TNSM Searchable Index

Author(s)	Title	Year	Issue	Keywords
Yifan Miao, Hui Tian, Hao Wu, Wanli Ni, Yang Tian	Computation-Aware Link Repair for Large-Scale Damage in Distributed Cloud Networks	2024	Early Access	Maintenance engineering Costs Heuristic algorithms	Due to the distributed deployment and inter-network dependence, distributed cloud network (DCN) is vulnerable to large-scale damage, making emergent system recovery of vital importance. Given limited resources at an early stage of network recovery, we propose a computation-aware link repair (CALR) algorithm to meet the computation demands of data centers in heavily damaged DCNs. Taking into account both network structure and traffic dynamics, we formulate a total system cost minimization problem to guarantee network repair performance. To tackle this challenging mixed-integer programming problem, we leverage the Benders decomposition (BD) to transfer it into an iteration problem with the mutually independent master problem and subproblem, which are solved by the cutting plane and the minimum cost flow algorithms, respectively. To accelerate the convergence speed of the proposed BD-based approach, we apply a small perturbation on the subproblem for facilitating the recovery of large-scale networks. Moreover, the computational complexity is reduced significantly by generating maximal non-dominated Benders cuts. Numerical simulations demonstrate that the proposed approach outperforms benchmarks under different settings such as network scale, data significance, available resources, and topology.	10.1109/TNSM.2024.3351860
Mi Chen, Jalel Ben-Othman, Lynda Mokdad	Greedy Behavior Detection With Machine Learning for LoRaWAN Network	2024	Early Access	Behavioral sciences Resource management Timing	LoRaWAN (Long Range Wide Area Network) has garnered significant attention within the Internet of Things (IoT) due to its ability to establish a wireless network with massive devices over long distances while minimizing energy consumption. Our previous work shows its suitability for Intelligent Transportation Systems (ITS) scenarios. However, the utilization of the Aloha MAC protocol presents a challenge for LoRaWAN as it grapples with the presence of compromised nodes. These nodes may engage in greedy behaviors, disregarding network regulations to enhance their own performance or acquire additional network resources, and are often difficult to detect. This research contributes to machine learning-based greedy behavior detection methods. After proposing several end-to-end (E2E) methods with different ML algorithms, EDLoG (Encoder-based detection method of LoRaWAN Greedy behaviors) is proposed. It is a greedy behavior detection method combining a Multilayer Perceptron (MLP) encoder network and a statistical abnormal detection algorithm. The performance evaluations are conducted using simulation data under different scenarios given by MELoNS, a Modular and Extendable Simulator for the LoRaWAN Network developed in our previous work. The results show that the proposed method gives a detection recall 15%-20% higher than the baseline method by keeping a high detection precision. Moreover, the proposed methods show high timing efficiency with a running time much smaller than LoRaWAN’s time scale, making the method easily deployed to a real LoRaWAN network.	10.1109/TNSM.2024.3351313
Ariel L. C. Portela, Silvio E. S. B. Ribeiro, Rafael A. Menezes, Thelmo de Araujo, Rafael L. Gomes	T-For: An Adaptable Forecasting Model for Throughput Performance	2024	Early Access	Throughput Predictive models Forecasting	Network monitoring services are performed by several companies and Internet Service Providers (ISPs), which provide results of regular performance tests, where throughput is one of the most essential information. However, the monitoring tools still need to evolve in order to encompass more complex activities, such as forecasting. Within this context, this paper presents a Throughput performance Forecasting model (called T-For), based on Neural Networks and Time Series Analysis, which estimates future network performance in specific time periods, according to past throughput measurements. The experiments, using real data from the National Education and Research Network (RNP), show that the proposed model outperformed the existing approaches, reaching high levels of forecast accuracy.	10.1109/TNSM.2024.3349701
Jiajie Xu, Kaixin Li, Ying Chen, Jiwei Huang	Optimal Task Scheduling and Resource Allocation for Self-Powered Sensors in Internet of Things: An Energy Efficient Approach	2024	Early Access	Internet of Things (IoT) Self-powered Sensors Mobile Edge Computing (MEC)	The prosperous development of the Internet of Things (IoT) and wireless communication technologies has led to explosive growth in the number of IoT sensor devices. However, some sensor devices are inevitably deployed in remote and inaccessible areas. How to continuously and reliably power sensor devices is a critical problem that needs to be addressed. Deploying self-powered modules on sensor devices by adopting self-powered technology is an effective solution to the energy shortage of sensor devices. Besides, Mobile Edge Computing (MEC) as a promising paradigm has provoked widespread popularity. With the help of MEC, devices can offload computing tasks to edge servers for processing, which greatly alleviates the limitations on energy, storage, and computation capability of devices. In this paper, we jointly study task scheduling and resource allocation in the MEC scenario where the sensor devices are with self-powered modules. Our goal is to minimize the long-term average energy consumption of self-powered sensor devices while ensuring system performance. We adopt stochastic optimization techniques to transform the modeled stochastic problem into a deterministic problem. Then, the deterministic problem is decomposed into four sub-problems, and we propose a task scheduling and resource allocation (TSRA) algorithm to solve these problems. Finally, we carry out a series of parameter analysis and comparison experiments to verify the TSRA algorithm. The experimental results show that our TSRA algorithm can make a dynamic tradeoff between energy consumption and system performance. It also demonstrates the effectiveness of our TSRA algorithm compared with other baseline algorithms.	10.1109/TNSM.2024.3420254
Guosheng Kang, Yang Wang, Hongshuai Ren, Buqing Cao, Jianxun Liu, Yiping Wen	KS-GNN: Keyword Search via Graph Neural Network for Web API Recommendation	2024	Early Access	Mashups Feature extraction Collaboration	With the rapid development of service computing, a large number of methods for Web service recommendation have been proposed. However, the existing approaches using Mashup description information ignore the fact that the users without knowledge of Web APIs are not able to describe their needs in detail, let alone find Web services that meet those needs and are compatible with each other. Meanwhile, most approaches that utilize Web API collaboration network based on Mashup-API invocation relationships do not effectively capture the local and global structure between APIs and mine hidden API compatibility information in the network. This paper introduces the KS-GNN model, a novel approach that utilizes graph neural network and auto-encoder techniques for Web API recommendation. Firstly, we utilize KeyBert to extract keywords related to Web services from functional descriptions. Then, we embed the extracted keywords and use their embedded representations as node representation vectors on the Web API collaboration network. Finally, considering local and global structural relationships in the Web API collaborative network and the network structural relationships for message passing, KS-GNN performs keyword searching on the Web API collaborative network, to recommend the top-K Web services that match the user’s query. Experimental results on the ProgrammableWeb dataset show that KS-GNN outperforms other deep learning-based factorization machine recommendation models. In the meantime, we also confirm that the method of extracting keywords using KeyBert outperforms other keyword extraction methods.	10.1109/TNSM.2024.3420072
Md Facklasur Rahaman, Mohtasin Golam, Md Raihan Subhan, Esmot Ara Tuli, Dong-Seong Kim, Jae-Min Lee	Meta-Governance: Blockchain-Driven Metaverse Platform for Mitigating Misbehavior Using Smart Contract and AI	2024	Early Access	Metaverse Hate speech Blockchains	The immersive metaverse environment offers distinct social interactions and opportunities, yet it also presents significant challenges in securely managing misbehavior, including hate speech, bullying, and harassment. Existing solutions primarily focus on detecting such behavior through artificial intelligence but lack robust mechanisms for management and governance. This gap is critical as the metaverse continues to mirror complex real-world interactions and centralized authority systems prove vulnerable to compromise. Our research introduces a novel framework, Meta-Governance, which not only detects but also effectively manages and governs user behavior through smart contracts, ensuring a secure, fair, and transparent metaverse environment. The system incorporates behavior monitoring to identify and condemn inappropriate behavior, specifically targeting problems such as hate speech and cyberbullying. Occurrences of misbehavior are permanently preserved on the blockchain to ensure the capacity to trace and bear accountability. In this article, we deploy a Natural Language Processing (NLP) model and a smart contract-based framework to address unusual behavior monitoring, access control, and credit scoring. Deep learning models are used to identify and classify linguistic patterns that may be considered hazardous. Blockchain technology addresses virtual misconduct using smart contracts, while a distinctive credit scoring mechanism ensures that users are held responsible for making disrespectful statements. The efficacy of the proposed smart contract is comprehensively evaluated within the context of a private Hyperledger Besu system. The integration of AI and blockchain may greatly improve the security and inclusiveness of the metaverse, highlighting the crucial role of these technologies in combating hate speech and enhancing user engagement.	10.1109/TNSM.2024.3419151
Kundan Kanti Saha, Sangram Ray, Mou Dasgupta	ECMHP: ECC-Based Secure Handshake Protocol for Multicasting in CCN-IoT Environment	2024	Early Access	Internet of Things Protocols Security	Scalability, heterogeneity, energy efficiency, cost-effectiveness, robustness, interoperability, and low latency data transfer are some of the critical challenges posed by the Internet of Things in the modern era of the Internet. Content Centric Networks (CCN) and Named Data Networks (NDN) are some proposed solutions that can meet the abovementioned challenges. In-network caching, multicasting, content security, and decoupling of data from location are the significant advantages offered by the CCN. Despite the diverse benefits of CCN, it has to face some significant problems, such as how the key management and distribution are performed among the consumers and key managers efficiently, how the node-to-node communication occurs, and how to provide services to the neighboring nodes while saving resources, etc. This paper proposes an Elliptic Curve Cryptography-based secure communication protocol supporting broadcast/multicast IoT data. Push-based data generation and transfer, typical requirements of IoT devices, have been integrated with the proposed protocol. The proposed communication protocol has been compared with existing protocols regarding computation and communication overheads as well as functional and security features. The proposed protocol has been evaluated in AVISPA and NS3 simulators for security and efficiency, and it is found that the proposed protocol outperforms the existing protocols in all aspects.	10.1109/TNSM.2024.3419431
Amir Mohamad, Hossam S. Hassanein	Preemptive Prediction-Based Placement of Time-Critical SFCs With VNF Sharing at the Edge	2024	Early Access	Time factors Cloud computing Edge computing	The demand for ultra-low latency requirements is fueled by the growing popularity of real-time and time-critical applications such as virtual, augmented, and mixed reality, and industrial IoT. Time-critical applications and services are real-time software whose failure could result in catastrophic consequences such as fatalities, damage to property, and even financial losses. Edge computing is the main enabler of 5G ultra-low latency use cases. Edge resources are limited compared to abundant cloud computing resources. As such, provisioning time-critical applications at the edge is more challenging and demanding. Even though virtual network function (VNF) sharing improves the utilization of the service providers’ resources, service requests, including time-critical ones, can still be rejected due to insufficient resources. This paper proposes a Preemptive Prediction-based Placement scheme (PPPS) for time-critical services with VNF sharing. In addition to prioritizing timecritical premium (Pr) services over best-effort (BE) services, PPPS utilizes the predicted required resources in a defined lookahead window. In cases when no resources are available for Pr services, a preemption mechanism preempts resources for the Pr service, by deporting one or more running BE services. The experimental results show that PPPS can reduce the Pr services rejection rate to ~ 0% while minimizing the disturbance that BE services witness such as prolonged waiting times.	10.1109/TNSM.2024.3419051
Yange Chen, Lei Liu, Yuan Ping, Mohammed Atiquzzaman, Shahid Mumtaz, Zhili Zhang, Mohsen Guizani, Zhihong Tian	A Privacy-Preserving Federated Learning Framework With Lightweight and Fair in IoT	2024	Early Access	Federated learning Training Data privacy	Federated learning offers a partial safeguard for participants’ data privacy. Nevertheless, the current absence of an efficient privacy-preserving federated learning technology tailored for the Internet of Things (IoT) poses a challenge. Numerous privacy-preserving federated learning frameworks have been proposed, primarily relying on homomorphic cryptosystems, yet their suitability for IoT remains limited. Furthermore, the application of federated learning in IoT confronts two significant obstacles: mitigating the substantial communication costs and communication failure rates, and effectively discerning and utilizing high-quality data while discarding low-quality data for collaborative modeling purposes. In order to address these challenges, this paper introduces a privacy-preserving optimal aggregation federated learning framework that relies on the utilization of the multi-key EC-ElGamal cryptosystem (MEEC) and the federated sum optimization algorithm (FSOA), which are characterized by their lightweight nature and fair properties. The proposed MEEC approach aims to tackle the issue of multi-key collaborative computing within the context of federated learning, thereby resulting in reduced communication costs and enhanced communication efficiency. This is achieved through the leverage of the EC-ElGamal cryptosystem, which is known for its ability to generate short keys and ciphertexts. Furthermore, this paper presents a dynamic federated learning framework that incorporates user dynamic quit and join algorithms. The primary objective of this framework is to mitigate the adverse effects of communication failures and enhance power computation on IoT devices. Additionally, an FSOA is devised to ensure the acquisition of optimal training data, thereby preventing the inclusion of low-quality data in the training process. Subsequently, the proposed scheme undergoes rigorous security analysis and performance evaluation. The obtained results unequivocally demonstrate that our scheme outperforms existing solutions in terms of security, practicality, and efficiency with lower communication and computational costs.	10.1109/TNSM.2024.3418786
Yacine Anser, Chrystel Gaber, Jean-Philippe Wary, Samia Bouzefrane, Méziane Yacoub, Onur Kalinagac, Gürkan Gür	Liability and Trust Analysis Framework for Multi-Actor Dynamic Microservices	2024	Early Access	Microservice architectures Measurement Service level agreements	Microservices architecture has become an increasingly common approach for building complex software systems. With the distributed nature of microservices, multiple actors can contribute to a service, hence affecting the dynamics of the environment and making the management of liabilities and trust more challenging. Service-Level Agreements (SLAs) are critical in that regard and any SLA violation or breach can result in significant financial damages. One major challenge is the lack of indicators to handle the liability and trust in such architectures. To address this issue, in this paper we propose a liability and trust analysis framework, namely the LASM Analysis Service (LAS), for multi-actor dynamic microservices that employs Machine Learning (ML) techniques.	10.1109/TNSM.2024.3417934
Tom Goethals, Mays Al-Naday, Bruno Volckaert, Filip De Turck	Warrens: Decentralized Connectionless Tunnels for Edge Container Networks	2024	Early Access	Containers Peer-to-peer computing Virtual private networks	In recent years, workload containerisation has been extended to the edge, bringing with it the need for flexible overlay networking. However, current container networking solutions are generally designed for the cloud, aimed at relatively static clusters with centralized generation of container subnet addresses and assigning them to nodes. Added to that existing tunneling solutions, such as Virtual Private Networks (VPN), also have centralized components. Conversely, the network edge is geo-dispersed and has a volatile topology,with edge nodes typically hidden behind routers, in private networks. To enable large-scale networking at the edge, there is need for decentralized self-management of container network addresses and overlay tunnels. This manuscript presents Warrens, a framework for fully decentralized and self-organizing cloud-edge container networks. Warrens enables communication between edge nodes in different private networks by enabling connectionless tunnels, supported by decentralized self-assignment of container IP addresses, with the assignment scheme minimizing address conflict to a negligible level. Warrens has been implemented in two variants using kernel-level eBPF for processing speed, and user-level Golang for wider compatibility. Warrens is shown to be highly scalable compared to a typical VPN solution, and performance evaluations demonstrate it can handle a full network load on both x64 devices and a Raspberry Pi with ≈0.5% to 5% total CPU load, depending on traffic direction and protocols used.	10.1109/TNSM.2024.3417703
Yibo Ma, Tong Li, Yan Zhou, Li Yu, Depeng Jin	Mitigating Energy Consumption in Heterogeneous Mobile Networks Through Data-Driven Optimization	2024	Early Access	5G mobile communication Base stations Computer architecture	5G networks, with their notable energy consumption, pose a significant challenge. Traditional energy-saving methods, effective for 4G, struggle in heterogeneous 4G and 5G networks. In this paper, we propose the pRoactivE Data-drivEn Energy Saving Method (REDEEM) to mitigate energy consumption in heterogeneous 4G and 5G mobile networks. REDEEM spatially divides the network into meshes based on cell overlaps, predicts cell traffic for proactive control, and selects active cells within each mesh. Our framework includes energy efficiency profiling for each mesh and offloads 5G traffic onto overlapping 4G cells to reduce 5G energy usage. Experiments based on the Nanchang mobile networks validate REDEEM’s effectiveness, yielding energy savings of 3442.72 MWh over a week. Notably, our approach achieves a 53.10% energy-saving rate, surpassing threshold-based methods by 38.85%, optimization-based methods by 18.15%, and fluid capacity engine by 14.79%. It minimally impacts service quality, with less than four parts per million traffic missed. Experimental results also demonstrate REDEEM’s robustness across various temporal, spatial, and traffic load scenarios.	10.1109/TNSM.2024.3416947
Wenhao Fan, Xuewei Li, Bihua Tang, Yi Su, Yuan’an Liu	MEC Network Slicing: Stackelberg-Game-Based Slice Pricing and Resource Allocation With QoS Guarantee	2024	Early Access	Resource management Pricing Network slicing	In multi-access edge computing (MEC) networks, network slicing enables the MEC network service provider (MEC-NSP) to provide customizable MEC services for user devices (UDs) with diverse QoS (Quality of Service) demands. In MEC network slicing, slice pricing and network resource allocation for slices are two core problems, which have not been jointly considered by existing works. To this end, we propose a two-stage slice pricing scheme to achieve balanced slice pricing and optimal network resource allocation. The goal of our scheme is to reduce the resource costs of the MEC-NSP and ensure its profit while meeting different user QoS requirements. At the first stage, we jointly optimize the computing, cache and communication resource allocation for all the slices by using problem decomposition. Then, we formulate a slice pricing problem based the Stackelberg game, prove the Nash equilibrium existence of the problem, and design an iterative algorithm based on the optimal response function. Extensive simulations are conducted in 4 scenarios, where our scheme is compared with 4 reference schemes. The simulation results demonstrate the superiority of our scheme in all the scenarios. The profit of the MEC-NSP optimized by our scheme is 17.64%-24.39% higher than those by the comparative works.	10.1109/TNSM.2024.3409277
Shalitha Wijethilaka, Awaneesh Kumar Yadav, An Braeken, Madhusanka Liyanage	Blockchain-Based Secure Authentication and Authorization Framework for Robust 5G Network Slicing	2024	Early Access	Noise measurement Security Blockchains	The rapid evolution of heterogeneous applications signifies the requirement for network slicing to cater to diverse network requirements. Network Functions (NFs), which are the essential elements of network slices, are required to communicate with each other securely to facilitate network services. Certificates are the established method to authenticate each other. However, dynamic certificate management while allowing NFs to communicate in a multi-operator environment is arduous. Also, sharing NFs between network slices originates authorization-related security challenges such as unauthorized service utilization, deceptive Denial of Service attacks, and data leakages from network slices. In this paper, we develop a novel framework to address the security challenges related to authentication and authorization in 5G network slicing systems. A blockchain-based multi-party distributed certificate management framework with secure communication protocols is developed using elliptic curve cryptography to facilitate certificate services for multi-operator environments. Also, we propose a blockchain-based NF authorization framework to mitigate the security vulnerabilities in NF sharing between network slices. We implement the proposed framework using Hyperledger Fabric blockchain with Java chain codes and perform comprehensive experiments to show the significance of our framework.The Ability to mitigate the single point of failure with respect to state-of-the-art, including traditional certificate authorities and blockchain-based certificate authorities, time analysis for certificate generation, and the potential to eliminate the mentioned authorization attacks are some of the experiments conducted.Also, we have shown that our framework is secure using informal and formal (using Real-Or-Random (ROR) logic and Scyther Validation tool) security verification mechanisms.	10.1109/TNSM.2024.3416418
Rong Chai, Guorong Yang, Lei Liu, Qianbin Chen	DRL-Based Dynamic Resource Allocation for Multi-Beam Satellite Systems	2024	Early Access	Resource management Satellites Heuristic algorithms	Multi-beam satellite communication systems have been widely recognized as an efficient technology for providing reliable and high-speed communication services. In this paper, we consider a multi-beam satellite communication system, which consists of a multi-beam satellite, ground cells and a ground gateway for processing information of the system. We focus on the beam scheduling, subchannel and power allocation problem to improve system performance. To jointly consider data transmission performance and power consumption, we define a utility function as the weighted sum of service queue length and satellite transmit power. To adapt to the dynamic arriving of data packets and the time-varying satellite channels, we formulate the resource allocation problem as a long-term utility function maximization problem. Since the optimization problem is a non-convex mixed integer problem, which cannot be solved using traditional convex optimization tools, we first decouple the original problem into beam scheduling subproblem and joint subchannel and power allocation subproblem. To solve beam scheduling subproblem, two beam scheduling schemes are proposed. Furthermore, three deep reinforcement learning (DRL)-based joint subchannel and power allocation algorithms are proposed to tackle joint subchannel and power allocation subproblem. Numerical results demonstrate the effectiveness of the proposed algorithms.	10.1109/TNSM.2024.3416443
Jiali Zheng, Yuxi Zhang	RSHS: A Blockchain Consensus Mechanism for Edge Computing-Supported Agri-IoT Systems	2024	Early Access	Agriculture Internet of Things Scalability Consensus protocol	The Agricultural Internet of Things (Agri-IoT) has emerged to boost food safety and agricultural efficiency, but its integration with centralized servers, even when supplemented by edge computing, presents risks. These include challenges in device security, data privacy, and scalability. In this paper, we integrate blockchain with Agri-IoT and introduce reputation-based secure HotStuff (RSHS), aiming to address these security and scalability issues. Firstly, RSHS calculates the comprehensive reputation of nodes by incorporating both consensus and execution reputations. This facilitates a classification threshold for nodes, enhancing the reliability of nodes in consensus. Secondly, RSHS integrates peer evaluation scores with reputation and a verifiable random function for primary node selection, thereby balancing security and unpredictability. Building on this approach, the phased selection strategy further reduces communication overhead by progressively eliminating low-reputation nodes. Finally, the simulation results reveal that with an increase in both the network size and the proportion of Byzantine nodes, RSHS, in comparison to HotStuff, diminishes the average delay by 68.89% and yields an average throughput that is 3.82 times higher. In terms of fault tolerance, RSHS improves the chain inclusion rate by 39.84% under fork attacks and sees a 17.85% increase under silence attacks. These findings demonstrate that RSHS consistently exhibits superior scalability and good fault tolerance.	10.1109/TNSM.2024.3415610
Ali Chouman, Dimitrios Michael Manias, Abdallah Shami	A Modular, End-to-End Next-Generation Network Testbed: Towards a Fully Automated Network Management Platform	2024	Early Access	5G mobile communication Network systems Testing	Experimentation in practical, end-to-end (E2E) next-generation networks deployments is becoming increasingly prevalent and significant in the realm of modern networking and wireless communications research. The prevalence of fifth-generation technology (5G) testbeds and the emergence of developing networks systems, for the purposes of research and testing, focus on the capabilities and features of analytics, intelligence, and automated management using novel testbed designs and architectures, ranging from simple simulations and setups to complex networking systems; however, with the ever-demanding application requirements for modern and future networks, 5G-and-beyond (denoted as 5G+) testbed experimentation can be useful in assessing the creation of large-scale network infrastructures that are capable of supporting E2E virtualized mobile network services. To this end, this paper presents a functional, modular E2E 5G+ system, complete with the integration of a Radio Access Network (RAN) and handling the connection of User Equipment (UE) in real-world scenarios. As well, this paper assesses and evaluates the effectiveness of emulating full network functionalities and capabilities, including a complete description of user-plane data, from UE registrations to communications sequences, and leads to the presentation of a future outlook in powering new experimentation for 6G and next-generation networks.	10.1109/TNSM.2024.3416031
Xin Li, Hui Zhou, Lirong Ma, Jingjie Xin, Shanguo Huang	Cost and Latency Customized SFC Deployment in Hybrid VNF and PNF Environment	2024	Early Access	Costs Hardware Cloud computing	The SFC deployment problem has proved to be NP-hard and has attracted great research interests. At present, most existing studies focus on deploying SFC through virtual network functions (VNFs) and few studies involve hybrid VNF and physical network function (PNF) environment. However, in the transition to a comprehensive network function virtualization (NFV) network, hybrid VNF and PNF environment is possible and important. Currently, VNF and PNF both play an important role in todays cloud networks. PNF is far superior in processing speed, but VNF has advantages in flexibility and cost. Using VNF or PNF alone may not meet user needs, affecting user experience and system performance. Hence, this paper studies the SFC deployment problem in hybrid VNF and PNF environment. A cost and latency customized dynamic SFC deployment (CLSFCD) scheme is proposed. Instead of giving priority to the PNF or the VNF roughly, the CL-SFCD scheme maps network functions to VNFs or PNFs according to the SFC requesters personalized demands to satisfy the demands of different users. It aims to minimize the weighted value of total cost and latency. The CL-SFCD scheme is formulated as a mixed integer linear programming (MILP) model for exact results. Heuristic algorithms are developed to provide an approximate optimal solution in large-scale cloud networks. Numerical results show that the CL-SFCD scheme achieves performance that balances cost and latency based on user requirements than the VNFonly, VNF-GA, and PNF-only deployment schemes.	10.1109/TNSM.2024.3415174
Ibrahim Mohammed Sayem, Moinul Islam Sayed, Sajal Saha, Anwar Haque	ENIDS: A Deep Learning-Based Ensemble Framework for Network Intrusion Detection Systems	2024	Early Access	Computer crime Support vector machines Telecommunication traffic	Rapid and widespread adoption of emerging Information Technology (IT) infrastructures and services in commercial and private endeavors opens new horizons for novel cyberattacks. Network Intrusion Detection Systems (NIDS) gained attention as an effective means of combating various cyber threats. Recent research demonstrates the potency of machine learning (ML) and deep learning (DL) approaches in the development of NIDS. In this paper, we propose a DL-based framework called the Ensemble Framework for Network Intrusion Detection System (ENIDS) to detect various types of cyberattacks, which includes dynamic data pre-processing, optimal feature selection, the handling of imbalanced data samples, and a DL-based ensemble model. Our DL-based ensemble model is comprised of two layers: the base learner and the meta-learner. The base learner is composed of three robust DL models: convolutional neural networks (CNN), long short-term memory (LSTM), and gated recurrent units (GRU), and the meta-learner is a deep neural network (DNN) model. The proposed framework experimented with two publicly available and popular network traffic datasets, namely UNSW-15 and CICIDS-2017. In the UNSW-15 and CICIDS-2017 datasets, our proposed framework detects cyberattacks with an accuracy of 90.6% and 99.6% and an F1-score of 90.5 and 99.6%, respectively. According to experimental findings, the proposed ensemble framework outperforms existing state-ofthe-art approaches and demonstrates better performance than benchmark DL methods in terms of accuracy, F1-score, and execution time for training and testing.	10.1109/TNSM.2024.3414305
Stefan Geißler, Andra Lutu, Florian Wamser, Thomas Favale, Viktoria Vomhoff, Michael Krolikowski, Marco Mellia, Diego Perino, Tobias Hoßfeld	Untangling IoT Global Connectivity: The Importance of Mobile Signaling Traffic	2024	Early Access	Internet of Things Ecosystems Monitoring	IoT plays an important role in cellular networks, and its need for global connectivity is driving the rise of Global IoT Providers. These provide service by aggregating multiple mobile providers through roaming, complicating the understanding of the overall mobile ecosystem. This calls for lightweight monitoring solutions, which are crucial to meet the quality demanded by IoT services, and of automatic means to analyze the data, with the final goal to carry out economic and management activities. This paper provides insights from the study of two commercial, widespread IoT providers. We show how monitoring signaling traffic between mobile networks offers a unique opportunity to understand both the IoT customers characteristics and the network functioning. Leveraging clustering, we offer the first data-driven methodology to examine large IoT signaling datasets. By analyzing over 1.3 billion signaling dialogues across two providers, we identify common signaling profiles that depend on the specific IoT vertical, likely misconfigured devices, and sudden changes that indicate potential problems. This provides actionable insights for network management decisions and service improvements, and lays the groundwork for future research on IoT traffic modeling.	10.1109/TNSM.2024.3414975