Foroozan Darbandeh, Muhammad Rizwan Asghar, Liqun Chen
The rapid expansion of the Internet of Things (IoT) across industries such as healthcare, manufacturing, transportation, and smart cities has made these networks prime targets for cyber attacks. Due to their distributed nature, device diversity, and resource constraints, traditional cyber security solutions alone are insufficient to protect against evolving threats. In addition, the increasing complexity of managing numerous interconnected devices and the limitations of realtime threat detection heighten the risk of cyber breaches. As a result, researchers and engineers are shifting beyond purely defensive cyber security approaches and focusing instead on recoverability and adaptability through cyber resilience mechanisms. The primary objective of cyber resilience in IoT networks is to go beyond conventional protective layers, ensuring long-term sustainability and strengthening resilience against persistent and sophisticated cyber threats. This survey analyses the cyber resilience concept and its steps in IoT networks and outlines challenges in providing cyber resilience in these networks. We review existing definitions of cyber resilience, highlighting their limitations in the IoT context. Also, the relationship between the key security features of IoT networks and cyber resilience is examined. We categorise proposed cyber resilience mechanisms according to their operational layers within the IoT architecture and evaluate them across multiple dimensions, including resilience phases, alignment with IoT requirements, the application domain and employed techniques. Furthermore, this survey examines several directions for future research by highlighting the diverse challenges posed by the various facets of IoT networks within this research domain. The findings of this research contribute to the existing body of knowledge on IoT security and cyber resilience while laying the groundwork for future research and development. Ultimately, this survey seeks to support the development of effective and sustainable strategies to ensure the security and resilience of IoT networks in the face of evolving cyber threats.
{"title":"A Survey on Cyber Resilience in IoT Networks: Challenges, Mechanisms, and Future Directions","authors":"Foroozan Darbandeh, Muhammad Rizwan Asghar, Liqun Chen","doi":"10.1145/3845986","DOIUrl":"https://doi.org/10.1145/3845986","url":null,"abstract":"The rapid expansion of the Internet of Things (IoT) across industries such as healthcare, manufacturing, transportation, and smart cities has made these networks prime targets for cyber attacks. Due to their distributed nature, device diversity, and resource constraints, traditional cyber security solutions alone are insufficient to protect against evolving threats. In addition, the increasing complexity of managing numerous interconnected devices and the limitations of realtime threat detection heighten the risk of cyber breaches. As a result, researchers and engineers are shifting beyond purely defensive cyber security approaches and focusing instead on recoverability and adaptability through cyber resilience mechanisms. The primary objective of cyber resilience in IoT networks is to go beyond conventional protective layers, ensuring long-term sustainability and strengthening resilience against persistent and sophisticated cyber threats. This survey analyses the cyber resilience concept and its steps in IoT networks and outlines challenges in providing cyber resilience in these networks. We review existing definitions of cyber resilience, highlighting their limitations in the IoT context. Also, the relationship between the key security features of IoT networks and cyber resilience is examined. We categorise proposed cyber resilience mechanisms according to their operational layers within the IoT architecture and evaluate them across multiple dimensions, including resilience phases, alignment with IoT requirements, the application domain and employed techniques. Furthermore, this survey examines several directions for future research by highlighting the diverse challenges posed by the various facets of IoT networks within this research domain. The findings of this research contribute to the existing body of knowledge on IoT security and cyber resilience while laying the groundwork for future research and development. Ultimately, this survey seeks to support the development of effective and sustainable strategies to ensure the security and resilience of IoT networks in the face of evolving cyber threats.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"51 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148890204","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}
Steffen Eger, Yong Cao, Jennifer D'Souza, Andreas Geiger, Christian Greisinger, Stephanie Gross, Yufang Hou, Brigitte Krenn, Anne Lauscher, Yizhi Li, Chenghua Lin, Nafise Moosavi, Wei Zhao, Tristan Miller
With the advent of large multimodal language models, science is now at a threshold of an AI-based technological transformation. An emerging ecosystem of models and tools aims to support researchers throughout the scientific lifecycle, including (1) searching for relevant literature, (2) generating research ideas and conducting experiments, (3) producing text-based content, (4) creating multimodal artifacts such as figures and diagrams, and (5) evaluating scientific work, as in peer review. In this survey, we provide a curated overview of literature representative of the core techniques, evaluation practices, and emerging trends in AI-assisted scientific discovery. Across the five tasks outlined above, we discuss datasets, methods, results, evaluation strategies, limitations, and ethical concerns, including risks to research integrity through the misuse of generative models. We aim for this survey to serve both as an accessible, structured orientation for newcomers to the field, as well as a catalyst for new AI-based initiatives and their integration into future “AI4Science” systems.
{"title":"Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation","authors":"Steffen Eger, Yong Cao, Jennifer D'Souza, Andreas Geiger, Christian Greisinger, Stephanie Gross, Yufang Hou, Brigitte Krenn, Anne Lauscher, Yizhi Li, Chenghua Lin, Nafise Moosavi, Wei Zhao, Tristan Miller","doi":"10.1145/3845596","DOIUrl":"https://doi.org/10.1145/3845596","url":null,"abstract":"With the advent of large multimodal language models, science is now at a threshold of an AI-based technological transformation. An emerging ecosystem of models and tools aims to support researchers throughout the scientific lifecycle, including (1) searching for relevant literature, (2) generating research ideas and conducting experiments, (3) producing text-based content, (4) creating multimodal artifacts such as figures and diagrams, and (5) evaluating scientific work, as in peer review. In this survey, we provide a curated overview of literature representative of the core techniques, evaluation practices, and emerging trends in AI-assisted scientific discovery. Across the five tasks outlined above, we discuss datasets, methods, results, evaluation strategies, limitations, and ethical concerns, including risks to research integrity through the misuse of generative models. We aim for this survey to serve both as an accessible, structured orientation for newcomers to the field, as well as a catalyst for new AI-based initiatives and their integration into future “AI4Science” systems.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"46 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148890209","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}
Yong Li, Yuang Chen, Qiming Liang, Shuhan Lv, Fang Lin
Despite the remarkable achievements of deep neural networks (DNNs) in numerous fields, the growing number of parameters and computational complexity severely limit their deployment feasibility on edge devices. Against this backdrop, lightweight DNNs have not only become a hot topic in academic research but also a key technological pathway to promote the democratization and implementation of AI. This article focuses on reviewing the methods of designing lightweight DNN architectures to achieve model lightweighting, aiming to provide researchers with effective solutions for designing lightweight model architectures. The article distinguishes between convolutional-based and Transformer-based frameworks for model design and delves into several typical lightweight model structural designs, development paths, and their pros and cons. By experimentally comparing the lightweighting metrics of different models, this article points out that model selection needs to be closely integrated with the constraints of specific application scenarios. Finally, the article observes that future breakthroughs may lie in exploring the lightweighting of hybrid architectures that combine convolution and Transformer, to integrate the advantages of local perception and global modeling, and further enhance model expressiveness while maintaining efficiency. In summary, this article not only provides a comprehensive review of lightweight model structural design but also emphasizes its practical guidance and development direction in promoting the implementation of edge intelligence.
{"title":"A Survey on Lightweight Deep Neural Network Architecture Design","authors":"Yong Li, Yuang Chen, Qiming Liang, Shuhan Lv, Fang Lin","doi":"10.1145/3842661","DOIUrl":"https://doi.org/10.1145/3842661","url":null,"abstract":"Despite the remarkable achievements of deep neural networks (DNNs) in numerous fields, the growing number of parameters and computational complexity severely limit their deployment feasibility on edge devices. Against this backdrop, lightweight DNNs have not only become a hot topic in academic research but also a key technological pathway to promote the democratization and implementation of AI. This article focuses on reviewing the methods of designing lightweight DNN architectures to achieve model lightweighting, aiming to provide researchers with effective solutions for designing lightweight model architectures. The article distinguishes between convolutional-based and Transformer-based frameworks for model design and delves into several typical lightweight model structural designs, development paths, and their pros and cons. By experimentally comparing the lightweighting metrics of different models, this article points out that model selection needs to be closely integrated with the constraints of specific application scenarios. Finally, the article observes that future breakthroughs may lie in exploring the lightweighting of hybrid architectures that combine convolution and Transformer, to integrate the advantages of local perception and global modeling, and further enhance model expressiveness while maintaining efficiency. In summary, this article not only provides a comprehensive review of lightweight model structural design but also emphasizes its practical guidance and development direction in promoting the implementation of edge intelligence.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"17 10 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148890202","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}
Deep Neural Networks (DNNs) have proven to be extremely effective at learning a wide range of tasks. Due to their complexity and inexplicable internal state, DNNs are difficult to analyze. Several attempts to interpret their operation have been made, but analyzing them from the perspective of the knowledge encoded in their layers is a promising research direction. The purpose of this survey is two-fold: a) to review the concept of DNN knowledge quantification and highlight it as an important near-future challenge, and b) to provide a brief account of the scant existing methods attempting to actually quantify DNN knowledge.
{"title":"Quantifying the Knowledge in Deep Neural Networks: An Overview","authors":"Ioanna Valsamara, Ioannis Mademlis, Ioannis Pitas","doi":"10.1145/3845595","DOIUrl":"https://doi.org/10.1145/3845595","url":null,"abstract":"Deep Neural Networks (DNNs) have proven to be extremely effective at learning a wide range of tasks. Due to their complexity and inexplicable internal state, DNNs are difficult to analyze. Several attempts to interpret their operation have been made, but analyzing them from the perspective of the knowledge encoded in their layers is a promising research direction. The purpose of this survey is two-fold: a) to review the concept of DNN knowledge quantification and highlight it as an important near-future challenge, and b) to provide a brief account of the scant existing methods attempting to actually quantify DNN knowledge.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"54 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148883811","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}
Shasha Guo, Liang Pang, Jing Zhang, Cuiping Li, Hong Chen
The goal of question generation is to automatically produce relevant and meaningful questions from diverse inputs such as knowledge bases, natural language texts, and images. With the rapid advancement of neural architectures, neural question generation (NQG) has attracted growing attention across both academia and industry. In this survey, we provide a comprehensive review of developments in NQG, spanning traditional neural approaches to the latest paradigms driven by large language models (LLMs) and multimodal large language models (MLLMs). We begin by outlining the fundamental components of NQG, including its problem formulation, benchmark datasets, evaluation metrics, and representative applications. Next, we categorize existing methods into three main types: structured NQG , which relies on structured data sources; unstructured NQG , which handles loosely structured inputs such as texts or images; and hybrid NQG , which integrates multiple modalities. For each category, we review representative neural models and synthesize the problems addressed by successive generations of methods, their remaining limitations, and the motivations behind major methodological transitions. Furthermore, we trace the progression of NQG from supervised neural approaches and pre-trained models to prompting, retrieval-augmented generation, reinforcement learning, and emerging tool-augmented and agent-based paradigms. We also discuss how recent LLMs and MLLMs have enabled more contextually aligned, knowledge-grounded, and reasoning-enhanced question generation, together with emerging concerns such as hallucination, bias, and evaluation reliability. Finally, we outline open challenges and emerging research trends, offering a forward-looking perspective on the evolution of NQG. This survey presents a meticulously curated compilation of related papers, datasets, and code, serving as a comprehensive resource for anyone studying NQG.
{"title":"A Review of Neural Question Generation: Approaches, Challenges, and Future Directions","authors":"Shasha Guo, Liang Pang, Jing Zhang, Cuiping Li, Hong Chen","doi":"10.1145/3843765","DOIUrl":"https://doi.org/10.1145/3843765","url":null,"abstract":"The goal of question generation is to automatically produce relevant and meaningful questions from diverse inputs such as knowledge bases, natural language texts, and images. With the rapid advancement of neural architectures, neural question generation (NQG) has attracted growing attention across both academia and industry. In this survey, we provide a comprehensive review of developments in NQG, spanning traditional neural approaches to the latest paradigms driven by large language models (LLMs) and multimodal large language models (MLLMs). We begin by outlining the fundamental components of NQG, including its problem formulation, benchmark datasets, evaluation metrics, and representative applications. Next, we categorize existing methods into three main types: <jats:italic toggle=\"yes\">structured NQG</jats:italic> , which relies on structured data sources; <jats:italic toggle=\"yes\">unstructured NQG</jats:italic> , which handles loosely structured inputs such as texts or images; and <jats:italic toggle=\"yes\">hybrid NQG</jats:italic> , which integrates multiple modalities. For each category, we review representative neural models and synthesize the problems addressed by successive generations of methods, their remaining limitations, and the motivations behind major methodological transitions. Furthermore, we trace the progression of NQG from supervised neural approaches and pre-trained models to prompting, retrieval-augmented generation, reinforcement learning, and emerging tool-augmented and agent-based paradigms. We also discuss how recent LLMs and MLLMs have enabled more contextually aligned, knowledge-grounded, and reasoning-enhanced question generation, together with emerging concerns such as hallucination, bias, and evaluation reliability. Finally, we outline open challenges and emerging research trends, offering a forward-looking perspective on the evolution of NQG. This survey presents a meticulously curated compilation of related papers, datasets, and code, serving as a comprehensive resource for anyone studying NQG.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"17 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148883836","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}
This is a corrigendum for the article "40 Years of Designing Code Comprehension Experiments: A Systematic Mapping Study" published in ACM Comput. Surv. 56, 4, Article 106 (November 2023), 42 pages.
{"title":"Corrigendum: 40 Years of Designing Code Comprehension Experiments: A Systematic Mapping Study","authors":"Marvin Wyrich, Justus Bogner, Stefan Wagner","doi":"10.1145/3838282","DOIUrl":"https://doi.org/10.1145/3838282","url":null,"abstract":"This is a corrigendum for the article \"40 Years of Designing Code Comprehension Experiments: A Systematic Mapping Study\" published in ACM Comput. Surv. 56, 4, Article 106 (November 2023), 42 pages.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"13 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148883837","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}
Lilan Liu, Yixiang Zhang, Yan-Ning Sun, Hongxia Cai, Chen Wang
The Industrial Internet of Things (IIoT) is characterized by the generation of vast amounts of time-series data. Modern IIoT systems enable efficient collection, storage, and querying of massive industrial time-series data, making the processing and analysis of such data a key enabler for data-driven decision-making in modern manufacturing. To provide researchers and practitioners with comprehensive guidance on industrial time series data analysis, this paper presents a systematic review of state-of-the-art methods—spanning statistical approaches, machine learning (ML), deep learning (DL), and cutting-edge large models—along with their applications in industrial decision-making. It details the application status of these methods in key equipment condition monitoring, manufacturing process supervision, and energy network management. Additionally, the paper discusses existing gaps between methods and real-world applications, as well as future trends and challenges, such as optimizing data structures for cost-sensitive learning, exploring causality and time-series-oriented model architectures, and developing cascaded/hybrid pipelines for end-to-end industrial use cases. Ultimately, this review aims to inspire innovations in realizing data-driven intelligent decision-making for next-generation IIoT systems.
{"title":"A State-Of-The-Art Review of Industrial Time Series Data Analysis: Methods and Applications","authors":"Lilan Liu, Yixiang Zhang, Yan-Ning Sun, Hongxia Cai, Chen Wang","doi":"10.1145/3844497","DOIUrl":"https://doi.org/10.1145/3844497","url":null,"abstract":"The Industrial Internet of Things (IIoT) is characterized by the generation of vast amounts of time-series data. Modern IIoT systems enable efficient collection, storage, and querying of massive industrial time-series data, making the processing and analysis of such data a key enabler for data-driven decision-making in modern manufacturing. To provide researchers and practitioners with comprehensive guidance on industrial time series data analysis, this paper presents a systematic review of state-of-the-art methods—spanning statistical approaches, machine learning (ML), deep learning (DL), and cutting-edge large models—along with their applications in industrial decision-making. It details the application status of these methods in key equipment condition monitoring, manufacturing process supervision, and energy network management. Additionally, the paper discusses existing gaps between methods and real-world applications, as well as future trends and challenges, such as optimizing data structures for cost-sensitive learning, exploring causality and time-series-oriented model architectures, and developing cascaded/hybrid pipelines for end-to-end industrial use cases. Ultimately, this review aims to inspire innovations in realizing data-driven intelligent decision-making for next-generation IIoT systems.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"133 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148883838","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}
Federated learning (FL) has emerged as a secure paradigm for collaborative training among clients. Without data centralization, FL allows clients to share local information in a privacy-preserving manner. This approach has gained considerable attention, promoting numerous surveys to summarize the related works. However, the majority of these surveys concentrate on FL methods that share model parameters during the training process, while overlooking the possibility of sharing local information in other forms. In this paper, we present a systematic survey from a new perspective of what to share in FL, with an emphasis on the model utility, privacy leakage, and communication efficiency. First, we present a new taxonomy of FL methods in terms of three sharing methods, which respectively share model, synthetic data, and knowledge. Second, we analyze the vulnerability of different sharing methods to privacy attacks and review the defense mechanisms. Third, we conduct extensive experiments to compare the learning performance and communication overhead of various sharing methods in FL. Besides, we assess the potential privacy leakage through model inversion and membership inference attacks, while comparing the effectiveness of various defense approaches. Finally, we identify future research directions and conclude the survey.
{"title":"A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency","authors":"Jiawei Shao, Zijian Li, Wenqiang Sun, Tailin Zhou, Yuchang Sun, Lumin Liu, Zehong Lin, Yuyi Mao, Jun Zhang","doi":"10.1145/3844940","DOIUrl":"https://doi.org/10.1145/3844940","url":null,"abstract":"Federated learning (FL) has emerged as a secure paradigm for collaborative training among clients. Without data centralization, FL allows clients to share local information in a privacy-preserving manner. This approach has gained considerable attention, promoting numerous surveys to summarize the related works. However, the majority of these surveys concentrate on FL methods that share model parameters during the training process, while overlooking the possibility of sharing local information in other forms. In this paper, we present a systematic survey from a new perspective of <jats:italic toggle=\"yes\">what to share</jats:italic> in FL, with an emphasis on the model utility, privacy leakage, and communication efficiency. First, we present a new taxonomy of FL methods in terms of three sharing methods, which respectively share model, synthetic data, and knowledge. Second, we analyze the vulnerability of different sharing methods to privacy attacks and review the defense mechanisms. Third, we conduct extensive experiments to compare the learning performance and communication overhead of various sharing methods in FL. Besides, we assess the potential privacy leakage through model inversion and membership inference attacks, while comparing the effectiveness of various defense approaches. Finally, we identify future research directions and conclude the survey.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"14 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148860959","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}
Hongcheng Fan, Jielun Wu, Xincheng He, Yang Feng, Yibiao Yang, Baowen Xu, Qingkai Shi
Reading the original semantics from symbol-stripped binaries, symbol-stripped bytecode, or symbol-stripped source code is challenging because high-level information, such as function and variable names, is unavailable. Recovering these names is crucial to understanding software behavior and enabling security applications. Previous work has shown that lost symbol names can be recovered, leading many researchers to propose approaches for name recovery. This work presents a systematic survey to examine the significance, existing methodologies, implementations, and evaluations. Additionally, the study discusses insights gained from the systematization, highlights remaining challenges, and offers thoughts on future research directions.
{"title":"A Survey of Symbol Name Recovery in Software Reverse Engineering","authors":"Hongcheng Fan, Jielun Wu, Xincheng He, Yang Feng, Yibiao Yang, Baowen Xu, Qingkai Shi","doi":"10.1145/3844948","DOIUrl":"https://doi.org/10.1145/3844948","url":null,"abstract":"Reading the original semantics from symbol-stripped binaries, symbol-stripped bytecode, or symbol-stripped source code is challenging because high-level information, such as function and variable names, is unavailable. Recovering these names is crucial to understanding software behavior and enabling security applications. Previous work has shown that lost symbol names can be recovered, leading many researchers to propose approaches for name recovery. This work presents a systematic survey to examine the significance, existing methodologies, implementations, and evaluations. Additionally, the study discusses insights gained from the systematization, highlights remaining challenges, and offers thoughts on future research directions.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"17 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148860956","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}
Xiao Zhang, Nishaant Madhankumar, Deniz Acikbas, Rohit Raval, Juexing Wang, Zheng Song, Xin Xia, Xinyu Lei, Di Zhang
Intelligent Vehicular Networks (IVNs) serve as a core infrastructure for next-generation smart transportation, yet their large-scale deployment is severely hindered by insufficient trustworthiness, fragmented technologies, and difficult integration of multi-domain systems. Existing IVN surveys often lack a systematic taxonomy for trustworthy design and fail to comprehensively address practical challenges in V2X deployment and emerging 6G-enabled evolution. Most prior reviews overlook the joint optimization of cognition, communication, and computation layers, and rarely conduct a unified analysis of security, privacy, ethics, and trust issues across the full IVN pipeline. They also provide limited insights into real-world indoor and outdoor use cases and long-term developmental trends toward 2030. This survey proposes a three-layer hierarchical taxonomy of trustworthy IVNs, encompassing cognition, communication, and computation, to systematically organize and evaluate state-of-the-art technologies. We review sensing, communication, and computing in IVNs, while analyzing trustworthy risks and ethical dilemmas within each layer. We further validate practical IVN implementations through representative indoor and outdoor case studies and forecast key trends including 6G, AI-native networking, intelligent reflecting surfaces, integrated sensing and communication, and large language models. This survey provides a standardized analytical framework for researchers and offers actionable references for the secure, ethical, and trustworthy development and deployment of next-generation IVNs.
{"title":"Trustworthy Intelligent Vehicular Networks: A Survey","authors":"Xiao Zhang, Nishaant Madhankumar, Deniz Acikbas, Rohit Raval, Juexing Wang, Zheng Song, Xin Xia, Xinyu Lei, Di Zhang","doi":"10.1145/3844946","DOIUrl":"https://doi.org/10.1145/3844946","url":null,"abstract":"Intelligent Vehicular Networks (IVNs) serve as a core infrastructure for next-generation smart transportation, yet their large-scale deployment is severely hindered by insufficient trustworthiness, fragmented technologies, and difficult integration of multi-domain systems. Existing IVN surveys often lack a systematic taxonomy for trustworthy design and fail to comprehensively address practical challenges in V2X deployment and emerging 6G-enabled evolution. Most prior reviews overlook the joint optimization of cognition, communication, and computation layers, and rarely conduct a unified analysis of security, privacy, ethics, and trust issues across the full IVN pipeline. They also provide limited insights into real-world indoor and outdoor use cases and long-term developmental trends toward 2030. This survey proposes a three-layer hierarchical taxonomy of trustworthy IVNs, encompassing cognition, communication, and computation, to systematically organize and evaluate state-of-the-art technologies. We review sensing, communication, and computing in IVNs, while analyzing trustworthy risks and ethical dilemmas within each layer. We further validate practical IVN implementations through representative indoor and outdoor case studies and forecast key trends including 6G, AI-native networking, intelligent reflecting surfaces, integrated sensing and communication, and large language models. This survey provides a standardized analytical framework for researchers and offers actionable references for the secure, ethical, and trustworthy development and deployment of next-generation IVNs.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"74 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148860957","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}
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