ACM TRANSACTIONS ON INTERNET TECHNOLOGY (TOIT) 汇集了许多计算学科，包括计算机软件工程、计算机编程语言、中间件、数据库管理、安全性、知识发现和数据挖掘、网络和分布式系统、通信、性能和可扩展性等。TOIT 将涵盖各个学科的结果和作用以及它们之间的关系。

ACM Transactions on Internet Technology (TOIT) brings together research from various computer science disciplines, including software engineering, programming languages, middleware, database management, security, knowledge discovery and data mining, networking and distributed systems, communication, performance, and scalability. TOIT focuses on the results and interrelations of these disciplines.

《ACM Transactions on Internet Technology (TOIT)》汇集了多个计算机学科的研究成果，包括计算机软件工程、编程语言、中间件、数据库管理、安全性、知识发现与数据挖掘、网络与分布式系统、通信、性能与可扩展性等。TOIT 关注这些学科的研究成果及其相互关系。

ACM Transactions on Internet Technology

We can often detect from a person’s utterances whether he or she is in favor of or against a given target entity—one’s stance toward the target. However, a person may express the same stance toward a target by using negative or positive language. Here for the first time we present a dataset of tweet–target pairs annotated for both stance and sentiment. The targets may or may not be referred to in the tweets, and they may or may not be the target of opinion in the tweets. Partitions of this dataset were used as training and test sets in a SemEval-2016 shared task competition. We propose a simple stance detection system that outperforms submissions from all 19 teams that participated in the shared task. Additionally, access to both stance and sentiment annotations allows us to explore several research questions. We show that although knowing the sentiment expressed by a tweet is beneficial for stance classification, it alone is not sufficient. Finally, we use additional unlabeled data through distant supervision techniques and word embeddings to further improve stance classification.

Saif M. Mohammad

Parinaz Sobhani

Svetlana Kiritchenko

ACM Trans. Internet Technol.

Stance and Sentiment in Tweets

推文中的立场与情感

The blockchain technology has been gaining an increasing popularity for the last years, and smart contracts are being used for a growing number of applications in several scenarios. The execution of smart contracts on public blockchains can be invoked by any user with a transaction, although in many scenarios there would be the need for restricting the right of executing smart contracts only to a restricted set of users. To help deal with this issue, this article proposes a system based on a popular access control framework called RT, Role-based Trust Management, to regulate smart contracts execution rights. The proposed system, called Layer 2 DecentrAlized Role-based Trust management (L2DART), implements the RT framework on a public blockchain, and it is designed as a layer-2 technology that involves both on-chain and off-chain functionalities to reduce the blockchain costs while keeping blockchain auditability, i.e., immutability and transparency. The on-chain costs of L2DART have been evaluated on Ethereum and compared with a previous solution implementing on-chain all the functionalities. The results show that the on-chain costs of L2DART are relatively low, making the system deployable in real-world scenarios.

Andrea De Salve

Luca Franceschi

Andrea Lisi

Paolo Mori

Laura Ricci

L2DART: A Trust Management System Integrating Blockchain and Off-Chain Computation

L2DART：一种集成区块链与链下计算的信任管理系统

The advent of Deep Neural Networks (DNNs) has empowered numerous computer-vision applications. Due to the high computational intensity of DNN models, as well as the resource constrained nature of Industrial Internet-of-Things (IIoT) devices, it is generally very challenging to deploy and execute DNNs efficiently in the industrial scenarios. Substantial research has focused on model compression or edge-cloud offloading, which trades off accuracy for efficiency or depends on high-quality infrastructure support, respectively. In this article, we present EdgeDI, a framework for executing DNN inference in a partitioned, distributed manner on a cluster of IIoT devices. To improve the inference performance, EdgeDI exploits two key optimization knobs, including: (1) Model compression based on deep architecture design, which transforms the target DNN model into a compact one that reduces the resource requirements for IIoT devices without sacrificing accuracy; (2) Distributed inference based on adaptive workload partitioning, which achieves high parallelism by adaptively balancing the workload distribution among IIoT devices under heterogeneous resource conditions. We have implemented EdgeDI based on PyTorch, and evaluated its performance with the NEU-CLS defect classification task and two typical DNN models (i.e., VGG and ResNet) on a cluster of heterogeneous Raspberry Pi devices. The results indicate that the proposed two optimization approaches significantly outperform the existing solutions in their specific domains. When they are well combined, EdgeDI can provide scalable DNN inference speedups that are very close to or even much higher than the theoretical speedup bounds, while still maintaining the desired accuracy.

Weiwei Fang

Wenyuan Xu

Chongchong Yu

Neal. N. Xiong

Joint Architecture Design and Workload Partitioning for DNN Inference on Industrial IoT Clusters

工业物联网集群上DNN推理的联合架构设计与工作负载划分

Decentralized Finance (DeFi), a pivotal component of the emerging Web3 landscape, is gaining popularity but remains vulnerable to market manipulations, such as wash trading. Wash trading is an illegal practice, where traders buy and sell assets to themselves within cryptocurrency exchanges to artificially inflate trading volumes and distort market perceptions. However, current research primarily focuses on traditional exchanges based on the Order-Book mechanism (similar to stock markets), while ignoring the Automated Market Maker (AMM) exchanges, which dominate over 75% of the market and represent a significant innovation within the DeFi. This study utilizes entity recognition technology to detect wash trading on AMM exchanges within Ethereum-like systems, based on the understanding that colluding addresses (perceived as the same entity) must use ETH for transaction fees and exhibit direct or indirect ETH transfer links. We identify wash trading when addresses with transfer connections almost simultaneously buy and sell assets while their total asset holdings remain nearly constant. This comprehensive blockchain network analysis, compared to focusing solely on transactions within exchanges, unveils covert wash trading activities. Our detection method achieves a 95.9% recall and a 96.7% true negative rate in identifying pools affected by wash trading, demonstrating its superiority over existing methods. Furthermore, we apply our method to 98,945 pools from Uniswap V2 &amp; V3 (the most popular AMM exchanges on Ethereum) and identify 1,070,626 abnormal transactions, totaling $27.51 billion in trading volume. Analysis of these transactions uncovers insights into wash traders’ behaviors, including the utilization of multiple addresses and the dual roles of certain addresses as wash traders and liquidity providers. These insights are crucial for developing more effective strategies to combat fraudulent activities in the DeFi ecosystem and enhance financial scrutiny.

Rundong Gan

Le Wang

Liang Xue

Xiaodong Lin

Exposing Stealthy Wash Trading on Automated Market Maker Exchanges

揭示自动化做市商交易所中的隐秘洗售交易

Simon Mayer

Arne Broering

Kimberly Garcia

Konstantinos Fysarakis

Beatriz Soret

Introduction to the Special Issue on Distributed Intelligence on the Internet

《互联网分布式智能特刊导言》

We propose a novel change detection framework to identify changes in the long-term performance behavior of an IaaS service. An IaaS service’s long-term performance behavior is represented by an IaaS performance signature. The proposed framework leverages time series similarity measures and a sliding window technique to detect changes in IaaS performance signatures. We introduce a new IaaS performance noise model that enables the proposed framework to distinguish between performance noise and actual changes in performance. The proposed framework utilizes a novel Signal-to-Noise Ratio (SNR) based approach to detect changes when prior knowledge about performance noise is available. A set of experiments is conducted using real-world datasets to demonstrate the effectiveness of the proposed change detection framework.

Athman Bouguettaya

Sheik Mohammad Mostakim Fattah

Signature-based IaaS Performance Change Detection

基于签名的IaaS性能变化检测

This work aims to develop a surveillance mission system using unmanned aerial vehicles (UAVs) or drones when Denial-of-Service (DoS) attacks are present to disrupt normal operations for mission systems. In particular, we introduce the concept of cyber deception using honey drones (HDs) to protect the mission system from DoS attacks. HDs exhibit fake vulnerabilities and employ stronger signal strengths to lure DoS attacks, unlike the legitimate drones called mission drones (MDs) deployed for mission execution. This research formulates an optimization problem to identify an optimal set of signal strengths of HDs and MDs to best prevent the system from DoS attacks while maximizing mission performance under the resource constraints of UAVs. To solve this optimization problem, we leverage deep reinforcement learning (DRL) to achieve these multiple objectives of the mission system concerning system security and performance. Particularly, for efficient and effective parallel processing in DRL, we utilize a DRL algorithm called the Asynchronous Advantage Actor-Critic (A3C) algorithm to model attack-defense interactions. We employ a physical engine-based simulation testbed to consider realistic scenarios and demonstrate valid findings from the realistic testbed. The extensive experiments proved that our HD-based approach could achieve up to a 32% increase in mission completion, a 20% reduction in energy consumption, and a 62% decrease in attack success rates compared to existing defense strategies.

Zelin Wan

Jin-Hee Cho

Mu Zhu

Ahmed Anwar

Charles Kamhoua

Munindar Singh

Optimizing Effectiveness and Defense of Drone Surveillance Missions via Honey Drones

通过蜜罐无人机优化无人机监视任务的有效性和防御

The Internet of Things (IoT) devices have been increasingly deployed in smart homes to automatically monitor and control their environments. Unfortunately, extensive recent research has shown that on-path external adversaries can infer and further fingerprint people’s sensitive private information by analyzing IoT network traffic traces. In addition, most recent approaches that aim to defend against these malicious IoT traffic analytics cannot adequately protect user privacy with reasonable traffic overhead. In particular, these approaches often did not consider practical traffic reshaping limitations, user daily routine permitting, and user privacy protection preference in their design. To address these issues, we design a new low-cost, open-source user-centric defense system—PrivacyGuard that enables people to regain the privacy leakage control of their IoT devices, while still permitting sophisticated IoT data analytics that is necessary for smart home automation. In essence, our approach employs intelligent deep convolutional generative adversarial networks (DCGANs)-assisted IoT device traffic signature learning, long short-term memory (LSTM)-based artificial traffic signature injection, and partial traffic reshaping to obfuscate private information that can be observed in IoT device traffic traces. We evaluate PrivacyGuard using IoT network traffic traces of 31 IoT devices from 5 smart homes and buildings. We find that PrivacyGuard can effectively prevent a wide range of state-of-the-art adversarial machine learning and deep learning based user in-home activity inference and fingerprinting attacks and help users to achieve the balance between their IoT data utility and privacy preserving.

Keyang Yu

Qi Li

Dong Chen

Liting Hu

Safeguarding User-Centric Privacy in Smart Homes

保护智能家居中的用户中心隐私

Network Function Virtualization (NFV) is a promising technology that can transform how internet service providers deliver their services. However, recent studies have identified several challenges in adopting NFV. Two key challenges are central to the operation and capacity planning of NFV Data Centers (DCs): (i) Service Function Chain (SFC) realization – determining if a new request with a known profile can be accommodated, and (ii) Network Function Virtualization (NFV) traffic bounds estimation – estimating the total traffic that a data center can handle considering all service requests and their performance constraints. To address these challenges, we propose a model that leverages stochastic network calculus to effectively dimension an NFV DC while ensuring delay and availability bounds for all service requests. Our theoretical model provides a mathematical framework to assess the realization of a single SFC request without delving into the specifics of the realization process. We utilize established availability-aware Virtual Network Function (VNF) placement patterns to obtain traffic bounds essential to planning data center capacity. We analyze NFV data center traffic under various scenarios over a Fat-tree DC topology. The results demonstrate that data center capacity is significantly influenced by the VNF placement strategy. Additionally, for data centers hosting latency-sensitive services, Service Level Objective (SLO) constraints on availability and delay are crucial in determining the number of such requests that can be accommodated.

Sidharth Sharma

Admela Jukan

Aashi Malik

Ashwin Gumaste

A Network Calculus Model for SFC Realization and Traffic Bounds Estimation in Data Centers

数据中心中SFC实现与流量界限估计的网络演算模型

短名	ACM Trans. Internet Technol.
Journal Impact	3.77
国际分区	COMPUTER SCIENCE, SOFTWARE ENGINEERING(Q1)
期刊索引	SCI Q1中科院 3 区

ISSN	1533-5399, 1557-6051
h-index	65
国内分区	计算机科学(3区)计算机科学计算机信息系统(3区)计算机科学计算机软件工程(3区)

涉及主题	计算机科学操作系统万维网工程类数学人工智能计算机安全程序设计语言法学政治学计算机网络经济生物物理哲学业务机器学习互联网统计地理电信分布式计算互联网隐私
出版信息	出版商: Association for Computing Machinery (ACM)，出版周期: Quarterly，期刊类型: journal，开源期刊: 否
基本数据	创刊年份: 2001，原创研究文献占比: 100.00%，自引率:0.00%， Gold OA占比: 1.52%
平均审稿周期	网友分享经验：>12周，或约稿
平均录用比例	网友分享经验：容易

ACM Transactions on Internet Technology

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Stance and Sentiment in Tweets

L2DART: A Trust Management System Integrating Blockchain and Off-Chain Computation

Joint Architecture Design and Workload Partitioning for DNN Inference on Industrial IoT Clusters

Exposing Stealthy Wash Trading on Automated Market Maker Exchanges

Introduction to the Special Issue on Distributed Intelligence on the Internet

Introduction to the Special Issue on Distributed Intelligence on the Internet

Signature-based IaaS Performance Change Detection

Optimizing Effectiveness and Defense of Drone Surveillance Missions via Honey Drones

Safeguarding User-Centric Privacy in Smart Homes

A Network Calculus Model for SFC Realization and Traffic Bounds Estimation in Data Centers