ACM Computing Surveys最新文献_第2页

Backdoor Attacks and Defenses Targeting Multi-Domain AI Models: A Comprehensive Review 针对多域人工智能模型的后门攻击和防御：全面回顾

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-11-15 DOI: 10.1145/3704725

Shaobo Zhang, Yimeng Pan, Qin Liu, Zheng Yan, Kim-Kwang Raymond Choo, Guojun Wang

{"title":"Backdoor Attacks and Defenses Targeting Multi-Domain AI Models: A Comprehensive Review","authors":"Shaobo Zhang, Yimeng Pan, Qin Liu, Zheng Yan, Kim-Kwang Raymond Choo, Guojun Wang","doi":"10.1145/3704725","DOIUrl":"https://doi.org/10.1145/3704725","url":null,"abstract":"Since the emergence of security concerns in artificial intelligence (AI), there has been significant attention devoted to the examination of backdoor attacks. Attackers can utilize backdoor attacks to manipulate model predictions, leading to significant potential harm. However, current research on backdoor attacks and defenses in both theoretical and practical fields still has many shortcomings. To systematically analyze these shortcomings and address the lack of comprehensive reviews, this paper presents a comprehensive and systematic summary of both backdoor attacks and defenses targeting multi-domain AI models. Simultaneously, based on the design principles and shared characteristics of triggers in different domains and the implementation stages of backdoor defense, this paper proposes a new classification method for backdoor attacks and defenses. We use this method to extensively review backdoor attacks in the fields of computer vision and natural language processing, and also examine the current applications of backdoor attacks in audio recognition, video action recognition, multimodal tasks, time series tasks, generative learning, and reinforcement learning, while critically analyzing the open problems of various backdoor attack techniques and defense strategies. Finally, this paper builds upon the analysis of the current state of AI security to further explore potential future research directions for backdoor attacks and defenses.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"5 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142642616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Systematic Review of Generative Modelling Tools and Utility Metrics for Fully Synthetic Tabular Data 全合成表格式数据的生成建模工具和效用指标系统性综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-11-14 DOI: 10.1145/3704437

Anton Danholt Lautrup, Tobias Hyrup, Arthur Zimek, Peter Schneider-Kamp

{"title":"Systematic Review of Generative Modelling Tools and Utility Metrics for Fully Synthetic Tabular Data","authors":"Anton Danholt Lautrup, Tobias Hyrup, Arthur Zimek, Peter Schneider-Kamp","doi":"10.1145/3704437","DOIUrl":"https://doi.org/10.1145/3704437","url":null,"abstract":"Sharing data with third parties is essential for advancing science, but it is becoming more and more difficult with the rise of data protection regulations, ethical restrictions, and growing fear of misuse. Fully synthetic data, which transcends anonymisation, may be the key to unlocking valuable untapped insights stored away in secured data vaults. This review examines current synthetic data generation methods and their utility measurement. We found that more traditional generative models such as Classification and Regression Tree models alongside Bayesian Networks remain highly relevant and are still capable of surpassing deep learning alternatives like Generative Adversarial Networks. However, our findings also display the same lack of agreement on metrics for evaluation, uncovered in earlier reviews, posing a persistent obstacle to advancing the field. We propose a tool for evaluating the utility of synthetic data and illustrate how it can be applied to three synthetic data generation models. By streamlining evaluation and promoting agreement on metrics, researchers can explore novel methods and generate compelling results that will convince data curators and lawmakers to embrace synthetic data. Our review emphasises the potential of synthetic data and highlights the need for greater collaboration and standardisation to unlock its full potential.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"21 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Democratizing Container Live Migration for Enhanced Future Networks - A Survey 面向增强型未来网络的民主化容器实时迁移--一项调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-11-14 DOI: 10.1145/3704436

Wissem Soussi, Gürkan Gür, Burkhard Stiller

引用次数: 0

Membership Inference Attacks and Defenses in Federated Learning: A Survey 联盟学习中的成员推理攻击与防御：调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-11-14 DOI: 10.1145/3704633

Li Bai, Haibo Hu, Qingqing Ye, Haoyang Li, Leixia Wang, Jianliang Xu

{"title":"Membership Inference Attacks and Defenses in Federated Learning: A Survey","authors":"Li Bai, Haibo Hu, Qingqing Ye, Haoyang Li, Leixia Wang, Jianliang Xu","doi":"10.1145/3704633","DOIUrl":"https://doi.org/10.1145/3704633","url":null,"abstract":"Federated learning is a decentralized machine learning approach where clients train models locally and share model updates to develop a global model. This enables low-resource devices to collaboratively build a high-quality model without requiring direct access to the raw training data. However, despite only sharing model updates, federated learning still faces several privacy vulnerabilities. One of the key threats is membership inference attacks, which target clients’ privacy by determining whether a specific example is part of the training set. These attacks can compromise sensitive information in real-world applications, such as medical diagnoses within a healthcare system. Although there has been extensive research on membership inference attacks, a comprehensive and up-to-date survey specifically focused on it within federated learning is still absent. To fill this gap, we categorize and summarize membership inference attacks and their corresponding defense strategies based on their characteristics in this setting. We introduce a unique taxonomy of existing attack research and provide a systematic overview of various countermeasures. For these studies, we thoroughly analyze the strengths and weaknesses of different approaches. Finally, we identify and discuss key future research directions for readers interested in advancing the field.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"37 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Acceleration for Deep Reinforcement Learning using Parallel and Distributed Computing: A Survey 使用并行和分布式计算加速深度强化学习：调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-11-14 DOI: 10.1145/3703453

Zhihong Liu, Xin Xu, Peng Qiao, DongSheng Li

{"title":"Acceleration for Deep Reinforcement Learning using Parallel and Distributed Computing: A Survey","authors":"Zhihong Liu, Xin Xu, Peng Qiao, DongSheng Li","doi":"10.1145/3703453","DOIUrl":"https://doi.org/10.1145/3703453","url":null,"abstract":"Deep reinforcement learning has led to dramatic breakthroughs in the field of artificial intelligence for the past few years. As the amount of rollout experience data and the size of neural networks for deep reinforcement learning have grown continuously, handling the training process and reducing the time consumption using parallel and distributed computing is becoming an urgent and essential desire. In this paper, we perform a broad and thorough investigation on training acceleration methodologies for deep reinforcement learning based on parallel and distributed computing, providing a comprehensive survey in this field with state-of-the-art methods and pointers to core references. In particular, a taxonomy of literature is provided, along with a discussion of emerging topics and open issues. This incorporates learning system architectures, simulation parallelism, computing parallelism, distributed synchronization mechanisms, and deep evolutionary reinforcement learning. Further, we compare 16 current open-source libraries and platforms with criteria of facilitating rapid development. Finally, we extrapolate future directions that deserve further research.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"197 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Survey on Security of UAV Swarm Networks: Attacks and Countermeasures 无人机群网络安全调查：攻击与对策

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-11-08 DOI: 10.1145/3703625

Xiaojie Wang, Zhonghui Zhao, Ling Yi, Zhaolong Ning, Lei Guo, F. Richard Yu, Song Guo

{"title":"A Survey on Security of UAV Swarm Networks: Attacks and Countermeasures","authors":"Xiaojie Wang, Zhonghui Zhao, Ling Yi, Zhaolong Ning, Lei Guo, F. Richard Yu, Song Guo","doi":"10.1145/3703625","DOIUrl":"https://doi.org/10.1145/3703625","url":null,"abstract":"The increasing popularity of Unmanned Aerial Vehicle (UAV) swarms is attributed to their ability to generate substantial returns for various industries at a low cost. Additionally, in the future landscape of wireless networks, UAV swarms can serve as airborne base stations, alleviating the scarcity of communication resources. However, UAV swarm networks are vulnerable to various security threats that attackers can exploit with unpredictable consequences. Against this background, this paper provides a comprehensive review on security of UAV swarm networks. We begin by briefly introducing the dominant UAV swarm technologies, followed by their civilian and military applications. We then present and categorize various potential attacks that UAV swarm networks may encounter, such as denial-of-service attacks, man-in-the-middle attacks and attacks against Machine Learning (ML) models. After that, we introduce security technologies that can be utilized to address these attacks, including cryptography, physical layer security techniques, blockchain, ML, and intrusion detection. Additionally, we investigate and summarize mitigation strategies addressing different security threats in UAV swarm networks. Finally, some research directions and challenges are discussed.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"150 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142597420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Security and Privacy on Generative Data in AIGC: A Survey AIGC 中生成数据的安全性和隐私性：一项调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-11-07 DOI: 10.1145/3703626

Tao Wang, Yushu Zhang, Shuren Qi, Ruoyu Zhao, Xia Zhihua, Jian Weng

{"title":"Security and Privacy on Generative Data in AIGC: A Survey","authors":"Tao Wang, Yushu Zhang, Shuren Qi, Ruoyu Zhao, Xia Zhihua, Jian Weng","doi":"10.1145/3703626","DOIUrl":"https://doi.org/10.1145/3703626","url":null,"abstract":"The advent of artificial intelligence-generated content (AIGC) represents a pivotal moment in the evolution of information technology. With AIGC, it can be effortless to generate high-quality data that is challenging for the public to distinguish. Nevertheless, the proliferation of generative data across cyberspace brings security and privacy issues, including privacy leakages of individuals and media forgery for fraudulent purposes. Consequently, both academia and industry begin to emphasize the trustworthiness of generative data, successively providing a series of countermeasures for security and privacy. In this survey, we systematically review the security and privacy on generative data in AIGC, particularly for the first time analyzing them from the perspective of information security properties. Specifically, we reveal the successful experiences of state-of-the-art countermeasures in terms of the foundational properties of privacy, controllability, authenticity, and compliance, respectively. Finally, we show some representative benchmarks, present a statistical analysis, and summarize the potential exploration directions from each of theses properties.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"37 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Open-Ethical AI: Advancements in Open-Source Human-Centric Neural Language Models 开放伦理人工智能：以人为本的开源神经语言模型的进展

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-11-06 DOI: 10.1145/3703454

Sabrina Sicari, Jesus F. Cevallos M., Alessandra Rizzardi, Alberto Coen-Porisini

引用次数: 0

A Survey on Emerging Trends and Applications of 5G and 6G to Healthcare Environments 5G 和 6G 在医疗环境中的新兴趋势和应用调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-11-02 DOI: 10.1145/3703154

Shamsher Ullah, Jianqiang Li, Jie Chen, IKRAM ALI, Salabat Khan, Abdul Ahad, Farhan Ullah, Victor Leung

{"title":"A Survey on Emerging Trends and Applications of 5G and 6G to Healthcare Environments","authors":"Shamsher Ullah, Jianqiang Li, Jie Chen, IKRAM ALI, Salabat Khan, Abdul Ahad, Farhan Ullah, Victor Leung","doi":"10.1145/3703154","DOIUrl":"https://doi.org/10.1145/3703154","url":null,"abstract":"A delay, interruption, or failure in the wireless connection has a significant impact on the performance of wirelessly connected medical equipment. Researchers presented the fastest technological innovations and industrial changes to address these problems and improve the applications of information and communication technology. The development of the 6G communication infrastructure was greatly aided by the use of Block-chain technology, artificial intelligence (AI), virtual reality (VR), and the Internet of Things (IoT). In this paper, we comprehensively discuss 6G technologies enhancement, its fundamental architecture, difficulties, and other issues associated with it. In addition, the outcomes of our research help make 6G technology more applicable to real-world medical environments. The most important thing that this study has contributed is an explanation of the path that future research will take and the current state of the art. This study might serve as a jumping-off point for future researchers in the academic world who are interested in investigating the possibilities of 6G technological developments.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"26 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142566124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation Methodologies in Software Protection Research 软件保护研究中的评估方法

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-11-02 DOI: 10.1145/3702314

Bjorn De Sutter, Sebastian Schrittwieser, Bart Coppens, Patrick Kochberger

{"title":"Evaluation Methodologies in Software Protection Research","authors":"Bjorn De Sutter, Sebastian Schrittwieser, Bart Coppens, Patrick Kochberger","doi":"10.1145/3702314","DOIUrl":"https://doi.org/10.1145/3702314","url":null,"abstract":"Man-at-the-end (MATE) attackers have full control over the system on which the attacked software runs, and try to break the confidentiality or integrity of assets embedded in the software. Both companies and malware authors want to prevent such attacks. This has driven an arms race between attackers and defenders, resulting in a plethora of different protection and analysis methods. However, it remains difficult to measure the strength of protections because MATE attackers can reach their goals in many different ways and a universally accepted evaluation methodology does not exist. This survey systematically reviews the evaluation methodologies of papers on obfuscation, a major class of protections against MATE attacks. For 571 papers, we collected 113 aspects of their evaluation methodologies, ranging from sample set types and sizes, over sample treatment, to performed measurements. We provide detailed insights into how the academic state of the art evaluates both the protections and analyses thereon. In summary, there is a clear need for better evaluation methodologies. We identify nine challenges for software protection evaluations, which represent threats to the validity, reproducibility, and interpretation of research results in the context of MATE attacks and formulate a number of concrete recommendations for improving the evaluations reported in future research papers.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"7 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142566121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0