Annals of Telecommunications最新文献

{"title":"Organic 6G networks: ultra-flexibility through extensive stateless functional split","authors":"Marius Corici,&nbsp;Fabian Eichhorn,&nbsp;Hauke Buhr,&nbsp;Thomas Magedanz","doi":"10.1007/s12243-024-01024-6","DOIUrl":"10.1007/s12243-024-01024-6","url":null,"abstract":"<div><p>With the increase in hardware performance, the 5G mobile network architecture shifted from physical components to software-only micro-services. The very modular network functions can be deployed flexibly on commodity hardware. However, the extensive modularity of these network functions is increasing the number of managed entities, and the core network request latency. Also, it requires extensive procedures to be able to re-select the components for specific devices, a fundamental condition for a potential system scale-down. In this paper, we propose a new organic 6G network architecture that handles these challenges through a new functionality split based on the experience of IT software services. Furthermore, we provide an analysis based on main 5G procedures, showing that the newly proposed architecture is handling the re-selection of functionality significantly better, which is a cornerstone of high-speed scaling (especially scaling-out), as well as migration of functionality and users.</p></div>","PeriodicalId":50761,"journal":{"name":"Annals of Telecommunications","volume":"79 9-10","pages":"605 - 619"},"PeriodicalIF":1.8,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12243-024-01024-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140573363","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}

{"title":"Performance evaluation of DFT/DCT/DST-based SC-FDMA systems in the presence of CFOs for wireless images transmission","authors":"Khaled A. M. Al Soufy,&nbsp;Faisal S. Al-Kamali,&nbsp;Claude D’Amours,&nbsp;Nagi H. N. Al-Ashwal,&nbsp;Farhan M. Nashwan,&nbsp;Mohamed A. Swillam","doi":"10.1007/s12243-024-01020-w","DOIUrl":"10.1007/s12243-024-01020-w","url":null,"abstract":"<div><p>Single carrier frequency division multiple access (SC-FDMA) has become increasingly popular in broadband data transmission systems due to its many advantages. One of the main advantages is the lower peak-to-average power ratio (PAPR), which significantly benefits the mobile terminal station in terms of transmit power efficiency. However, SC-FDMA is susceptible to carrier frequency offsets (CFOs) which affect the orthogonality between subcarriers and cause inter-carrier interference (ICI) and multiple access interference (MAI). In this paper, we analyze and evaluate the performance of SC-FDMA in the presence of CFOs for wireless image transmission with different basis functions, different subcarrier mapping techniques, and different modulation schemes over vehicular A and SUI3 channel models. This study focuses on evaluating the performance of SC-FDMA using wireless image transmission. The evaluation is conducted based on two performance metrics, namely peak signal-to-noise ratio (PSNR) and mean square error (MSE). Specifically, we consider the following three cases: the no CFOs case, the case when CFOs are present but without compensation, and the case when CFOs are present and CFO compensation is used. The CFO compensation technique used in this work is the joint mean minimum squared error (JMMSE) method. The results showed that JMMSE with DFT can provide better performance in the presence of CFOs compared to DCT and DST. Additionally, the choice of interleaved subcarrier mapping technique provides better performance compared to localized subcarrier mapping. Furthermore, the impact of the modulation scheme and the channel model on system performance is also evaluated, with the results showing that QPSK is more robust to CFOs compared to 16QAM modulation and the performance is better transmitting over the SU13 model rather than the vehicular A channel model. Simulation results demonstrate the effectiveness of JMMSE combined with DFT and interleaved subcarrier mapping in mitigating the effects of CFOs and multipath channels, especially with the SUI3 channel model and QPSK modulation.</p></div>","PeriodicalId":50761,"journal":{"name":"Annals of Telecommunications","volume":"80 3-4","pages":"171 - 207"},"PeriodicalIF":1.8,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140573491","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":"Guarding 6G use cases: a deep dive into AI/ML threats in All-Senses meeting","authors":"Leyli Karaçay,&nbsp;Zakaria Laaroussi,&nbsp;Sonika ujjwal,&nbsp;Elif Ustundag Soykan","doi":"10.1007/s12243-024-01031-7","DOIUrl":"10.1007/s12243-024-01031-7","url":null,"abstract":"<div><p>With the recent advances in 5G and 6G communications and the increasing need for immersive interactions due to pandemic, new use cases such as All-Senses meeting are emerging. To realize these use cases, numerous sensors, actuators, and virtual reality devices are used. Additionally, artificial intelligence (AI) and machine learning (ML) including generative AI can be used to analyze large amount of data generated by 6G networks and devices to enable new applications and services. While AI/ML technologies are evolving, they do not have the same level of security as well-known information technology components. So, AI/ML threats and their impacts can be overlooked. On the other hand, due to inherent characteristics of AI/ML components and design of AI/ML pipeline, AI/ML services can be a target for sophisticated attacks. In order to provide a holistic security view, the effect of AI/ML components should be investigated, threats should be identified, and countermeasures should be planned. Therefore, in this study, which is an extended version of our recent study (Karaçay et al. 2023), we shed the light on the use of AI/ML services including generative large language model scenarios in All-Senses meeting use case and their security aspects by carrying out a threat modeling using the STRIDE framework and attack tree methodology. Additionally, we point out some countermeasures for identified threats.</p></div>","PeriodicalId":50761,"journal":{"name":"Annals of Telecommunications","volume":"79 9-10","pages":"663 - 677"},"PeriodicalIF":1.8,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140573503","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":"The use of statistical features for low-rate denial-of-service attack detection","authors":"Ramin Fuladi,&nbsp;Tuncer Baykas,&nbsp;Emin Anarim","doi":"10.1007/s12243-024-01027-3","DOIUrl":"10.1007/s12243-024-01027-3","url":null,"abstract":"<div><p>Low-rate denial-of-service (LDoS) attacks can significantly reduce network performance. These attacks involve sending periodic high-intensity pulse data flows, sharing similar harmful effects with traditional DoS attacks. However, LDoS attacks have different attack modes, making detection particularly challenging. The high level of concealment associated with LDoS attacks makes them extremely difficult to identify using traditional DoS detection methods. In this paper, we explore the potential of using statistical features for LDoS attack detection. Our results demonstrate the promising performance of statistical features in detecting these attacks. Furthermore, through ANOVA, mutual information, RFE, and SHAP analysis, we find that entropy and L-moment-based features play a crucial role in LDoS attack detection. These findings provide valuable insights into utilizing statistical features enhancing network security, thereby improving the overall resilience and stability of networks against various types of attacks.</p></div>","PeriodicalId":50761,"journal":{"name":"Annals of Telecommunications","volume":"79 9-10","pages":"679 - 691"},"PeriodicalIF":1.8,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140573612","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 survey of public datasets for O-RAN: fostering the development of machine learning models","authors":"Rodrigo S. Couto,&nbsp;Pedro Cruz,&nbsp;Roberto G. Pacheco,&nbsp;Vivian Maria S. Souza,&nbsp;Miguel Elias M. Campista,&nbsp;Luís Henrique M. K. Costa","doi":"10.1007/s12243-024-01029-1","DOIUrl":"10.1007/s12243-024-01029-1","url":null,"abstract":"<div><p>The O-RAN architecture allows for unprecedented flexibility in Radio Access Networks (RANs). O-RAN’s components designed to control RANs, such as RAN Intelligent Controllers (RICs), places intelligence at the center of the management and orchestration of 5 G/6 G cellular networks. RICs run applications based on machine learning models, which require massive RAN data for training. Nonetheless, building testbeds to collect these data is challenging since RANs use expensive hardware and operate under a licensed spectrum, usually not available for the academy. Even though producing RAN datasets is challenging, some research groups have already made their data available. In this paper, we survey the primary public datasets available online that are considered in O-RAN papers. We identify the main characteristics and purpose of each dataset, contributing with a complement to their documentation. Also, we empirically showcase the viability of using publicly available datasets for machine learning applications within the O-RAN domain, such as spectrum and traffic classification.</p></div>","PeriodicalId":50761,"journal":{"name":"Annals of Telecommunications","volume":"79 9-10","pages":"649 - 662"},"PeriodicalIF":1.8,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140573489","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":"NestedChain: “Blockchain-inside-a-Blockchain” new generation prototype","authors":"Daniel Maldonado-Ruiz,&nbsp;Alan Pulval-Dady,&nbsp;Yulin Shi,&nbsp;Zhe Wang,&nbsp;Nour El Madhoun,&nbsp;Jenny Torres","doi":"10.1007/s12243-024-01030-8","DOIUrl":"10.1007/s12243-024-01030-8","url":null,"abstract":"<div><p>New developments of blockchain designs, for both research and commercial environments, focus on improving security and energy consumption. Indeed, these implementations are based on managing a single type of information linked to a single blockchain. In this paper, we propose a new design called NestedChain. This proposal creates a system that allows two completely different types of information to be held in the same physical structure, enabling the creation of a blockchain within a blockchain. The new blockchain design can be implemented in various environments where it is necessary to have two different and parallel sets of information in the same network infrastructure. This new conception of blockchain offers a new way to understand the limitations of existing implementations and suggests how the evolution of the blockchain environment could be enhanced.</p></div>","PeriodicalId":50761,"journal":{"name":"Annals of Telecommunications","volume":"79 11-12","pages":"881 - 899"},"PeriodicalIF":1.8,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140573616","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":"Large language models and unsupervised feature learning: implications for log analysis","authors":"Egil Karlsen,&nbsp;Xiao Luo,&nbsp;Nur Zincir-Heywood,&nbsp;Malcolm Heywood","doi":"10.1007/s12243-024-01028-2","DOIUrl":"10.1007/s12243-024-01028-2","url":null,"abstract":"<div><p>Log file analysis is increasingly being addressed through the use of large language models (LLM). LLM provides the mechanism for discovering embeddings for distinguishing between different behaviors present in log files. In this work, we are interested in discriminating between normal and anomalous behaviors via an unsupervised learning approach. To this end, firstly five recent LLM architectures are evaluated over six different log files. Then, further research is conducted to explicitly quantify the significance of performing self-supervised fine-tuning on the LLMs. Moreover, we show that the quality of an (unsupervised) feature map used to make the overall (normal/anomalous) predictions may also benefit from an AutoEncoder stage between LLM and feature map. Such an AutoEncoder provides significant reductions in the cost of training the feature map and typically improves the quality of the resulting predictions.</p></div>","PeriodicalId":50761,"journal":{"name":"Annals of Telecommunications","volume":"79 11-12","pages":"711 - 729"},"PeriodicalIF":1.8,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140573502","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":"E-Watcher: insider threat monitoring and detection for enhanced security","authors":"Zhiyuan Wei,&nbsp;Usman Rauf,&nbsp;Fadi Mohsen","doi":"10.1007/s12243-024-01023-7","DOIUrl":"10.1007/s12243-024-01023-7","url":null,"abstract":"<div><p>Insider threats refer to harmful actions carried out by authorized users within an organization, posing the most damaging risks. The increasing number of these threats has revealed the inadequacy of traditional methods for detecting and mitigating insider threats. These existing approaches lack the ability to analyze activity-related information in detail, resulting in delayed detection of malicious intent. Additionally, current methods lack advancements in addressing noisy datasets or unknown scenarios, leading to under-fitting or over-fitting of the models. To address these, our paper presents a hybrid insider threat detection framework. We not only enhance prediction accuracy by incorporating a layer of statistical criteria on top of machine learning-based classification but also present optimal parameters to address over/under-fitting of models. We evaluate the performance of our framework using a real-life threat test dataset (CERT r4.2) and compare it to existing methods on the same dataset (Glasser and Lindauer 2013). Our initial evaluation demonstrates that our proposed framework achieves an accuracy of 98.48% in detecting insider threats, surpassing the performance of most of the existing methods. Additionally, our framework effectively handles potential bias and data imbalance issues that can arise in real-life scenarios.</p></div>","PeriodicalId":50761,"journal":{"name":"Annals of Telecommunications","volume":"79 11-12","pages":"819 - 831"},"PeriodicalIF":1.8,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12243-024-01023-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140573836","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}

{"title":"ICIN 2023 special issue — Emergence of the data and intelligence networking across the edge-cloud continuum","authors":"Marie-José Montpetit,&nbsp;Walter Cerroni","doi":"10.1007/s12243-024-01026-4","DOIUrl":"10.1007/s12243-024-01026-4","url":null,"abstract":"","PeriodicalId":50761,"journal":{"name":"Annals of Telecommunications","volume":"79 3-4","pages":"131 - 133"},"PeriodicalIF":1.8,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140751719","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":"Generating practical adversarial examples against learning-based network intrusion detection systems","authors":"Vivek Kumar,&nbsp;Kamal Kumar,&nbsp;Maheep Singh","doi":"10.1007/s12243-024-01021-9","DOIUrl":"10.1007/s12243-024-01021-9","url":null,"abstract":"<div><p>There has been a significant development in the design of intrusion detection systems (IDS) by using deep learning (DL)/machine learning (ML) methods for detecting threats in a computer network. Unfortunately, these DL/ML-based IDS are vulnerable to adversarial examples, wherein a malicious data sample can be slightly perturbed to cause a misclassification by an IDS while retaining its malicious properties. Unlike image recognition domain, the network domain has certain constraints known as <i>domain constraints</i> which are multifarious interrelationships and dependencies between features. To be considered as practical and realizable, an adversary must ensure that the adversarial examples comply with domain constraints. Recently, generative models like GANs and VAEs have been extensively used for generating adversarial examples against IDS. However, majority of these techniques generate adversarial examples which do not satisfy all domain constraints. Also, current generative methods lack explicit restrictions on the amount of perturbation which a malicious data sample undergoes during the crafting of adversarial examples, leading to the potential generation of invalid data samples. To address these limitations, a solution is presented in this work which utilize a variational autoencoder to generate adversarial examples that not only result in misclassification by an IDS, but also satisfy domain constraints. Instead of perturbing the data samples itself, the adversarial examples are crafted by perturbing the latent space representation of the data sample. It allows the generation of adversarial examples under limited perturbation. This research has explored the novel applications of generative networks for generating constraint satisfying adversarial examples. The experimental results support the claims with an attack success rate of 64.8<span>(%)</span> against ML/DL-based IDS. The trained model can be integrated further into an operational IDS to strengthen its robustness against adversarial examples; however, this is out of scope of this work.</p></div>","PeriodicalId":50761,"journal":{"name":"Annals of Telecommunications","volume":"80 3-4","pages":"209 - 226"},"PeriodicalIF":1.8,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140313957","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}