Seyed Mohammad Azimi-Abarghouyi;Nicola Bastianello;Karl H. Johansson;Viktoria Fodor
{"title":"Hierarchical Federated ADMM","authors":"Seyed Mohammad Azimi-Abarghouyi;Nicola Bastianello;Karl H. Johansson;Viktoria Fodor","doi":"10.1109/LNET.2025.3527161","DOIUrl":"https://doi.org/10.1109/LNET.2025.3527161","url":null,"abstract":"In this letter, we depart from the widely-used gradient descent-based hierarchical federated learning (FL) algorithms to develop a novel hierarchical FL framework based on the alternating direction method of multipliers (ADMM), leveraging a network architecture consisting of a single cloud server and multiple edge servers, where each edge server is dedicated to a specific client set. Within this framework, we propose two novel FL algorithms, which both use ADMM in the top layer: one that employs ADMM in the lower layer and another that uses the conventional gradient descent-based approach. The proposed framework enhances privacy, and experiments demonstrate the superiority of the proposed algorithms compared to the conventional algorithms in terms of learning convergence and accuracy. Additionally, gradient descent on the lower layer performs well even if the number of local steps is very limited, while ADMM on both layers lead to better performance otherwise.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 1","pages":"11-15"},"PeriodicalIF":0.0,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10833716","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"SGMFuzz: State Guided Mutation Protocol Fuzzing","authors":"Zhenyu Wen;Jianfeng Yu;Zening Huang;Yiming Wu;Zhen Hong;Rajiv Ranjan","doi":"10.1109/LNET.2025.3526776","DOIUrl":"https://doi.org/10.1109/LNET.2025.3526776","url":null,"abstract":"Protocol implementations are fundamental components in network communication systems, and their security is crucial to the overall system. Fuzzing is one of the most popular techniques for detecting vulnerabilities and has been widely applied to the security evaluation of protocol implementations. However, due to the lack of machine-understandable prior knowledge and effective state-guided strategies, existing protocol fuzzing tools tailored for stateful network protocol implementations often suffer from shallow state coverage and generate numerous invalid test cases, thereby impacting the effectiveness of the testing process. In this letter, we introduce SGMFuzz, a grey-box fuzzing tool that combines a state-guided mutation mechanism to detect security vulnerabilities in protocol implementations. SGMFuzz uses the feedback collected during fuzzing to construct a finite-state machine, which aids in a deeper exploration of the program. Additionally, we design a message-aware module to enhance the tool’s ability to generate valid test cases. Our evaluation demonstrates that, compared to the most advanced and widely used network protocol fuzzing tools, SGMFuzz increases the number of discovered execution paths by over 15% on average and improves state transition coverage by over 10%, providing a more comprehensive security assessment of protocol implementations.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 1","pages":"71-75"},"PeriodicalIF":0.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Bluetooth Low Energy-Based Novel Power Efficient Buffalo Calving Detection Solution","authors":"Radhika Raina;Kamal Jeet Singh;Suman Kumar;Sarthak Jain","doi":"10.1109/LNET.2025.3526658","DOIUrl":"https://doi.org/10.1109/LNET.2025.3526658","url":null,"abstract":"This letter focuses on tackling the challenge of accurately determining the timing of buffalo calving while prioritizing power efficiency. To achieve this, a novel, compact, lightweight and power efficient device is designed for buffalo comfort and can be conveniently attached to the tail. The device wirelessly transmits data to a gateway using Bluetooth Low Energy (BLE). This functionality becomes particularly crucial when the tail movement increases in the last 12 hrs before calving and is regarded as a key behavioral indicator for predicting the onset of labor. Moreover, when an accelerometer is tied to the buffalo’s tail, the Z axis, which represents the vertical axis perpendicular to ground is anticipated to show the most notable deflections during this period as discussed in the literature. Thus, to conserve power, data is only transmitted when significant tail movement is detected, typically 12 hrs before calving, i.e., when Mean Z >-3 m/<inline-formula> <tex-math>$s^{2}$ </tex-math></inline-formula>. This approach reduces the device’s power consumption, extending its battery life to more than 6.08 years (approx.) using 620 mAh / 3V battery.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 1","pages":"6-10"},"PeriodicalIF":0.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Individual Packet Features are a Risk to Model Generalization in ML-Based Intrusion Detection","authors":"Kahraman Kostas;Mike Just;Michael A. Lones","doi":"10.1109/LNET.2025.3525901","DOIUrl":"https://doi.org/10.1109/LNET.2025.3525901","url":null,"abstract":"Machine learning is increasingly employed for intrusion detection in IoT networks. This letter provides the first empirical evidence of the risks associated with modeling network traffic using individual packet features (IPF). Through a comprehensive literature review and novel experimental case studies, we identify critical limitations of IPF, such as information leakage and low data complexity. We offer the first in-depth critique of IPF-based detection systems, highlighting their risks for real-world deployment. Our results demonstrate that IPF-based models can achieve deceptively high detection rates (up to 100% in some cases), but these rates fail to generalize to new datasets, with performance dropping by more than 90% in cross-session tests. These findings underscore the importance of considering packet interactions and contextual information, rather than relying solely on IPF, for developing robust and reliable intrusion detection systems in IoT environments.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 1","pages":"66-70"},"PeriodicalIF":0.0,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Quantum-Safe Blockchain in Hyperledger Fabric","authors":"Shahroz Abbas;Ajmery Sultana;Georges Kaddoum","doi":"10.1109/LNET.2024.3522966","DOIUrl":"https://doi.org/10.1109/LNET.2024.3522966","url":null,"abstract":"With the advances of quantum computing the security of existing cryptographic frameworks is increasingly at risk. Accordingly, in the present study, we investigate the integration of post-quantum cryptographic algorithms into Hyperledger Fabric, a blockchain framework, to safeguard it against emerging quantum threats. To this end, a modified Cryptogen tool was developed to generate X.509 certificates with both classical and post-quantum cryptographic keys. Furthermore, using tools like Hyperledger Caliper and Prometheus for empirical analysis, we demonstrate that this hybrid approach effectively strengthens security without affecting system performance. These results not only improve the security of Hyperledger Fabric, but also offer a practical guide for adding post-quantum cryptography to blockchain technologies.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 1","pages":"61-65"},"PeriodicalIF":0.0,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"HeRo: Heuristic-Based Routing in Payment Channel Networks","authors":"Shruti Mishra;Virat Aggarwal;Sujata Pal;Vidushi Agarwal","doi":"10.1109/LNET.2024.3520350","DOIUrl":"https://doi.org/10.1109/LNET.2024.3520350","url":null,"abstract":"Payment channels support off-chain transactions by enhancing transaction speed and reducing fees in the main blockchain. However, the costs and complexity of the network increase as we increase the size of the network. To address these challenges, we propose Heuristic-Based Routing with Scheduling (HeRo) combining heuristic-based routing and scheduling techniques in Payment Channel Networks (PCNs). HeRo achieves a cost reduction of 32.71% and 73.08% compared with multi-charge PCN (MPCN-RP) and Dijkstra algorithms, respectively.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 1","pages":"56-60"},"PeriodicalIF":0.0,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"SAcache: A Strongly Adaptive Online Caching Scheme for Non-Stationary Environments","authors":"Zhenghao Sha;Kechao Cai;Jinbei Zhang","doi":"10.1109/LNET.2024.3516321","DOIUrl":"https://doi.org/10.1109/LNET.2024.3516321","url":null,"abstract":"Online caching at the network edge is becoming increasingly important for alleviating the transmission pressure on backbone networks. Previous studies on online caching policies mainly use the static regret as the performance metric, which relies on a fixed benchmark and lacks the capacity to ensure optimal performance in non-stationary environments. In this letter, we introduce the strongly adaptive regret into online caching and propose a Strongly Adaptive online caching scheme (SAcache). Our SAcache scheme focuses on the performance over time intervals with a length between <inline-formula> <tex-math>$tau _{min }$ </tex-math></inline-formula> and <inline-formula> <tex-math>$tau _{max }$ </tex-math></inline-formula>, where <inline-formula> <tex-math>$tau _{min }$ </tex-math></inline-formula> and <inline-formula> <tex-math>$tau _{max }$ </tex-math></inline-formula> are the lower and upper bound on how long the environment changes, respectively. SAcache consists of multiple interval caches operating in a lazy restart mode to make candidate caching decisions, and an aggregated cache that weights the these candidate decisions to determine the final caching decision. We prove that the regret upper bound is sub-linear with respect to the time interval’s length <inline-formula> <tex-math>$tau $ </tex-math></inline-formula>, i.e., <inline-formula> <tex-math>$O(sqrt {tau log (tau _{max }/tau _{min })})$ </tex-math></inline-formula>. Our experiment results demonstrate that SAcache achieves the highest cache hit ratio and the lowest regret compared to other caching policies in non-stationary environments.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 1","pages":"46-50"},"PeriodicalIF":0.0,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"FBCNet: Fusion Basis Complex-Valued Neural Network for CSI Compression in Massive MIMO Networks","authors":"C Kiruthika;E. S. Gopi","doi":"10.1109/LNET.2024.3512658","DOIUrl":"https://doi.org/10.1109/LNET.2024.3512658","url":null,"abstract":"Deep learning-based CSI compression has shown its efficacy for massive multiple-input multiple-output networks, and on the other hand, federated learning (FL) excels the conventional centralized learning by avoiding privacy leakage issues and training communication overhead. The realization of an FL-based CSI feedback network consumes more computational resources and time, and the continuous reporting of local models to the base station results in overhead. To overcome these issues, in this letter, we propose a FBCNet. The proposed FBCNet combines the advantages of the novel fusion basis (FB) technique and the fully connected complex-valued neural network (CNet) based on gradient (G) and non-gradient (NG) approaches. The experimental results show the advantages of both CNet and FB individually over the existing techniques. FBCNet, the combination of both FB and CNet, outperforms the existing federated averaging-based CNet (FedCNet) with improvement in reconstruction performance, less complexity, reduced training time, and low transmission overhead. For the distributed array-line of sight topology at the compression ratio (CR) of 20:1, it is noted that the NMSE and the cosine similarity of FedCNet-G are −8.2837 dB, 0.9262; FedCNet-NG are −3.5291 dB, 0.8452; proposed FB are −26.8621, 0.9653. Also the NMSE and the cosine similarity of the proposed FBCNet-G are −19.7521, 0.9307; FBCNet-NG are −24.0442, 0.9539 at a high CR of 64:1.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"6 4","pages":"262-266"},"PeriodicalIF":0.0,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Zero Trust Security Architecture for 6G Open Radio Access Networks (ORAN)","authors":"Hajar Moudoud;Zakaria Abou El Houda;Bouziane Brik","doi":"10.1109/LNET.2024.3514357","DOIUrl":"https://doi.org/10.1109/LNET.2024.3514357","url":null,"abstract":"The evolution of Open Radio Access Networks (O-RAN) is crucial for the deployment and operation of 6G networks, providing flexibility and interoperability through its disaggregated and open architecture. However, this openness introduces new security issues. To address these challenges, we propose a novel Zero-Trust architecture tailored for ORAN (ZTORAN). ZTORAN includes two main modules: (1) A blockchain-based decentralized trust management system for secure verification, authentication, and dynamic access control of xApps; and (2) A threat detection module that uses Federated Multi-Agent Reinforcement Learning (FMARL) to monitor network activities continuously and detects anomalies within the ORAN ecosystem. Through comprehensive simulations and evaluations, we demonstrate the effectiveness of ZTORAN in providing a resilient and secure framework for next-generation wireless networks.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"6 4","pages":"272-275"},"PeriodicalIF":0.0,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"AI-Centric D2D in 6G Networks","authors":"Jianwen Xu;Kaoru Ota;Mianxiong Dong","doi":"10.1109/LNET.2024.3512659","DOIUrl":"https://doi.org/10.1109/LNET.2024.3512659","url":null,"abstract":"As a fundamental component of 6G, Device-to-Device (D2D) communication facilitates direct connections between devices without base stations. In order to support advanced AI applications in ubiquitous scenarios, in this letter, we propose an AI-centric D2D communication infrastructure upon mobile devices, addressing current challenges in bandwidth and transmission speed. This approach aims to leverage 6G’s potential to create more efficient, reliable, and intelligent wireless communication systems, bridging the gap between AI and next-generation D2D communication. The results from real-world case study and simulation show that our design can save time and improve efficiency in D2D transmission and on-device AI processing.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"6 4","pages":"257-261"},"PeriodicalIF":0.0,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}