ACM Computing Surveys最新文献_第6页

Survey on Quality Assurance of Smart Contracts 智能合约质量保证调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-09-14 DOI: 10.1145/3695864

Zhiyuan Wei, Jing Sun, Zijian Zhang, Xianhao Zhang, Xiaoxuan Yang, Liehuang Zhu

{"title":"Survey on Quality Assurance of Smart Contracts","authors":"Zhiyuan Wei, Jing Sun, Zijian Zhang, Xianhao Zhang, Xiaoxuan Yang, Liehuang Zhu","doi":"10.1145/3695864","DOIUrl":"https://doi.org/10.1145/3695864","url":null,"abstract":"As blockchain technology continues to advance, the secure deployment of smart contracts has become increasingly prevalent, underscoring the critical need for robust security measures. This surge in usage has led to a rise in security breaches, often resulting in substantial financial losses for users. This paper presents a comprehensive survey of smart contract quality assurance, from understanding vulnerabilities to evaluating the effectiveness of detection tools. Our work is notable for its innovative classification of forty smart contract vulnerabilities, mapping them to established attack patterns. We further examine nine defense mechanisms, assessing their efficacy in mitigating smart contract attacks. Furthermore, we develop a labeled dataset as a benchmark encompassing ten common vulnerability types, which serves as a critical resource for future research. We also conduct comprehensive experiments to evaluate fourteen vulnerability detection tools, providing a comparative analysis that highlights their strengths and limitations. In summary, this survey synthesizes state-of-the-art knowledge in smart contract security, offering practical recommendations to guide future research and foster the development of robust security practices in the field.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":16.6,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142374639","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

Alert Prioritisation in Security Operations Centres: A Systematic Survey on Criteria and Methods 安全行动中心的警报优先级：关于标准和方法的系统调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-09-14 DOI: 10.1145/3695462

Fatemeh Jalalvand, Mohan Baruwal Chhetri, Surya Nepal, Cecile Paris

{"title":"Alert Prioritisation in Security Operations Centres: A Systematic Survey on Criteria and Methods","authors":"Fatemeh Jalalvand, Mohan Baruwal Chhetri, Surya Nepal, Cecile Paris","doi":"10.1145/3695462","DOIUrl":"https://doi.org/10.1145/3695462","url":null,"abstract":"Security Operations Centres (SOCs) are specialised facilities where security analysts leverage advanced technologies to monitor, detect, and respond to cyber incidents. However, the increasing volume of security incidents has overwhelmed security analysts, leading to alert fatigue. Effective alert prioritisation (AP) becomes crucial to address this problem through the utilisation of proper criteria and methods. Human-AI teaming (HAT) has the potential to significantly enhance AP by combining the complementary strengths of humans and AI. AI excels in processing large volumes of alert data, identifying anomalies, uncovering hidden patterns, and prioritising alerts at scale, all at machine speed. Human analysts can leverage their expertise to investigate prioritised alerts, re-prioritise them based on additional context, and provide valuable feedback to the AI system, reducing false positives and ensuring critical alerts are prioritised. This work provides a comprehensive review of the criteria and methods for AP in SOC. We analyse the advantages and disadvantages of the different categories of AP criteria and methods based on HAT, specifically considering automation, augmentation, and collaboration. We also identify several areas for future research. We anticipate that our findings will contribute to the advancement of AP techniques, fostering more effective security incident response in SOCs.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":16.6,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142374667","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

State of the Art and Potentialities of Graph-level Learning 图层面学习的现状与潜力

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-09-13 DOI: 10.1145/3695863

Zhenyu Yang, Ge Zhang, Jia Wu, Jian Yang, Quan Z. Sheng, Shan Xue, Chuan Zhou, Charu Aggarwal, Hao Peng, Wenbin Hu, Edwin Hancock, Pietro Liò

{"title":"State of the Art and Potentialities of Graph-level Learning","authors":"Zhenyu Yang, Ge Zhang, Jia Wu, Jian Yang, Quan Z. Sheng, Shan Xue, Chuan Zhou, Charu Aggarwal, Hao Peng, Wenbin Hu, Edwin Hancock, Pietro Liò","doi":"10.1145/3695863","DOIUrl":"https://doi.org/10.1145/3695863","url":null,"abstract":"Graphs have a superior ability to represent relational data, like chemical compounds, proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as input, has been applied to many tasks including comparison, regression, classification, and more. Traditional approaches to learning a set of graphs heavily rely on hand-crafted features, such as substructures. While these methods benefit from good interpretability, they often suffer from computational bottlenecks as they cannot skirt the graph isomorphism problem. Conversely, deep learning has helped graph-level learning adapt to the growing scale of graphs by extracting features automatically and encoding graphs into low-dimensional representations. As a result, these deep graph learning methods have been responsible for many successes. Yet, no comprehensive survey reviews graph-level learning starting with traditional learning and moving through to the deep learning approaches. This article fills this gap and frames the representative algorithms into a systematic taxonomy covering traditional learning, graph-level deep neural networks, graph-level graph neural networks, and graph pooling. In addition, the evolution and interaction between methods from these four branches within their developments are examined to provide an in-depth analysis. This is followed by a brief review of the benchmark datasets, evaluation metrics, and common downstream applications. Finally, the survey concludes with an in-depth discussion of 12 current and future directions in this booming field.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":16.6,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142374640","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

Image steganography approaches and their detection strategies: a survey 图像隐写术方法及其检测策略概览

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-09-10 DOI: 10.1145/3694965

Meike Helena Kombrink, Zeno Jean Marius Hubert Geradts, Marcel Worring

{"title":"Image steganography approaches and their detection strategies: a survey","authors":"Meike Helena Kombrink, Zeno Jean Marius Hubert Geradts, Marcel Worring","doi":"10.1145/3694965","DOIUrl":"https://doi.org/10.1145/3694965","url":null,"abstract":"Steganography is the art and science of hidden (or covered) communication. In digital steganography, the bits of images, videos, audio and text files are tweaked to represent the information to hide. This paper covers the current methods for hiding information in images, alongside steganalysis methods which aim to detect the presence of steganography. By reviewing 456 references, this paper discusses the different approaches that can be taken toward steganography and its much less widely studied counterpart. Currently in research older steganography approaches are more widely used than newer methods even though these show greater potential. New methods do have flaws, therefore more research is needed to make these practically applicable. For steganalysis one of the greatest challenges is the generalisability. Often one scheme can detect the presence of one specific hiding method. More research is needed to combine current schemes and/or create new generalisable schemes. To allow readers to compare results between different papers in our work performance indications of all steganalysis methods are outlined and a comparison of performance is included. This comparison is given using ’topological sorting’ graphs, which compares detection results from all papers (as stated in the papers themselves) on different steganographic schemes.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":16.6,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142374641","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

Multimodal Recommender Systems: A Survey 多模式推荐系统：调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-09-10 DOI: 10.1145/3695461

Qidong Liu, Jiaxi Hu, Yutian Xiao, Xiangyu Zhao, Jingtong Gao, Wanyu Wang, Qing Li, Jiliang Tang

{"title":"Multimodal Recommender Systems: A Survey","authors":"Qidong Liu, Jiaxi Hu, Yutian Xiao, Xiangyu Zhao, Jingtong Gao, Wanyu Wang, Qing Li, Jiliang Tang","doi":"10.1145/3695461","DOIUrl":"https://doi.org/10.1145/3695461","url":null,"abstract":"The recommender system (RS) has been an integral toolkit of online services. They are equipped with various deep learning techniques to model user preference based on identifier and attribute information. With the emergence of multimedia services, such as short videos, news and etc. , understanding these contents while recommending becomes critical. Besides, multimodal features are also helpful in alleviating the problem of data sparsity in RS. Thus, M ultimodal R ecommender S ystem (MRS) has attracted much attention from both academia and industry recently. In this paper, we will give a comprehensive survey of the MRS models, mainly from technical views. First, we conclude the general procedures and major challenges for MRS. Then, we introduce the existing MRS models according to four categories, i.e., Modality Encoder , Feature Interaction , Feature Enhancement and Model Optimization . Besides, to make it convenient for those who want to research this field, we also summarize the dataset and code resources. Finally, we discuss some promising future directions of MRS and conclude this paper. To access more details of the surveyed papers, such as implementation code, we open source a repository.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":16.6,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142374645","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

How to Improve Video Analytics with Action Recognition: A Survey 如何通过动作识别改进视频分析？一项调查

IF 23.8 1区计算机科学

ACM Computing Surveys Pub Date : 2024-08-08 DOI: 10.1145/3679011

Gayathri T, Mamatha Hr

引用次数: 0

When Federated Learning Meets Privacy-Preserving Computation 当联合学习遇上隐私保护计算

IF 23.8 1区计算机科学

ACM Computing Surveys Pub Date : 2024-07-22 DOI: 10.1145/3679013

Jingxue Chen, Hang Yan, Zhiyuan Liu, Min Zhang, Hu Xiong, Shui Yu

{"title":"When Federated Learning Meets Privacy-Preserving Computation","authors":"Jingxue Chen, Hang Yan, Zhiyuan Liu, Min Zhang, Hu Xiong, Shui Yu","doi":"10.1145/3679013","DOIUrl":"https://doi.org/10.1145/3679013","url":null,"abstract":"Nowadays, with the development of artificial intelligence (AI), privacy issues attract wide attention from society and individuals. It is desirable to make the data available but invisible, i.e., to realize data analysis and calculation without disclosing the data to unauthorized entities. Federated learning (FL) has emerged as a promising privacy-preserving computation method for AI. However, new privacy issues have arisen in FL-based application because various inference attacks can still infer relevant information about the raw data from local models or gradients. This will directly lead to the privacy disclosure. Therefore, it is critical to resist these attacks to achieve complete privacy-preserving computation. In light of the overwhelming variety and a multitude of privacy-preserving computation protocols, we survey these protocols from a series of perspectives to supply better comprehension for researchers and scholars. Concretely, the classification of attacks is discussed including four kinds of inference attacks as well as malicious server and poisoning attack. Besides, this paper systematically captures the state of the art of privacy-preserving computation protocols by analyzing the design rationale, reproducing the experiment of classic schemes, and evaluating all discussed protocols in terms of efficiency and security properties. Finally, this survey identifies a number of interesting future directions.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":23.8,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141817556","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 review and benchmark of feature importance methods for neural networks 神经网络特征重要性方法回顾与基准

IF 23.8 1区计算机科学

ACM Computing Surveys Pub Date : 2024-07-19 DOI: 10.1145/3679012

Hannes Mandler, Bernhard Weigand

{"title":"A review and benchmark of feature importance methods for neural networks","authors":"Hannes Mandler, Bernhard Weigand","doi":"10.1145/3679012","DOIUrl":"https://doi.org/10.1145/3679012","url":null,"abstract":"\u0000 Feature attribution methods (AMs) are a simple means to provide explanations for the predictions of black-box models like neural networks. Due to their conceptual differences, the numerous different methods, however, yield ambiguous explanations. While this allows for obtaining different insights into the model, it also complicates the decision which method to adopt. This paper, therefore, summarizes the current state of the art regarding AMs, which includes the requirements and desiderata of the methods themselves as well as the properties of their explanations. Based on a survey of existing methods, a representative subset consisting of the\u0000 δ\u0000 -sensitivity index, permutation feature importance, variance-based feature importance in artificial neural networks and DeepSHAP, is described in greater detail and, for the first time, benchmarked in a regression context. Specifically for this purpose, a new verification strategy for model-specific AMs is proposed. As expected, the explanations’ agreement with the intuition and among each other clearly depends on the AMs’ properties. This has two implications: First, careful reasoning about the selection of an AM is required. Secondly, it is recommended to apply multiple AMs and combine their insights in order to reduce the model’s opacity even further.\u0000","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":23.8,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141823933","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

Enabling Technologies and Techniques for Floor Identification 楼层识别的使能技术和工艺

IF 23.8 1区计算机科学

ACM Computing Surveys Pub Date : 2024-07-17 DOI: 10.1145/3678878

Imran Ashraf, Y. B. Zikria, Sahil Garg, Soojung Hur, Yongwan Park, Mohsen Guizani

{"title":"Enabling Technologies and Techniques for Floor Identification","authors":"Imran Ashraf, Y. B. Zikria, Sahil Garg, Soojung Hur, Yongwan Park, Mohsen Guizani","doi":"10.1145/3678878","DOIUrl":"https://doi.org/10.1145/3678878","url":null,"abstract":"Location information has initiated a multitude of applications such as location-based services, health care, emergency response and rescue operations, and assets tracking. A plethora of techniques and technologies have been presented to ensure enhanced location accuracy, both horizontal and vertical. Despite many surveys covering horizontal localization technologies, the literature lacks a comprehensive survey incorporating up-to-data vertical localization approaches. This paper provides a detailed survey of different vertical localization techniques such as path loss models, time of arrival, received signal strength, reference signal received power, and fingerprinting utilized by WiFi, radio frequency identification (RFID), global system for mobile communications (GSM), long term evolution (LTE), barometer, inertial measurement unit (IMU) sensors, and geomagnetic field. The paper primarily aims at human localization in indoor environments using smartphones in essence. Besides the localization accuracy, the presented approaches are evaluated in terms of cost, infrastructure dependence, deployment complexity, and sensitivity. We highlight the pros and cons of these approaches and outline future research directions to enhance the accuracy to meet the future needs of floor identification standards set by the Federal Communications Commission.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":23.8,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141831472","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 Comprehensive Analysis of Explainable AI for Malware Hunting 全面分析用于恶意软件猎杀的可解释人工智能

IF 23.8 1区计算机科学

ACM Computing Surveys Pub Date : 2024-07-11 DOI: 10.1145/3677374

Mohd Saqib, Samaneh Mahdavifar, Benjamin C. M. Fung, P. Charland

{"title":"A Comprehensive Analysis of Explainable AI for Malware Hunting","authors":"Mohd Saqib, Samaneh Mahdavifar, Benjamin C. M. Fung, P. Charland","doi":"10.1145/3677374","DOIUrl":"https://doi.org/10.1145/3677374","url":null,"abstract":"In the past decade, the number of malware variants has increased rapidly. Many researchers have proposed to detect malware using intelligent techniques, such as Machine Learning (ML) and Deep Learning (DL), which have high accuracy and precision. These methods, however, suffer from being opaque in the decision-making process. Therefore, we need Artificial Intelligence (AI)-based models to be explainable, interpretable, and transparent to be reliable and trustworthy. In this survey, we reviewed articles related to Explainable AI (XAI) and their application to the significant scope of malware detection. The article encompasses a comprehensive examination of various XAI algorithms employed in malware analysis. Moreover, we have addressed the characteristics, challenges, and requirements in malware analysis that cannot be accommodated by standard XAI methods. We discussed that even though Explainable Malware Detection (EMD) models provide explainability, they make an AI-based model more vulnerable to adversarial attacks. We also propose a framework that assigns a level of explainability to each XAI malware analysis model, based on the security features involved in each method. In summary, the proposed project focuses on combining XAI and malware analysis to apply XAI models for scrutinizing the opaque nature of AI systems and their applications to malware analysis.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":23.8,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141656095","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