2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)最新文献_第2页

Research on OTFS Systems for 6G 面向6G的OTFS系统研究

2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2022-12-01 DOI: 10.1109/TrustCom56396.2022.00213

Bei Li, Tian Xiao, K. Zhou, Lexi Xu, Guanghai Liu, Bor-Ming Wang, Jie Gao, Jian Guan, Zixiang Di

引用次数: 0

Study on Gateway Station Deployment for Large Scale LEO Satellite Constellation Networks 大规模LEO卫星星座网网关站部署研究

2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2022-12-01 DOI: 10.1109/trustcom56396.2022.00220

Lei Cheng, Shuaijun Liu, Lixaing Liu, Hailong Hu, Jingyi Chen, Xiandong Meng, Pengcheng Ding

引用次数: 0

Detecting Unknown Network Attacks with Attention Encoding and Deep Metric Learning 基于注意编码和深度度量学习的未知网络攻击检测

2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2022-12-01 DOI: 10.1109/TrustCom56396.2022.00047

Chunlan Fu, Shirong Han, Gang Shen

{"title":"Detecting Unknown Network Attacks with Attention Encoding and Deep Metric Learning","authors":"Chunlan Fu, Shirong Han, Gang Shen","doi":"10.1109/TrustCom56396.2022.00047","DOIUrl":"https://doi.org/10.1109/TrustCom56396.2022.00047","url":null,"abstract":"Emerging and evolving cybersecurity threats pose significant risks to the private data and assets of government, businesses, and individuals. The timely detection of unknown network attacks is a crucial defense measure to stop cybercrimes. However, the intricate organization and elaborate disguise make the previously unknown attacks hard to pinpoint. In this paper, we propose an approach with an attention encoding and deep metric learning model for intrusion detection. To handle the class-imbalance problem in the training data, we introduced a genetic algorithm-inspired data augmentation, applying the selection-crossover model to generate additional rare-class data. Using the class centers learned by the t-SNE algorithm for the online triplets, we reduced the randomness in the loss function calculation for the Triplet network. The self-attention and channel attention help to find the correlations between the samples and strengthen the mapping power of the low-dimensional metric space. To test the proposed detection system, we used NSL-KDD datasets for evaluation. Compared with the state-of-the-art methods in other research, our system presented a better performance for detecting unknown attacks, with an accuracy of 87% for multi-class classification, improving over 2.8%.","PeriodicalId":276379,"journal":{"name":"2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"302 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124536158","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

An Empirical Study Towards SAR Adversarial Examples SAR对抗实例的实证研究

2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2022-12-01 DOI: 10.1109/TrustCom56396.2022.00157

Zhiwei Zhang, Xunzhang Gao, Shuowei Liu, Yujia Diao

引用次数: 0

A Study on the Exoskeleton Motion Intent Recognition Algorithm for Embedded Calculation 嵌入式计算外骨骼运动意图识别算法研究

2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2022-12-01 DOI: 10.1109/TrustCom56396.2022.00150

Lei Shi, Peng Yin, Yang Ming, S. Qu, Z. Liu

{"title":"A Study on the Exoskeleton Motion Intent Recognition Algorithm for Embedded Calculation","authors":"Lei Shi, Peng Yin, Yang Ming, S. Qu, Z. Liu","doi":"10.1109/TrustCom56396.2022.00150","DOIUrl":"https://doi.org/10.1109/TrustCom56396.2022.00150","url":null,"abstract":"The exoskeleton robot to assist load-carrying has received much attention in recent years. It is a highly coupled human-machine system. In order to realize the compliant motion control target and complete the reliable power-assisted control for its wearer, it is necessary to accurately identify and predict the wearer's motion intention in real time. In this study, based on the foot pressure signal and the detected human motion information, the multi-sensor fusion method is used to complete the recognition of the wearer's motion intention. For the recognition of motion patterns, by comparing the recognition accuracy, resource consumption and real-time processing ability of various machine learning algorithms, the paper is finally determined that the support vector machine (SVM) is used to realize the action recognition for 8 daily motion patterns (Sit, Stand, Walk, Run, Ramp Ascent, Ramp Descent, Stairs Ascent and Stairs Descent), and the average recognition accuracy rate reaches 95%. For the prediction of motion phase and motion switching events, the neural-fuzzy inference method is used to complete the motion phase recognition and state switching event prediction. On the given test set, the accuracy of phase recognition is 99%, and the average absolute value of the deviation between the predicted state switching moment and the real value is around 61.6ms, which meets the requirements of exoskeleton compliance control for prediction time.","PeriodicalId":276379,"journal":{"name":"2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126109064","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

Optimal Block Propagation and Incentive Mechanism for Blockchain Networks in 6G 6G下区块链网络的最优块传播与激励机制

2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2022-12-01 DOI: 10.1109/TrustCom56396.2022.00058

Jinbo Wen, Xiaojun Liu, Zehui Xiong, Meng Shen, Siming Wang, Yutao Jiao, Jiawen Kang, He Li

{"title":"Optimal Block Propagation and Incentive Mechanism for Blockchain Networks in 6G","authors":"Jinbo Wen, Xiaojun Liu, Zehui Xiong, Meng Shen, Siming Wang, Yutao Jiao, Jiawen Kang, He Li","doi":"10.1109/TrustCom56396.2022.00058","DOIUrl":"https://doi.org/10.1109/TrustCom56396.2022.00058","url":null,"abstract":"Due to the prominent advantages of decentralization, transparency, security, and traceability, blockchain technologies have attracted ever-increasing attention from academia and industry, which can be applied to establish secure and reliable resource sharing platforms for future networks and applications. Especially, with the promising 6G technology which has large bandwidth and space-air-ground integrated coverage, blockchains have been evolved into 6G-enabled blockchain and envisioned to build various decentralized data and resource management systems. However, for 6G-enabled wireless blockchain networks, there still exist many challenges for their development and prosperity, e.g., large block propagation delay and propagation incentive. Therefore, this paper focuses on addressing the block propagation challenges. Firstly, inspired by epidemic models, we classify consensus nodes into five different states and establish a block propagation model for public blockchains that depicts block propagation laws. Then, considering consensus nodes are limited rational, we propose an Incentive Mechanism based on evolutionary game for Block Propagation (marked as BPIM) to minimize the block propagation delay. Numerical results demonstrate that compared with traditional routing algorithms, BPIM has better block propagation efficiency and greater incentive strength.","PeriodicalId":276379,"journal":{"name":"2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"198 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122274458","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

Smarkchain: An Amendable and Correctable Blockchain Based on Smart Markers Smarkchain:基于智能标记的可修改和可纠正的区块链

2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2022-12-01 DOI: 10.1109/TrustCom56396.2022.00116

Chin-Tser Huang, L. Njilla, Tieming Geng

{"title":"Smarkchain: An Amendable and Correctable Blockchain Based on Smart Markers","authors":"Chin-Tser Huang, L. Njilla, Tieming Geng","doi":"10.1109/TrustCom56396.2022.00116","DOIUrl":"https://doi.org/10.1109/TrustCom56396.2022.00116","url":null,"abstract":"Immutability is an important property of blockchain which ensures integrity and prevents forgery modification of previous transactions. However, immutability also prohibits the possibility of amending outdated codes and correcting typography or fraudulent data, both of which are common needs in many use cases. Therefore, a tradeoff that keeps the integrity guarantee but lifts the strict limitation of immutability is necessary to allow the practical applications of blockchain in areas other than cryptocurrencies. In this paper, we propose a novel scheme called Smarkchain, which will enhance blockchain technology with the features of amendment and correction by incorporating smart markers, an approach which enables multiway branching and merging in blockchain. Smarkchain has the unique advantages of allowing multiple consecutive blocks to be amended or corrected at one time, allowing multiple amendments or corrections over the same blocks, and not needing to modify existing blocks. Evaluation results of a prototype implementation show that our approach is practical.","PeriodicalId":276379,"journal":{"name":"2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132513194","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

Mal-Bert-GCN: Malware Detection by Combining Bert and GCN Mal-Bert-GCN:结合Bert和GCN的恶意软件检测

2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2022-12-01 DOI: 10.1109/TrustCom56396.2022.00034

Zhenquan Ding, Huixuan Xu, Yonghe Guo, Longchuan Yan, Lei Cui, Zhiyu Hao

{"title":"Mal-Bert-GCN: Malware Detection by Combining Bert and GCN","authors":"Zhenquan Ding, Huixuan Xu, Yonghe Guo, Longchuan Yan, Lei Cui, Zhiyu Hao","doi":"10.1109/TrustCom56396.2022.00034","DOIUrl":"https://doi.org/10.1109/TrustCom56396.2022.00034","url":null,"abstract":"With the dramatic increase in malicious software, the sophistication and innovation of malware have increased over the years. In particular, the dynamic analysis based on the deep neural network has shown high accuracy in malware detection. However, most of the existing methods only employ the raw API sequence feature, which cannot accurately reflect the actual behavior of malicious programs in detail. The relationship between API calls is critical for detecting suspicious behavior. Therefore, this paper proposes a malware detection method based on the graph neural network. We first connect the API sequences executed by different processes to build a directed process graph. Then, we apply Bert to encode the API sequences of each process into node embedding, which facilitates the semantic execution information inside the processes. Finally, we employ GCN to mine the deep semantic information based on the directed process graph and node embedding. In addition to presenting the design, we have implemented and evaluated our method on 10,000 malware and 10,000 benign software datasets. The results show that the precision and recall of our detection model reach 97.84% and 97.83%, verifying the effectiveness of our proposed method.","PeriodicalId":276379,"journal":{"name":"2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130088302","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

An Attribute-attack-proof Watermarking Technique for Relational Database 关系型数据库的抗属性攻击水印技术

2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2022-12-01 DOI: 10.1109/TrustCom56396.2022.00156

Shuguang Yuan, Chi Chen, Ke Yang, Tengfei Yang, J. Yu

{"title":"An Attribute-attack-proof Watermarking Technique for Relational Database","authors":"Shuguang Yuan, Chi Chen, Ke Yang, Tengfei Yang, J. Yu","doi":"10.1109/TrustCom56396.2022.00156","DOIUrl":"https://doi.org/10.1109/TrustCom56396.2022.00156","url":null,"abstract":"Proving ownership rights on relational databases is an important issue. The robust watermarking technique could claim ownership by insertion information about the data owner. Hence, it is vital to improving the robustness of watermarking technique in that intruders could launch types of attacks to corrupt the inserted watermark. Furthermore, attributes are explicit and operable objectives to destroy the watermark. To my knowledge, there does not exist a comprehensive solution to resist attribute attack. In this paper, we propose a robust watermarking technique that is robust against subset and attribute attacks. The novelties lie in several points: applying the classifier to reorder watermarked attributes, designing a secret sharing mechanism to duplicate watermark independently on each attribute, and proposing twice majority voting to correct errors caused by attacks for improving the accuracy of watermark detection. In addition, our technique has features of blind, key-based, incrementally updatable, and low false hit rate. Experiments show that our algorithm is robust against subset and attribute attacks compared with AHK, DEW, and KSF algorithms. Moreover, it is efficient with running time in both insertion and detection phases.","PeriodicalId":276379,"journal":{"name":"2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130292534","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

Understanding the Penetration Test Workflow: a security test with Tramonto in an e-Government application 理解渗透测试工作流程:在电子政务应用程序中使用Tramonto进行安全测试

2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2022-12-01 DOI: 10.1109/TrustCom56396.2022.00229

Daniel Dalalana Bertoglio, Luis G. B. Schüler, A. Zorzo, R. C. Lunardi

{"title":"Understanding the Penetration Test Workflow: a security test with Tramonto in an e-Government application","authors":"Daniel Dalalana Bertoglio, Luis G. B. Schüler, A. Zorzo, R. C. Lunardi","doi":"10.1109/TrustCom56396.2022.00229","DOIUrl":"https://doi.org/10.1109/TrustCom56396.2022.00229","url":null,"abstract":"Security and privacy became vital to any of the current computational systems or applications. Particularly, investigating possible security issues - to mitigate possible data leaks or tampering - is an important step in the current software development. Currently, penetration tests (pentest) are performed to detect possible system flaws and to prevent/correct eventual security issues. However pentesting (the act of performing pentest) a system can be complex and hard to control. There are many activities throughout the pentesting process and it is common to find difficulties in controlling them. At the same time, it is not easy to determine precisely what activities will be performed, since each tester can follow a specific methodology or even use their own testing model. Based on the main security assessment test methodologies, we created a framework for penetration testing that aims to improve the test workflow in terms of management, organization, standardization, and flexibility. This framework is called Tramonto. This paper presents and discusses a pentest case study performed by a security company using the Tramonto framework. To present this case, we introduce the Tramonto-App, a software that was implemented using the definitions present in the Tramonto framework. Tramonto-App was designed to assist testers in penetration tests based on features that help to organize scripts, handle the testing workflow, and generate reports. As a result, the Tramonto-App resulted in a reduced number of pentesting problems and reduced (human) effort required to perform the penetration, allowing the tester to improve the quality of the Pentest.","PeriodicalId":276379,"journal":{"name":"2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134021335","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