IEEE/ACM TRANSACTIONS ON NETWORKING 的高层次目标是发表来自通信/计算机网络领域的理论或实验探索的高质量原创研究成果，涵盖各种物理层技术上的各种信息传输网络，有线（各种引导媒体：例如，铜、光学）和无线（例如，射频、声学（例如，水下）、红外线），或这些的混合。该杂志欢迎报告新经验和实际系统实验的应用贡献。

The IEEE/ACM Transactions on Networking aims to publish high-quality original research results from the field of communication and computer networking. These studies can be theoretical or experimental, covering various physical layer technologies for information transmission networks, including wired networks (such as copper and optical media) and wireless networks (such as radio frequency, acoustic (e.g., underwater), and infrared), as well as hybrid applications of these technologies. The journal welcomes reports of new experiences and practical system experimental contributions.

IEEE/ACM Transactions on Networking 的目标是发表通信和计算机网络领域的高质量原创研究成果。这些研究可以是理论性的，也可以是实验性的，涵盖各种物理层技术的信息传输网络，包括有线网络（如铜缆和光纤）和无线网络（如射频、声学（例如水下声学）和红外线），以及这些技术的混合应用。该期刊欢迎报告新的经验和实际系统实验的应用贡献。

IEEE/ACM Transactions on Networking

Junjie Xiong

Mingkui Wei

Zhuo Lu

Yao Liu

Warmonger Attack: A Novel Attack Vector in Serverless Computing

With many network protocols using obfuscation techniques to hide their identity, robust methods of traffic classification are required. In traditional deep-packet-inspection (DPI) methods, application specific signatures are generated with byte-level data from payload. Increasingly new data formats are being used to encode the application protocols with bit-level information which render the byte-level signatures ineffective. In this paper, we describe BitCoding a bit-level DPI-based signature generation technique. BitCoding uses only a small number of initial bits from a flow and identify invariant bits as signature. Subsequently, these bit signatures are encoded and transformed into a newly defined state transition machine transition constrained counting automata. While short signatures are efficient for processing, this will increase the chances of collision and cross signature matching with increase in number of signatures (applications). We describe a method for signature similarity detection using a variant of Hamming distance and propose to increase the length of signatures for a subset of protocols to avoid overlaps. We perform extensive experiments with three different data sets consisting of 537 380 flows with a packet count of 3 445 969 and show that, BitCoding has very good detection performance across different types of protocols (text, binary, and proprietary) making it protocol-type agnostic. Further, to understand the portability of signatures generated we perform cross evaluation, i.e., signatures generated from one site are used for testing with data from other sites to conclude that it will lead to a small compromise in detection performance.

Neminath Hubballi

Mayank Swarnkar

$BitCoding$ : Network Traffic Classification Through Encoded Bit Level Signatures

In this paper, we introduce a new performance metric in the framework of status updates that we will refer to as the Age of Incorrect Information (AoII). This new metric deals with the shortcomings of both the Age of Information (AoI) and the conventional error penalty functions as it neatly extends the notion of fresh updates to that of fresh “informative” updates. The word informative in this context refers to updates that bring new and correct information to the monitor side. After properly motivating the new metric, and with the aim of minimizing its average, we formulate a Markov Decision Process (MDP) in a transmitter-receiver pair scenario where packets are sent over an unreliable channel. We show that a simple “always update” policy minimizes the aforementioned average penalty along with the average age and prediction error. We then tackle the general, and more realistic case, where the transmitter cannot surpass a specific power budget. The problem is formulated as a Constrained Markov Decision Process (CMDP) for which we provide a Lagrangian approach to solve. After characterizing the optimal transmission policy of the Lagrangian problem, we provide a rigorous mathematical proof to showcase that a mixture of two Lagrange policies is optimal for the CMDP in question. Equipped with this, we provide a low complexity algorithm that finds the AoII-optimal operating point of the system in the constrained scenario. Lastly, simulation results are laid out to showcase the performance of the proposed policy and highlight the differences with the AoI framework.

Ali Maatouk

Saad Kriouile

Mohamad Assaad

Anthony Ephremides

IEEE/ACM Trans. Networking

The Age of Incorrect Information: A New Performance Metric for Status Updates

Request latency directly affects the performance of modern cloud applications. Due to various causes in hosts and networks, requests can suffer from request latency anomalies (RLAs), which may violate the Service-Level Agreement. However, existing performance monitoring tools have incomplete coverage and inconsistent semantics for monitoring requests and cannot accurately diagnose RLAs. This paper presents <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BufScope , a high-coverage and low-overhead request event monitoring system, which monitors <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">buffers to capture most RLA-related abnormal events with consistent request-level semantics in the end-to-end datapath of request. First, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BufScope models the datapath of request as a buffer chain and defines events based on three properties of buffers, so as to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">end-to-end monitor the root causes of RLA. Then, to achieve <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">consistent semantics for captured events, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BufScope designs a request-level semantics injection mechanism to make events captured in networks have the victim requests’ ID. Finally, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BufScope offloads the semantics operations and event collection in software to SmartNICs for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">low CPU overhead . We have implemented <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BufScope on commodity SmartNICs and programmable switches. Evaluation results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BufScope can diagnose 98% RLAs with < 0.08% network bandwidth overhead and 0.6% application throughput decline.

Kaihui Gao

Chen Sun

Shuai Wang

Dan Li

Yu Zhou

Hongqiang Harry Liu

Lingjun Zhu

Ming Zhang

Xiang Deng

Cheng Zhou

Lu Lu

Buffer-Based High-Coverage and Low-Overhead Request Event Monitoring in the Cloud

基于缓冲的高覆盖率与低开销云请求事件监控

The lifetime of a UAV-assisted wireless network is determined by the amount of energy consumed by the UAVs during flight, data collection, and transmission to the ground station. Routing protocols are commonly used for data transmission in a communication network. However, because of the mobility of UAVs, using a routing protocol with a single communication technology results in higher delay and more energy consumption in a UAV-assisted wireless network. To overcome this, we propose two reinforcement learning (RL) algorithms, Q-learning and deep Q-network (DQN), for energy-efficient data transmission over a hybrid BLE/LTE/Wi-Fi/LoRa UAV-assisted wireless network. We consider BLE, LTE, Wi-Fi, and LoRa for communication over a UAV-GS link. The RL algorithms take any random network as input and learn the best policy to output the network with less energy consumption. The reward/penalty is chosen in such a way that the network with the highest energy consumption is penalized and the one with the lowest is rewarded, thereby minimizing total network energy consumption. Based on learning, it creates a hybrid BLE/LTE/Wi-Fi/LoRa UAV-assisted wireless network by assigning the best communication technology to a UAV-GS link. Further, we compare the performance of proposed RL algorithms with a rule-based algorithm and random hybrid scheme. In addition, we propose a theoretical framework for constructing hybrid network for both free space and free space multipath path loss models. We demonstrate the performance comparison of the proposed work with the conventional shortest path routing algorithm in terms of network energy consumption and average network delay using extensive results. Finally, the effect of the velocity of the UAV and the number of packets on the performance of the proposed framework is analyzed.

Wilson Ayyanthole Nelson

Sreenivasa Reddy Yeduri

Ajit Jha

Abhinav Kumar

Linga Reddy Cenkeramaddi

RL-Based Energy-Efficient Data Transmission Over Hybrid BLE/LTE/Wi-Fi/LoRa UAV-Assisted Wireless Network

Ziming Zhao

Zhaoxuan Li

Xiaofei Xie

Jiongchi Yu

Fan Zhang

Rui Zhang

Binbin Chen

Xiangyang Luo

Ming Hu

Wenrui Ma

: Towards Fine-Grained Unknown Class Detection Against the Open-Set Attack Spectrum With Variable Legitimate Traffic

面向开放式攻击谱中可变合法流量下的细粒度未知类别检测

MmWaves have been envisioned as a promising direction to provide Gbps wireless access. However, they are susceptible to high path losses and blockages, which can only be partially mitigated by directional antennas. That makes mmWave networks coverage-limited, thus requiring dense deployments. Integrated access and backhaul (IAB) architectures have emerged as a cost-effective solution for network densification. Resource allocation in mmWave IAB networks must face big challenges originated by heavy temporal dynamics, such as intermittent links caused by user mobility and blockages from moving obstacles. This makes it extremely difficult to find optimal and adaptive solutions. In this article, exploiting the distributed structure of the problem, we propose a Multi-Agent Reinforcement Learning (MARL) framework to optimize user throughput via flow routing and link scheduling in mmWave IAB networks characterized by mobile users and obstacles. The proposed approach implicitly captures the environment dynamics, coordinates the interference, and manages the buffer levels of IAB relay nodes. We design different MARL components, respectively for full-duplex and half-duplex networks. In addition, we propose an online training algorithm, which addresses the feasibility issues of practical systems, especially the communication and coordination among RL agents. Numerical results show the effectiveness of the proposed approach.

Bibo Zhang

Ilario Filippini

Mobility-Aware Resource Allocation for mmWave IAB Networks: A Multi-Agent Reinforcement Learning Approach

移动性感知的毫米波IAB网络资源分配：一种多智能体强化学习方法

The disaggregated and hierarchical architecture of Open Radio Access Network (ORAN) with openness paradigm promises to deliver the ever demanding 5G services. Meanwhile, it also faces new challenges for the efficient deployment of Machine Learning (ML) models. Although ORAN has been designed with built-in Radio Intelligent Controllers (RIC) providing the capability of training ML models, traditional centralized learning methods may be no longer appropriate for the RICs due to privacy issues, computational burden, and communication overhead. Recently, Federated Learning (FL), a powerful distributed ML training, has emerged as a new solution for training models in ORAN systems. 5G use cases such as meeting the network slice Service Level Agreement (SLA) and Key Performance Indicator (KPI) monitoring for the smart radio resource management can greatly benefit from the FL models. However, training FL models efficiently in ORAN system is a challenging issue due to the stringent deadline of ORAN control loops, expensive compute resources, and limited communication bandwidth. Moreover, to deliver Grade of Service (GoS), the trained ML models must converge with acceptable accuracy. In this paper, we propose a second order gradient descent based FL training method named MCORANFed that utilizes compression techniques to minimize the communication cost and yet converges at a faster rate than state-of-the-art FL variants. We formulate a joint optimization problem to minimize the overall resource cost and learning time, and then solve it by the decomposition method. Our experimental results prove that MCORANFed is communication efficient with respect to ORAN system, and outperforms FL methods like MFL, FedAvg, and ORANFed in terms of costs and convergence rate.

Amardip Kumar Singh

Kim Khoa Nguyen

Communication Efficient Compressed and Accelerated Federated Learning in Open RAN Intelligent Controllers

开放RAN智能控制器中高效通信的压缩与加速联邦学习

Jinlei Lin

Chenglong Li

Wenwen Gong

Guanglei Song

Linna Fan

Zhiliang Wang

Jiahai Yang

ProbeGeo: A Comprehensive Landmark Mining Framework Based on Web Content

ProbeGeo：基于网络内容的全面地标挖掘框架

Su Wang

Roberto Morabito

Seyyedali Hosseinalipour

Mung Chiang

Christopher G. Brinton

Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networks

合作边缘网络中联邦学习的设备采样与资源优化

Abbreviation	IEEE/ACM Trans. Networking
Journal Impact	3.12
Quartiles(Global)	TELECOMMUNICATIONS(Q2)

ISSN	1063-6692, 1558-2566
h-index	184

Publication Information	Publisher: Institute of Electrical and Electronics Engineers Inc.，Publishing cycle: Bimonthly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 1993，Proportion of original research papers: 100.00%，Self Citation Rate:6.70%， Gold OA Rate: 13.67%
Average review cycle	网友分享经验：偏慢,4-8周
Average recruitment ratio	网友分享经验：较易

IEEE/ACM Transactions on Networking

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