2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)最新文献_第3页

Energy-efficient and Traffic-aware VNF Placement for Vertical Services in 5G Networks 5G网络垂直业务的节能和流量感知VNF布局

2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2022-12-01 DOI: 10.1109/TrustCom56396.2022.00184

Yi Yue, W. Yang, Xiao Liang, Xihuizi Meng, Rong Huang, Xiongyan Tang

{"title":"Energy-efficient and Traffic-aware VNF Placement for Vertical Services in 5G Networks","authors":"Yi Yue, W. Yang, Xiao Liang, Xihuizi Meng, Rong Huang, Xiongyan Tang","doi":"10.1109/TrustCom56396.2022.00184","DOIUrl":"https://doi.org/10.1109/TrustCom56396.2022.00184","url":null,"abstract":"Enabled by Network Function Virtualization (NFV) and Software-Defined Networks (SDN), 5G networks benefit various industries (the so-called verticals) by supporting their technological and business needs flexibly and swiftly. However, a critical challenge is making high-quality joint optimal decisions for vertical demand mapping, involving Virtual Network Function (VNF) placement and optimization of network resources. In particular, to devise VNF placement schemes, network operators need to consider different objectives, such as minimizing operational costs or network latency, which are optimization objectives traditionally addressed separately. This paper studies the VNF placement for service function chains to minimize energy and traffic costs jointly. First, the problem is formulated as an optimization problem. Then we propose a joint optimization function to measure the energy consumption of physical nodes and traffic cost on links. Then, we improve the biogeography-based evolutionary algorithm to solve the proposed problem. Simulation results show that our method is effective for the proposed problem and outperforms existing methods in terms of performance.","PeriodicalId":276379,"journal":{"name":"2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"28 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":"133867238","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

A Blockchain-assisted Collaborative Ensemble Learning for Network Intrusion Detection 基于区块链的网络入侵检测协同集成学习

2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2022-12-01 DOI: 10.1109/TrustCom56396.2022.00142

Lijian Liu, Jinguo Li

{"title":"A Blockchain-assisted Collaborative Ensemble Learning for Network Intrusion Detection","authors":"Lijian Liu, Jinguo Li","doi":"10.1109/TrustCom56396.2022.00142","DOIUrl":"https://doi.org/10.1109/TrustCom56396.2022.00142","url":null,"abstract":"With the rapid growth of the Internet network, cyber attacks (mainly DDOS, U2R, Probe, Infiltration and Heart-bleed etc) on networks and computer systems have also increased expeditiously. Intrusion detection system has proven to be one of the most effective methods to resist cyber attacks. Most traditional machine learning methods can only learn shallow features of the data, so they are weak in detecting complex data. Recently, ensemble learning has begun to be applied in network intrusion detection due to their excellent generalization ability. However, most of them are designed based on machine learning methods. They usually do not have multiple hidden layers, which will lead to low detection accuracy. Furthermore, when the base learners in ensemble learning make voting decisions, the data can be easily tampered, which incurs wrong detection results. To solve the above problems, we propose an intrusion detection method based on blockchain and collaborative ensemble learning. In detail, to address the low detection rate of machine learning model, we design an ensemble deep learning approach combining with a weighted dynamic voting mechanism, which can enhance base learners with excellent performance and weaken base learners with poor performance. To solve the problem of data tampering during individual model voting decisions, we explore blockchain to verify the detection results of the individual model and the final results of the voting. Finally, we evaluate the performance of the proposed system on the CICIDS-2017 dataset. The experimental results demonstrate the accuracy and effectiveness of our proposed system.","PeriodicalId":276379,"journal":{"name":"2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"95 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":"134381466","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

Security Support on Memory Controller for Heap Memory Safety 内存控制器对堆内存安全的安全支持

2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2022-12-01 DOI: 10.1109/TrustCom56396.2022.00043

Chao Zhang, Rui Hou

{"title":"Security Support on Memory Controller for Heap Memory Safety","authors":"Chao Zhang, Rui Hou","doi":"10.1109/TrustCom56396.2022.00043","DOIUrl":"https://doi.org/10.1109/TrustCom56396.2022.00043","url":null,"abstract":"Memory corruption attacks have existed for multiple decades, and have become a major threat to computer systems. At the same time, a number of defense techniques have been proposed by research community. With the wide adoption of CPU-based memory safety solutions, sophisticated attackers tend to tamper with system memory via direct memory access (DMA) attackers, which leverage DMA-enabled I/O peripherals to fully compromise system memory. The Input-Output Memory Management Units (IOMMUs) based solutions are widely believed to mitigate DMA attacks. However, recent works point out that attackers can bypass IOMMU-based protections by manipulating the DMA interfaces, which are particularly vulnerable to race conditions and other unsafe interactions.State-of-the-art hardware-supported memory protections rely on metadata to perform security checks on memory access. Consequently, the additional memory request for metadata results in significant performance degradation, which limited their feasibility in real world deployments. For quantitative analysis, we separate the total metadata access latency into DRAM latency, on-chip latency, and cache latency, and observe that the actual DRAM access is less than half of the total latency. To minimize metadata access latency, we propose EMC, a low-overhead heap memory safety solution that implements a tripwire based mechanism on the memory controller. In addition, by using memory controller as a natural gateway of various memory access data paths, EMC could provide comprehensive memory safety enforcement to all memory data paths from/to system physical memory. Our evaluation shows an 0.54% performance overhead on average for SPEC 2017 workloads.","PeriodicalId":276379,"journal":{"name":"2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"18 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":"132811923","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

Research on Intelligent 5G Remote Interference Avoidance and Clustering Scheme 智能5G远程干扰避免与聚类方案研究

2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2022-12-01 DOI: 10.1109/TrustCom56396.2022.00212

Tian Xiao, Bei Li, Zixiang Di, Guanghai Liu, Lexi Xu, Jian Guan, Zhaoning Wang, Chen Cheng, Yi Li

引用次数: 0

"Comments Matter and The More The Better!": Improving Rumor Detection with User Comments “评论很重要，而且越多越好!”:利用用户评论改进谣言检测

2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2022-12-01 DOI: 10.1109/TrustCom56396.2022.00060

Yang Xu, Jie Guo, Weidong Qiu, Zheng Huang, Enes ALTUNCU, Shujun Li

{"title":"\"Comments Matter and The More The Better!\": Improving Rumor Detection with User Comments","authors":"Yang Xu, Jie Guo, Weidong Qiu, Zheng Huang, Enes ALTUNCU, Shujun Li","doi":"10.1109/TrustCom56396.2022.00060","DOIUrl":"https://doi.org/10.1109/TrustCom56396.2022.00060","url":null,"abstract":"While many online platforms bring great benefits to their users by allowing user-generated content, they have also facilitated generation and spreading of harmful content such as rumors. Researcher have proposed different rumor detection methods based on features extracted from the original post and/or associated comments, but how comments affect the performance of such methods remains largely less understood. In this paper, we first propose a new BERT-based rumor detection method that can outperform other state-of-the-art methods, and then used it to study the role of comments in rumor detection. Our proposed method concatenates the original post and associated comments to form a single long text, which is then segmented into shorter chunks more suitable for BERT-based vectorization. Features extracted from all trunks are fed into a classifier based on an LSTM network or a transformer layer for the classification task. The experimental results on the PHEME and Ma-Weibo datasets proved the superior performance of our method. We conducted additional experiments on different settings of our proposed method to study different aspects of the role comments play in the rumor detection task. These additional experiments led to some very interesting findings, including the surprising result that fixed-length segmentation is better than natural segmentation, and the observation that including more comments can help improve the rumor detector’s performance. Some of these findings have profound operational implications for online platforms, e.g., commentators can contribute to rumor detection positively so online platforms can leverage the crowd intelligence to detect online rumors more effectively without applying overstrict content consensus policies.","PeriodicalId":276379,"journal":{"name":"2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"03 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":"131050471","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

Telecom Big Data assisted Algorithm and System of Campus Safety Management 电信大数据辅助校园安全管理算法与系统

2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2022-12-01 DOI: 10.1109/TrustCom56396.2022.00223

Chen Cheng, Xinzhou Cheng, Shikun Jiang, Xin Zhao, Yuhui Han, Zhang Tao, Lijuan Cao, Yuwei Jia, Tian Xiao, Bei Li

引用次数: 0

Source Code Vulnerability Detection Using Vulnerability Dependency Representation Graph 使用漏洞依赖表示图的源代码漏洞检测

2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2022-12-01 DOI: 10.1109/TrustCom56396.2022.00070

Hongyu Yang, Haiyun Yang, Liang Zhang, Xiang Cheng

{"title":"Source Code Vulnerability Detection Using Vulnerability Dependency Representation Graph","authors":"Hongyu Yang, Haiyun Yang, Liang Zhang, Xiang Cheng","doi":"10.1109/TrustCom56396.2022.00070","DOIUrl":"https://doi.org/10.1109/TrustCom56396.2022.00070","url":null,"abstract":"Aiming at the fact that the existing source code vulnerability detection methods did not explicitly maintain the semantic information related to the vulnerability in the source code, which made it difficult for the vulnerability detection model to extract the vulnerability sentence features and had a high detection false positive rate, a source code vulnerability detection method based on the vulnerability dependency graph is proposed. Firstly, the candidate vulnerability sentences of the function were matched, and the vulnerability dependency representation graph corresponding to the function was generated by analyzing the multi-layer control dependencies and data dependencies of the candidate vulnerability sentences. Secondly, abstracted the function name and variable name of the code sentences node and generated the initial representation vector of the code sentence nodes in the vulnerability dependency representation graph. Finally, the source code vulnerability detection model based on the heterogeneous graph transformer was used to learn the context information of the code sentence nodes in the vulnerability dependency representation graph. In this paper, the proposed method was verified on three datasets. The experimental results show that the proposed method have better performance in source code vulnerability detection, and the recall rate is increased by 1.50%~22.27%, and the F1 score is increased by 1.86%~16.69%, which is better than the existing methods.","PeriodicalId":276379,"journal":{"name":"2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"83 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":"133349560","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

Near Field Air-Gap Covert Channel Attack 近场气隙隐蔽通道攻击

2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2022-12-01 DOI: 10.1109/TrustCom56396.2022.00074

Mordechai Guri

引用次数: 1

A Dynamic Transaction Pattern Aggregation Neural Network for Money Laundering Detection 一种用于洗钱检测的动态交易模式聚合神经网络

2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2022-12-01 DOI: 10.1109/TrustCom56396.2022.00114

Xuejiao Luo, Xiaohui Han, Wenbo Zuo, Zhengyuan Xu, Zhiwen Wang, Xiaoming Wu

{"title":"A Dynamic Transaction Pattern Aggregation Neural Network for Money Laundering Detection","authors":"Xuejiao Luo, Xiaohui Han, Wenbo Zuo, Zhengyuan Xu, Zhiwen Wang, Xiaoming Wu","doi":"10.1109/TrustCom56396.2022.00114","DOIUrl":"https://doi.org/10.1109/TrustCom56396.2022.00114","url":null,"abstract":"Money laundering is a significant problem in the financial system and provides the conditions for financing various crimes. Previous methods apply many flexible algorithms, such as machine learning, graph mining, and anomaly detection. However, most of these contemporary methods do not adequately consider the dynamic characteristics of transactions, which may contain discriminative information for money laundering detection. To address this issue, in this paper, we propose a dynamic transaction pattern aggregation neural network (DTPAN) for money laundering detection. DTPAN utilizes two feature extractors to learn the dynamic features of transaction behaviors and the evolution of transfer relationships between accounts. Furthermore, it employs a feature enhancement module to enhance the behavior dynamic features, capturing the latent dependency between behavior dynamic and relationship evolution. Experimental results obtained with a real-world dataset demonstrate the effectiveness of DTPAN. The results also reveal that DTPAN can enhance the performance of ML detection by adequately exploring the dynamic information of transactions.","PeriodicalId":276379,"journal":{"name":"2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"39 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":"128820538","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

Battery Aging-Robust Driving Range Prediction of Electric Bus 电池老化——电动客车续驶里程稳健预测

2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2022-12-01 DOI: 10.1109/TrustCom56396.2022.00163

Heng Li, Zhijun Liu, Yongting Liu, Hui Peng, Rui Zhang, Jun Peng, Zhiwu Huang

{"title":"Battery Aging-Robust Driving Range Prediction of Electric Bus","authors":"Heng Li, Zhijun Liu, Yongting Liu, Hui Peng, Rui Zhang, Jun Peng, Zhiwu Huang","doi":"10.1109/TrustCom56396.2022.00163","DOIUrl":"https://doi.org/10.1109/TrustCom56396.2022.00163","url":null,"abstract":"The prediction of driving range is very important for electric bus, but there is usually a difficulty: battery aging affects the accuracy of driving range prediction. In order to solve this problem, this paper proposes a driving range prediction method for electric bus, which is robust to the battery aging effect. Firstly, we extract the features that affect the driving range from the real-world dataset, quantify the correlation between them and the driving range by grey correlation analysis. Then through the feature enhancement technology, the time window processing is used to mitigate the influence of battery aging, and the time information hidden in the historical period sequence is deeply excavated. On this basis, we establish the driving range prediction model based on k-nearest neighbors regression, where the key parameters are optimized with the particle swarm optimization algorithm. Numerous experimental results show that compared with the classical methods, the method proposed in this paper has higher prediction accuracy especially when the batteries undergo significant aging effects.","PeriodicalId":276379,"journal":{"name":"2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"79 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":"115527372","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