2020 16th International Conference on Mobility, Sensing and Networking (MSN)最新文献_第5页

A Method to Construct Vulnerability Knowledge Graph based on Heterogeneous Data 基于异构数据的漏洞知识图构建方法

2020 16th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2020-12-01 DOI: 10.1109/MSN50589.2020.00126

Yizhen Sun, Dandan Lin, Hong Song, Minjia Yan, Linjing Cao

引用次数: 0

Analysing Ballistocardiography for Pervasive Healthcare 对普及医疗保健的心电图分析

2020 16th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2020-12-01 DOI: 10.1109/MSN50589.2020.00029

Roni Hytonen, Alison Tshala, J. Schreier, M. Holopainen, Aada Forsman, Minna Oksanen, R. Findling, Le Ngu Nguyen, S. Sigg, Nico Jähne-Raden

引用次数: 1

SASAK: Shrinking the Attack Surface for Android Kernel with Stricter “seccomp” Restrictions SASAK:通过更严格的“seccomp”限制来缩小Android内核的攻击面

2020 16th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2020-12-01 DOI: 10.1109/MSN50589.2020.00070

Yingjiao Niu, Lingguang Lei, Yuewu Wang, Jiang Chang, Shijie Jia, Chunjing Kou

{"title":"SASAK: Shrinking the Attack Surface for Android Kernel with Stricter “seccomp” Restrictions","authors":"Yingjiao Niu, Lingguang Lei, Yuewu Wang, Jiang Chang, Shijie Jia, Chunjing Kou","doi":"10.1109/MSN50589.2020.00070","DOIUrl":"https://doi.org/10.1109/MSN50589.2020.00070","url":null,"abstract":"The increasing vulnerabilities in Android kernel make it an attractive target to the attackers. Most kernel-targeted attacks are initiated through system calls. For security purpose, Google has introduced a Linux kernel security mechanism named “seccomp” since Android O to constrain the system calls accessible to the Android apps. Unfortunately, existing Android seccomp mechanism provides a fairly coarse-grained restriction by enforcing a unified seccomp policy containing more than 250 system calls for Android apps, which greatly reduces the effectiveness of seccomp. Also, it lacks an approach to profile the unnecessary system calls for a given Android app. In this paper we present a two-level control scheme named SASAK, which can shrink the attack surface of Android kernel by strictly constraining the system calls available to the Android apps with seccomp mechanism. First, instead of leveraging a unified seccomp policy for all Android apps, SASAK introduces an architecture- dedicated system call constraining by enforcing two separate and refined seccomp policies for the 32-bit Android apps and 64-bit Android apps, respectively. Second, we provide a tool to profile the necessary system calls for a given Android app and enforce an app-dedicated seccomp policy to further reduce the allowed system calls for the apps selected by the users. The app-dedicated control could dynamically change the seccomp policy for an app according to its actual needs. We implement a prototype of SASAK and the experiment results show that the architecture-dedicated constraining reduces 39.6% system calls for the 64-bit apps and 42.5% system calls for the 32-bit apps. 33% of the removed system calls for the 64-bit apps are vulnerable, and the number for the 32-bit apps is 18.8%. The app-dedicated restriction reduces about 66.9% and 62.5% system calls on average for the 64-bit apps and 32-bit apps, respectively. In addition, SASAK introduces negligible performance overhead.","PeriodicalId":447605,"journal":{"name":"2020 16th International Conference on Mobility, Sensing and Networking (MSN)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132627887","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

WSAD: An Unsupervised Web Session Anomaly Detection Method 一种无监督Web会话异常检测方法

2020 16th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2020-12-01 DOI: 10.1109/MSN50589.2020.00125

Yizhen Sun, Yiman Xie, Weiping Wang, Shigeng Zhang, Jun Gao, Yating Chen

{"title":"WSAD: An Unsupervised Web Session Anomaly Detection Method","authors":"Yizhen Sun, Yiman Xie, Weiping Wang, Shigeng Zhang, Jun Gao, Yating Chen","doi":"10.1109/MSN50589.2020.00125","DOIUrl":"https://doi.org/10.1109/MSN50589.2020.00125","url":null,"abstract":"servers in the Internet are vulnerable to Web attacks, to detect Web attacks, a commonly used method is to detect anomalies in the request parameters by making regular-expression-based matching rules for the parameters based on known security threats. However, such methods cannot detect unknown anomalies well and they can also be easily bypassed by using techniques like transcoding. Moreover, existing anomaly detection methods are usually based on a single HTTP request, which is easy to ignore the attack behavior within a period of time, such as brute-force password cracking attack. In this paper, we propose an unsupervised W eb S ession A nomaly D etection method called WSAD. WSAD uses ten features of web session to perform anomaly detection. After extracting the ten features, WSAD uses the DBSCAN algorithm to cluster the features of each session and outputs the outliers found in the clustering process as anomalies. We evaluate the performance of WSAD on several datasets from multiple real websites of a company. The results indicate that WSAD could detect malicious behaviors that could not be detected by Web Application Firewall, and it almost has no false positives.","PeriodicalId":447605,"journal":{"name":"2020 16th International Conference on Mobility, Sensing and Networking (MSN)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132822786","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

Dependency-Aware Dynamic Task Scheduling in Mobile-Edge Computing 基于依赖感知的移动边缘计算动态任务调度

2020 16th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2020-12-01 DOI: 10.1109/MSN50589.2020.00134

Mingzhi Wang, Tengyu Ma, Tao Wu, Chao Chang, F. Yang, Huaixi Wang

{"title":"Dependency-Aware Dynamic Task Scheduling in Mobile-Edge Computing","authors":"Mingzhi Wang, Tengyu Ma, Tao Wu, Chao Chang, F. Yang, Huaixi Wang","doi":"10.1109/MSN50589.2020.00134","DOIUrl":"https://doi.org/10.1109/MSN50589.2020.00134","url":null,"abstract":"With the popularity and development of the Internet of things (IoT), human life has been deeply affected. Because of the limitations of computation capability and battery capacity, it is difficult for IoT devices to support frequent and complex computing. Motivated by this challenge, many works attempt to upload tasks of IoT devices to the cloud center for computation. However, because of the limitation of distance and bandwidth, cloud computing is difficult to guarantee low latency. As a feasible solution, Mobile Edge Computing (MEC) has attracted more and more attention. Most existing works focus on the computation offloading strategy, while the task scheduling on edge servers is not studied in depth. The tasks uploaded by IoT devices are dynamic and random, and there are dependencies between these tasks. Therefore, it is difficult for edge servers to find a task scheduling scheme to minimize the task execution delay. In this paper, to solve the task scheduling problem of edge server in multi-server and multi-user MEC system, we propose a heuristic algorithm based on the following three scenarios: 1) Tasks uploaded by IoT devices is dynamic and uncertain. 2) There are dependencies between tasks. 3) The computation capability of the edge server is limited. Experimental results show that the proposed algorithm can significantly reduce the overall completion time of tasks and the average task execution delay in the edge server.","PeriodicalId":447605,"journal":{"name":"2020 16th International Conference on Mobility, Sensing and Networking (MSN)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122639663","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

Real-time and Accurate RFID Tag Localization based on Multiple Feature Fusion 基于多特征融合的RFID标签实时准确定位

2020 16th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2020-12-01 DOI: 10.1109/MSN50589.2020.00116

Shupo Fu, Shigeng Zhang, Danming Jiang, Xuan Liu

引用次数: 0

Panel: AI/ML for Mobility, Sensing, and Networking 小组讨论:面向移动、传感和网络的AI/ML

2020 16th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2020-12-01 DOI: 10.1109/msn50589.2020.00016

引用次数: 0

ECDT: Exploiting Correlation Diversity for Knowledge Transfer in Partial Domain Adaptation ECDT:利用相关多样性进行部分领域适应中的知识转移

2020 16th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2020-12-01 DOI: 10.1109/MSN50589.2020.00127

Shichang He, Xuan Liu, Xinning Chen, Ying Huang

{"title":"ECDT: Exploiting Correlation Diversity for Knowledge Transfer in Partial Domain Adaptation","authors":"Shichang He, Xuan Liu, Xinning Chen, Ying Huang","doi":"10.1109/MSN50589.2020.00127","DOIUrl":"https://doi.org/10.1109/MSN50589.2020.00127","url":null,"abstract":"Domain adaptation aims to transfer knowledge across different domains and bridge the gap between them. While traditional knowledge transfer considers identical domain, a more realistic scenario is to transfer from a larger and more diverse source domain to a smaller target domain, which is referred to as partial domain adaptation (PDA). However, matching the whole source domain to the target domain for PDA might produce negative transfer. Samples in the shared classes should be carefully selected to mitigate negative transfer in PDA. We observe that the correlations between different target domain samples and source domain samples are diverse: classes are not equally correlated and moreover, different samples have different correlation strengthes even when they are in the same class. In this study, we propose ECDT, a novel PDA method that Exploits the Correlation Diversity for knowledge Transfer between different domains. We propose a novel method to estimate target domain label space that utilizes the label distribution and feature distribution of target samples, based on which outlier source classes can be filtered out and their negative effects on transfer can be mitigated. Moreover, ECDT combines class-level correlation and instance-level correlation to quantity sample-level transferability in domain adversarial network. Experimental results on three commonly used cross-domain object data sets show that ECDT is superior to previous partial domain adaptation methods.","PeriodicalId":447605,"journal":{"name":"2020 16th International Conference on Mobility, Sensing and Networking (MSN)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131711989","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

Machine Learning Enabled Secure Collection of Phasor Data in Smart Power Grid Networks 机器学习支持智能电网相量数据的安全采集

2020 16th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2020-12-01 DOI: 10.1109/MSN50589.2020.00091

Wassila Lalouani, M. Younis

{"title":"Machine Learning Enabled Secure Collection of Phasor Data in Smart Power Grid Networks","authors":"Wassila Lalouani, M. Younis","doi":"10.1109/MSN50589.2020.00091","DOIUrl":"https://doi.org/10.1109/MSN50589.2020.00091","url":null,"abstract":"In a smart power grid, phasor measurement devices provide critical status updates in order to enable stabilization of the grid against fluctuations in power demands and component failures. Particularly the trend is to employ a large number of phasor measurement units (PMUs) that are inter-networked through wireless links. We tackle the vulnerability of such a wireless PMU network to message replay and false data injection (FDI) attacks. We propose a novel approach for avoiding explicit data transmission through PMU measurements prediction. Our methodology is based on applying advanced machine learning techniques to forecast what values will be reported and associate a level of confidence in such prediction. Instead of sending the actual measurements, the PMU sends the difference between actual and predicted values along with the confidence level. By applying the same technique at the grid control or data aggregation unit, our approach implicitly makes such a unit aware of the actual measurements and enables authentication of the source of the transmission. Our approach is data-driven and varies over time; thus it increases the PMU network resilience against message replay and FDI attempts since the adversary’s messages will violate the data prediction protocol. The effectiveness of approach is validated using datasets for the IEEE 14 and IEEE 39 bus systems and through security analysis.","PeriodicalId":447605,"journal":{"name":"2020 16th International Conference on Mobility, Sensing and Networking (MSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130367408","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

Scheduling mix-flow in SD-DCN based on Deep Reinforcement Learning with Private Link 基于私有链路深度强化学习的SD-DCN混合流调度

2020 16th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2020-12-01 DOI: 10.1109/MSN50589.2020.00071

Jinjie Lu, Waixi Liu, Yinghao Zhu, Sen Ling, Zhitao Chen, Jiaqi Zeng

{"title":"Scheduling mix-flow in SD-DCN based on Deep Reinforcement Learning with Private Link","authors":"Jinjie Lu, Waixi Liu, Yinghao Zhu, Sen Ling, Zhitao Chen, Jiaqi Zeng","doi":"10.1109/MSN50589.2020.00071","DOIUrl":"https://doi.org/10.1109/MSN50589.2020.00071","url":null,"abstract":"In software-defined datacenter networks, there are bandwidth-demanding elephant flows without deadline and delay-sensitive mice flows with strict deadline. They compete with each other for limited network resources, and how to effectively schedule such mix-flow is a huge challenge. We propose DRL-PLink (deep reinforcement learning with private link) that combines software-defined network and deep reinforcement learning (DRL) to schedule mix-flow. It divides the link bandwidth and establishes some corresponding private links for different types of flows respectively to isolate them. DRL is used to adaptively allocate bandwidth resources for these private links. Furthermore, DRL-PLink introduces Clipped Double Q-learning and parameter exploration NoisyNet technology to improve the scheduling policy for overestimated value estimates and action exploration problems in DRL. The simulation results show that DRL-PLink can effectively schedule mix-flow. Compared with ECMP and pFabric, the average flow completion time of DRL-PLink has decreased by 68.87% and 52.18% respectively. At the same time, it maintains a high deadline meet rate (>96.6%) close to pFabric and Karuna very much.","PeriodicalId":447605,"journal":{"name":"2020 16th International Conference on Mobility, Sensing and Networking (MSN)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115080074","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