixture–fully convolutional variational autoencoder (GM-FCVAE) model. The implementation is performed in Python, and the performance of the proposed GM-FCVAE model is analyzed by evaluating several metrics. The proposed GM-FCVAE model is tested on three datasets and attained superior accuracy of 99.18%, 98.81%, and 98.4% with UNSW-NB15, CICIDS 2019, and N_BaIoT datasets, respectively. The comparison reveals that the proposed GM-FCVAE model obtained higher results than the other deep learning techniques. The outperformance shows the efficacy of the proposed study in identifying security attacks.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"34 6","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142177414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Intelligent Reinforcement Learning–Based Method for Threat Detection in Mobile Edge Networks","authors":"Muhammad Yousaf Saeed, Jingsha He, Nafei Zhu, Muhammad Farhan, Soumyabrata Dev, Thippa Reddy Gadekallu, Ahmad Almadhor","doi":"10.1002/nem.2294","DOIUrl":"https://doi.org/10.1002/nem.2294","url":null,"abstract":"Traditional techniques for detecting threats in mobile edge networks are limited in their ability to adapt to evolving threats. We propose an intelligent reinforcement learning (RL)–based method for real‐time threat detection in mobile edge networks. Our approach enables an agent to continuously learn and adapt its threat detection capabilities based on feedback from the environment. Through experiments, we demonstrate that our technique outperforms traditional methods in detecting threats in dynamic edge network environments. The intelligent and adaptive nature of our RL‐based approach makes it well suited for securing mission‐critical edge applications with stringent latency and reliability requirements. We provide an analysis of threat models in multiaccess edge computing and highlight the role of on‐device learning in enabling distributed threat intelligence across heterogeneous edge nodes. Our technique has the potential, significantly enhancing threat visibility and resiliency in next‐generation mobile edge networks. Future work includes optimizing sample efficiency of our approach and integrating explainable threat detection models for trustworthy human–AI collaboration.","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"1 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142177416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blockchain-Enabled Decentralized Healthcare Data Exchange: Leveraging Novel Encryption Scheme, Smart Contracts, and Ring Signatures for Enhanced Data Security and Patient Privacy","authors":"S. Vidhya,&nbsp;P. M. Siva Raja,&nbsp;R. P. Sumithra","doi":"10.1002/nem.2289","DOIUrl":"10.1002/nem.2289","url":null,"abstract":"<div>\u0000 \u0000 <p>The healthcare industry has undergone a digital transformation in recent years, with the adoption of electronic health records (EHRs) becoming increasingly prevalent. While this digitization offers various advantages, concerns regarding the security and privacy of sensitive medical data have also intensified. Data breaches and cyber-attacks targeting healthcare organizations have underscored the need for robust solutions to protect patient data. Blockchain technology has emerged as a promising solution due to its decentralized and immutable nature, which ensures secure and transparent data recording. This paper proposes a novel approach that combines blockchain with advanced encryption scheme and privacy protection technique to establish a secure and privacy protected medical data sharing environment. The proposed system consists of three phases such as initialization phase, data processing phase, and authentication phase. The hybrid Feistal-Shannon homomorphic encryption algorithm (HFSHE) is proposed to encrypt the medical data to ensure data confidentiality, integrity, and availability. Ring signature is integrated to the system to provide additional anonymity and protect the identities of the participants involved in data transactions. In addition, the smart contract developed performs authentication checks on users, generates a time seal, and verifies the ring signature. Through this enhancement, the system becomes more resilient to both external and internal threats, enhancing overall security as well as privacy. A comprehensive security analysis is conducted to compare the proposed method's performance against existing techniques. The results demonstrate the effectiveness of the proposed approach in safeguarding sensitive medical information within the blockchain ecosystem.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"34 5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141927265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DELTA: A Modular, Transparent, and Efficient Synchronization of DLTs and Databases","authors":"F. Javier Fernández-Bravo Peñuela,&nbsp;Jordi Arjona Aroca,&nbsp;Francesc D. Muñoz-Escoí,&nbsp;Yuriy Yatsyk Gavrylyak,&nbsp;Ismael Illán García,&nbsp;José M. Bernabéu-Aubán","doi":"10.1002/nem.2293","DOIUrl":"10.1002/nem.2293","url":null,"abstract":"<div>\u0000 \u0000 <p>Besides cryptocurrencies, DLTs may be also exploited in enterprise systems operated by a consortium of organizations. Their interaction takes usually place on a permissioned blockchain network that holds a set of data to be queried frequently. In this scope, the main problem of DLTs is their unsuitability for a fast service of complex queries on those data. In order to solve this issue, many proposals dump the ledger contents onto databases that, because of their own goals and design, are already optimized for the execution of those queries. Unfortunately, many of those proposals assume that the data to be queried consist in only a block or (cryptocurrency-related) transaction history. However, those organization consortiums commonly store other structured business-related information in the DLT, and there is an evident lack of support for querying that other kind of structured data. To remedy those problems, DELTA synchronizes, with minimal overhead, the DLT state into a database, providing (1) a modular architecture with event-based handling of DLT updates that supports different DLTs and databases, (2) a transparent management, since DLT end users do not need to learn or use any new API in order to handle that synchronization (i.e., those users still rely on the original interface provided by their chosen DLT), (3) the efficient execution of complex queries on those structured data. Thus, DELTA reduces query times up to five orders of magnitude, depending on the DLT and the database, compared to queries directed to the ledger nodes.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"34 5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141944861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Privacy Preservation of Large Language Models in the Metaverse Era: Research Frontiers, Categorical Comparisons, and Future Directions","authors":"Dabin Huang,&nbsp;Mengyu Ge,&nbsp;Kunlan Xiang,&nbsp;Xiaolei Zhang,&nbsp;Haomiao Yang","doi":"10.1002/nem.2292","DOIUrl":"10.1002/nem.2292","url":null,"abstract":"<div>\u0000 \u0000 <p>Large language models (LLMs), with their billions to trillions of parameters, excel in natural language processing, machine translation, dialog systems, and text summarization. These capabilities are increasingly pivotal in the metaverse, where they can enhance virtual interactions and environments. However, their extensive use, particularly in the metaverse's immersive platforms, raises significant privacy concerns. This paper analyzes existing privacy issues in LLMs, vital for both traditional and metaverse applications, and examines protection techniques across the entire life cycle of these models, from training to user deployment. We delve into cryptography, embedding layer encoding, differential privacy and its variants, and adversarial networks, highlighting their relevance in the metaverse context. Specifically, we explore technologies like homomorphic encryption and secure multiparty computation, which are essential for metaverse security. Our discussion on Gaussian differential privacy, Renyi differential privacy, Edgeworth accounting, and the generation of adversarial samples and loss functions emphasizes their importance in the metaverse's dynamic and interactive environments. Lastly, the paper discusses the current research status and future challenges in the security of LLMs within and beyond the metaverse, emphasizing urgent problems and potential areas for exploration.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"35 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141871662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"No Worker Left (Too Far) Behind: Dynamic Hybrid Synchronization for In-Network ML Aggregation","authors":"Diego Cardoso Nunes,&nbsp;Bruno Loureiro Coelho,&nbsp;Ricardo Parizotto,&nbsp;Alberto Egon Schaeffer-Filho","doi":"10.1002/nem.2290","DOIUrl":"10.1002/nem.2290","url":null,"abstract":"<div>\u0000 \u0000 <p>Achieving high-performance aggregation is essential to scaling data-parallel distributed machine learning (ML) training. Recent research in in-network computing has shown that offloading the aggregation to the network data plane can accelerate the aggregation process compared to traditional server-only approaches, reducing the propagation delay and consequently speeding up distributed training. However, the existing literature on in-network aggregation does not provide ways to deal with slower workers (called stragglers). The presence of stragglers can negatively impact distributed training, increasing the time it takes to complete. In this paper, we present Serene, an in-network aggregation system capable of circumventing the effects of stragglers. Serene coordinates the ML workers to cooperate with a programmable switch using a hybrid synchronization approach where approaches can be changed dynamically. The synchronization can change dynamically through a control plane API that translates high-level code into switch rules. Serene switch employs an efficient data structure for managing synchronization and a hot-swapping mechanism to consistently change from one synchronization strategy to another. We implemented and evaluated a prototype using BMv2 and a Proof-of-Concept in a Tofino ASIC. We ran experiments with realistic ML workloads, including a neural network trained for image classification. Our results show that Serene can speed up training by up to 40% in emulation scenarios by reducing drastically the cumulative waiting time compared to a synchronous baseline.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"35 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141774195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Generalized Lightweight Intrusion Detection Model With Unified Feature Selection for Internet of Things Networks","authors":"Renya Nath N,&nbsp;Hiran V. Nath","doi":"10.1002/nem.2291","DOIUrl":"10.1002/nem.2291","url":null,"abstract":"<div>\u0000 \u0000 <p>The applicability of the Internet of Things (IoT) cutting across different domains has resulted in newer “things” acquiring IP connectivity. These things, technically known as IoT devices, are vulnerable to diverse security threats. Consequently, there has been an exponential increase in IoT malware over the past 5 years, and securing IoT devices from such attacks is a pressing concern in the current era. However, the traditional peripheral security measures do not comply with the lightweight security requirements of the IoT ecosystem. Considering this, we propose a lightweight intrusion detection model for IoT networks (LIDM-IoT) that demonstrates similar efficiency in exposing malicious activities compared with the existing computationally expensive methods. The crux of the proposed model is that it provides efficient attack detection with lower computational requirements in IoT networks. LIDM-IoT achieves the feat through a novel unified feature selection strategy that unifies filter-based and embedded feature selection methods. The proposed feature selection strategy reduces the feature space by 94%. Also, we use only the records of a single attack type to build the model using the XGBoost algorithm. We have tested LIDM-IoT with unseen attack types to ensure its generalized behavior. The results indicate that the proposed model exhibits efficient attack detection, with a reduced feature set, in IoT networks compared with the state-of-the-art models.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"34 6","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141774197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Domain-Adaptive Power Profiling Analysis Strategy for the Metaverse","authors":"Xiang Li,&nbsp;Ning Yang,&nbsp;Weifeng Liu,&nbsp;Aidong Chen,&nbsp;Yanlong Zhang,&nbsp;Shuo Wang,&nbsp;Jing Zhou","doi":"10.1002/nem.2288","DOIUrl":"10.1002/nem.2288","url":null,"abstract":"<div>\u0000 \u0000 <p>In the surge of the digital era, the metaverse, as a groundbreaking concept, has become a focal point in the technology sector. It is reshaping human work and life patterns, carving out a new realm of virtual and real interaction. However, the rapid development of the metaverse brings along novel challenges in security and privacy. In this multifaceted and complex technological environment, data protection is of paramount importance. The innovative capabilities of high-end devices and functions in the metaverse, owing to advanced integrated circuit technology, face unique threats from side-channel analysis (SCA), potentially leading to breaches in user privacy. Addressing the issue of domain differences caused by different hardware devices, which impact the generalizability of the analysis model and the accuracy of analysis, this paper proposes a strategy of portability power profiling analysis (PPPA). Combining domain adaptation and deep learning techniques, it models and calibrates the domain differences between the profiling and target devices, enhancing the model's adaptability in different device environments. Experiments show that our method can recover the correct key with as few as 389 power traces, effectively recovering keys across different devices. This paper underscores the effectiveness of cross-device SCA, focusing on the adaptability and robustness of analysis models in different hardware environments, thereby enhancing the security of user data privacy in the metaverse environment.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"35 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141612824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Innovations in Blockchain for Crypto Assets and Exchanges","authors":"James Won-Ki Hong,&nbsp;Andreas Veneris,&nbsp;Hongtaek Ju,&nbsp;Taeyeol Jeong,&nbsp;Changhoon Kang","doi":"10.1002/nem.2287","DOIUrl":"https://doi.org/10.1002/nem.2287","url":null,"abstract":"<p>This special issue contains extended versions of the best papers from the IEEE CryptoEx 2023 workshop (https://icbc2023.ieee-icbc.org/workshop/cryptoex-2023), which was held as a co-located workshop with 2023 IEEE International Conference on Blockchain and Cryptocurrency. The workshop was held on Friday, May 5, 2023, in Dubai, UAE. The papers in this special issue explore crucial advancements in fractional NFTs, stablecoins, and cryptocurrency exchanges, reflecting the diverse and innovative applications of blockchain technology.</p><p>The first paper, titled “Fractional Non-Fungible Tokens (NFTs): Overview, Evaluation, Marketplaces, and Challenges,” authored by Wonseok Choi, Jongsoo Woo, and James Won-Ki Hong, explores the innovative concept of fractional NFTs. By democratizing access to high-value digital assets, fractional NFTs merge tokenization, smart contracts, and ownership models to revolutionize the digital economy. The paper evaluates gas consumption and examines regulatory and security challenges, underscoring the importance of transparency and robust security measures in fostering trust within fractional NFT ecosystems.</p><p>The second paper, titled “Leveraging Ponzi-like Designs in Stablecoins,” by Shange Fu, Qin Wang, Jiangshan Yu, and Shiping Chen, provides a novel perspective on algorithmic stablecoins, which are often dismissed as Ponzi schemes. This study clarifies the fundamental nature of Ponzi schemes and introduces a rational model for evaluating the sustainability of algorithmic stablecoins. By applying historical data, the paper identifies conditions under which these stablecoins can function effectively as rational Ponzi games, offering a new understanding of their stability mechanisms.</p><p>The third paper, titled “Athena: Smart Order Routing on Centralized Crypto Exchanges using a Unified Order Book,” authored by Robert Henker, Daniel Atzberger, Jan Ole Vollmer, Willy Scheibel, Jürgen Döllner, and Markus Bick, describes the development and implementation of Athena. This system optimizes trading strategies by integrating order books from multiple centralized crypto exchanges into a unified order book. Athena's smart order routing algorithm significantly reduces implicit trading costs, making it particularly beneficial for institutional investors in illiquid crypto markets.</p><p>The fourth paper, titled “Deeper: A Shared Liquidity DEX Design for Low Trading Volume Tokens to Enhance Average Liquidity,” by Srisht Fateh Singh, Panagiotis Michalopoulos, and Andreas Veneris, introduces Deeper, a decentralized exchange design aimed at improving liquidity for low trading volume tokens. By enabling liquidity providers to share reserves of a common token, Deeper addresses issues like high slippage and sandwich attacks. The paper demonstrates the enhanced liquidity achieved through historical price experiments and highlights potential risks for liquidity providers.</p><p>We believe that these four papers make significant cont","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"34 4","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/nem.2287","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141624577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}