2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)最新文献

A Chinese Multi-modal Relation Extraction Model for Internet Security of Finance 面向互联网金融安全的中文多模态关系抽取模型

2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W) Pub Date : 2022-06-01 DOI: 10.1109/dsn-w54100.2022.00029

Qinghan Lai, Shuai Ding, J. Gong, Jin'an Cui, Song Liu

{"title":"A Chinese Multi-modal Relation Extraction Model for Internet Security of Finance","authors":"Qinghan Lai, Shuai Ding, J. Gong, Jin'an Cui, Song Liu","doi":"10.1109/dsn-w54100.2022.00029","DOIUrl":"https://doi.org/10.1109/dsn-w54100.2022.00029","url":null,"abstract":"As the base of the whole economy and society, internet security of finance directly affects the overall development of the country. With the development of the Internet, it is essential to effectively extract the relation between financial entities from internet financial intelligence and build a financial security knowledge graph, which lays the foundation for monitoring of internet security of finance. For relation extraction of Chinese internet financial intelligence, the existing models are all based on single-modal text semantics ignoring the role of Chinese pictographic semantics, while the shape and structure of Chinese characters contains useful semantics. In addition, the pictographic semantic fusion method of Chinese text also needs to be improved for better performance. To solve these shortcomings, we propose a Chinese Multimodal Relation Extraction model (CMRE), which improves the relation extraction ability on the Chinese internet financial intelligence. In CMRE, we extract pictographic semantics based on Chinese character shape and structure. Furthermore, we design a novel multi-modal semantic fusion module based on improved Transformer to effectively fuse the text and pictographic semantics. Additionally, we design experiments on the Chinese literature dataset(Sanwen) to test the relation extraction capability of CMRE. Finally, we employ CMRE to extract relations between financial entities on the internet financial intelligence dataset(FinRE) to compare with other baseline models.","PeriodicalId":349937,"journal":{"name":"2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129853356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Network Message Field Type Clustering for Reverse Engineering of Unknown Binary Protocols 未知二进制协议逆向工程的网络消息字段类型聚类

2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W) Pub Date : 2022-06-01 DOI: 10.1109/DSN-W54100.2022.00023

Stephan Kleber, F. Kargl, Milan State, M. Hollick

{"title":"Network Message Field Type Clustering for Reverse Engineering of Unknown Binary Protocols","authors":"Stephan Kleber, F. Kargl, Milan State, M. Hollick","doi":"10.1109/DSN-W54100.2022.00023","DOIUrl":"https://doi.org/10.1109/DSN-W54100.2022.00023","url":null,"abstract":"Reverse engineering of unknown network protocols based on recorded traffic traces enables security analyses and debugging of undocumented network services. One important step in protocol reverse engineering is to determine data types of message fields. Existing approaches for binary protocols (1) lack comprehensive methods to interpret message content and determine the data types of discovered segments in a message and (2) assume the availability of context, which prevents the analysis of complex and lower-layer protocols. Overcoming these limitations, we propose the first generic method to analyze message field data types in unknown binary protocols by clustering of segments with the same data type. Our extensive evaluation shows that our method in most cases provides clustering of up to 100 % precision at reasonable recall. Particularly relevant for use in fuzzing and misbehavior detection, we increase the coverage of message bytes over the state-of-the-art to 87 % by almost a factor of 30. We provide an open-source implementation to allow follow-up works.","PeriodicalId":349937,"journal":{"name":"2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130159970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Repairing Security Vulnerabilities Using Pre-trained Programming Language Models 使用预训练的编程语言模型修复安全漏洞

2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W) Pub Date : 2022-06-01 DOI: 10.1109/dsn-w54100.2022.00027

Kai Huang, Su Yang, Hongyu Sun, Chengyi Sun, Xuejun Li, Yuqing Zhang

{"title":"Repairing Security Vulnerabilities Using Pre-trained Programming Language Models","authors":"Kai Huang, Su Yang, Hongyu Sun, Chengyi Sun, Xuejun Li, Yuqing Zhang","doi":"10.1109/dsn-w54100.2022.00027","DOIUrl":"https://doi.org/10.1109/dsn-w54100.2022.00027","url":null,"abstract":"Repairing software bugs with automated solutions is a long-standing goal of researchers. Some of the latest automated program repair (APR) tools leverage natural language processing (NLP) techniques to repair software bugs. But natural languages (NL) and programming languages (PL) have significant differences, which leads to the fact that they may not be able to handle PL tasks well. Moreover, due to the difference between the vulnerability repair task and bug repair task, the performance of these tools on vulnerability repair is not yet known. To address these issues, we attempt to use large-scale pre-trained PL models (CodeBERT and GraphCodeBERT) for the vulnerability repair task based on the characteristics of PL and explore the real-world performance of the state-of-the-art data-driven approaches for vulnerability repair. The results show that using pre-trained PL models can better capture and process PL features and accomplish multi-line vulnerability repair. Specifically, our solution achieves advanced results (single-line repair accuracy 95.47%, multi-line repair accuracy 90.06%). These results outperform the state-of-the-art data-driven approaches and demonstrate that adding rich data-dependent features can help solve more complex code repair problems. Besides, we also discuss the previous work and our approach, pointing out some shortcomings and solutions we can work on in the future.","PeriodicalId":349937,"journal":{"name":"2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121899698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Alternative Route-Based Attacks in Metropolitan Traffic Systems 大都市交通系统中基于替代路由的攻击

2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W) Pub Date : 2022-06-01 DOI: 10.1109/dsn-w54100.2022.00014

Sidney La Fontaine, Naveen Muralidhar, Michael Clifford, T. Eliassi-Rad, C. Nita-Rotaru

{"title":"Alternative Route-Based Attacks in Metropolitan Traffic Systems","authors":"Sidney La Fontaine, Naveen Muralidhar, Michael Clifford, T. Eliassi-Rad, C. Nita-Rotaru","doi":"10.1109/dsn-w54100.2022.00014","DOIUrl":"https://doi.org/10.1109/dsn-w54100.2022.00014","url":null,"abstract":"With the growing reliance on driving direction applications that dynamically account for live traffic updates, drivers are much more likely to act optimally, by taking the shortest path to their destination, and therefore more predictably. As city networks transition into being made up of connected and autonomous vehicles, autonomous driving pilots are even more likely to act optimally and predictably. The predictability that comes from acting optimally allows motivated attackers to manipulate driver(s) to travel chosen slower alternative routes by causing disruptions on road segments that are part of faster routes. A motivated attacker could use this method to cause a number of different harms such as forcing specific vehicles to take unnecessarily long routes, forcing all vehicles traveling between popular locations to follow a chosen route, or making vehicles travel specific road segments that the attacker chose, such as toll roads. In this work, we show the feasibility and practicality of conducting such attacks on several real traffic networks of major North American cities. We analyze several attack objectives under different attacker constraints and we demonstrated that an attacker could find an attack strategy in a matter of seconds.","PeriodicalId":349937,"journal":{"name":"2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133370748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

General Probability in Coq Coq的一般概率

2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W) Pub Date : 2022-06-01 DOI: 10.1109/dsn-w54100.2022.00021

Avraham Shinnar, B. Trager

引用次数: 0

Message from the DCDS 2022 Organizers 消息从民国2022组织者

2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W) Pub Date : 2022-06-01 DOI: 10.1109/dsn-w54100.2022.00009

引用次数: 0

A Comprehensive Dynamic Quality Assessment Method for Cyber Threat Intelligence 网络威胁情报综合动态质量评估方法

2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W) Pub Date : 2022-06-01 DOI: 10.1109/dsn-w54100.2022.00037

Menghan Wang, Libin Yang, W. Lou

引用次数: 0

Federated Learning with Anomaly Client Detection and Decentralized Parameter Aggregation 基于异常客户端检测和分散参数聚合的联邦学习

2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W) Pub Date : 2022-06-01 DOI: 10.1109/dsn-w54100.2022.00016

Shu Liu, Yanlei Shang

{"title":"Federated Learning with Anomaly Client Detection and Decentralized Parameter Aggregation","authors":"Shu Liu, Yanlei Shang","doi":"10.1109/dsn-w54100.2022.00016","DOIUrl":"https://doi.org/10.1109/dsn-w54100.2022.00016","url":null,"abstract":"Federated learning is a framework for machine learning that is dedicated to data privacy protection. In federated learning, system cannot fully control the behavior of clients which can be faulty. These behaviors include sharing arbitrary faulty gradients and delaying the process of sharing due to Byzantine attacks or clients’ own software and hardware failures. In federated learning, the parameter server may also be faulty during gradient collection and aggregation, mainly including gradient-based training data inference and model parameter faulty update. The above problems may lead to reduced accuracy of federated learning model training, leakage of client privacy, etc. Existing research enhances the robustness of federated learning by exploiting the decentralization and immutability of Blockchain. For untrusted clients, most research is based on Byzantine fault tolerance to defend against clients indiscriminately, and may cause model accuracy reduction. In addition, most of the research focus on unencrypted gradients, and there is insufficient research on dealing with client anomalies in the case of gradient encryption. For untrusted parameter servers, existing research has problems in energy overhead and scalability. Aiming at the problems above, this paper studies the robustness of federated learning, and proposes a blockchain-based federated learning parameter update architecture PUS-FL. Through experiments simulating distributed machine learning on neural networks, we demonstrate that the anomaly detection algorithm of PUS-FL outperforms conventional gradient filters including geometric median, Multi-Krum and trimmed mean. In addition, our experiments also verify that the scalability-enhanced parameter aggregation consensus algorithm proposed in this paper(SE-PBFT) improves consensus scalability by reducing communication complexity.","PeriodicalId":349937,"journal":{"name":"2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130310898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

DTC: A Dynamic Trusted Collaboration Architecture for Mobile Edge Computing DTC:移动边缘计算动态可信协作架构

2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W) Pub Date : 2022-06-01 DOI: 10.1109/dsn-w54100.2022.00038

Ruizhong Du, Yan Gao

{"title":"DTC: A Dynamic Trusted Collaboration Architecture for Mobile Edge Computing","authors":"Ruizhong Du, Yan Gao","doi":"10.1109/dsn-w54100.2022.00038","DOIUrl":"https://doi.org/10.1109/dsn-w54100.2022.00038","url":null,"abstract":"Under mobile edge computing (MEC), collaborative computing is a new paradigm to optimize edge network load and server resource allocation. However, there are still issues such as resource imbalance and unreliable identity when collaborative nodes provide computing and caching services for computing devices. Therefore, an efficient and reliable MEC network should be established to manage these challenges. In this paper, a dynamic trusted collaboration scheme is proposed. Specifically, the transaction supervision of nodes in the MEC scenario is first realized through blockchain-based mobile edge computing (BMEC) technology. In BMEC, a management scheme is established laying the foundation for building a trusted MEC environment. Second, a collaborative model is designed. On this basis, it is formulated as an optimization problem based on computing, communication, caching, and energy consumption. The formulation problem is verified to be NP complete. Thus, an improved heuristic algorithm is adopted in this study to solve this problem. Ultimately, the experimental results demonstrate that DTC not only has good performance but also can effectively enhance the reliability of the MEC network compared with other cooperative schemes.","PeriodicalId":349937,"journal":{"name":"2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115531165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Blockchain-Based Incentive and Arbitrable Data Auditing Scheme 基于区块链的激励和可仲裁数据审计方案

2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W) Pub Date : 2022-06-01 DOI: 10.1109/dsn-w54100.2022.00036

Junfeng Tian, Qi Song, Haoning Wang

引用次数: 0