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2021 IEEE Symposium on Computers and Communications (ISCC)最新文献

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GSketch: A Comprehensive Graph Analytic Approach for Masquerader Detection Based on File Access Graph 基于文件访问图的伪装检测综合图分析方法
2021 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2021-09-05 DOI: 10.1109/ISCC53001.2021.9631465
Jianguo Jiang, Xu Wang, Yan Wang, Qiujian Lv, Meichen Liu, Tingting Wang, Leiqi Wang
{"title":"GSketch: A Comprehensive Graph Analytic Approach for Masquerader Detection Based on File Access Graph","authors":"Jianguo Jiang, Xu Wang, Yan Wang, Qiujian Lv, Meichen Liu, Tingting Wang, Leiqi Wang","doi":"10.1109/ISCC53001.2021.9631465","DOIUrl":"https://doi.org/10.1109/ISCC53001.2021.9631465","url":null,"abstract":"Masqueraders are a severe insider threat and have become a conventional security issue for most organizations. The majority of existing techniques for detecting masqueraders extract statistical features from file access logs. However, the graph's features from these logs have not been fully explored. In this work, we introduce GSketch. First, it divides each user's file access logs into equal length, non-overlapping time windows. Then file access logs on each time window are transformed into a graph according to chronological order. GSketch extracts global features and local features from the graph. Global features provide a panoramic view of the graph, and local features mine small, induced sub-graphs. Finally, GSketch applies an abnormal detection algorithm to find anomalous points in the feature space and marks these points as masquerader's activities. The effectiveness of GSketch is demonstrated by its excellent performances on two public datasets - WUIL and TWOS.","PeriodicalId":270786,"journal":{"name":"2021 IEEE Symposium on Computers and Communications (ISCC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115790440","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
Improving a Smart Environment with Wireless Network User Load Prediction 利用无线网络用户负荷预测改进智能环境
2021 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2021-09-05 DOI: 10.1109/ISCC53001.2021.9631441
L. R. Frank, Roberto M. Oliveira, A. Vieira, E. F. Silva
{"title":"Improving a Smart Environment with Wireless Network User Load Prediction","authors":"L. R. Frank, Roberto M. Oliveira, A. Vieira, E. F. Silva","doi":"10.1109/ISCC53001.2021.9631441","DOIUrl":"https://doi.org/10.1109/ISCC53001.2021.9631441","url":null,"abstract":"Over the years, wireless networks have been suffering a significant increase in the number of connected users, and dealing with this increase is extremely important in terms of economy and quality of service. In this work, a prediction model is proposed to improve this relationship, focusing on predicting the number of connected users to the wireless network. Our model consists of a particle swarm optimizer applied to the parameters of the Multilayer Perceptron neural network. The model was evaluated with real mobility data obtained from wireless networks with a total of more than twenty thousand users. The predictions made by the model allow allocating the network bandwidth efficiently, generating savings in the available resources. In fact, the simulated results indicate an average coefficient of determination of 94.08% and average savings of 67.31 % of the total available bandwidth.","PeriodicalId":270786,"journal":{"name":"2021 IEEE Symposium on Computers and Communications (ISCC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126944611","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
On the Data Analysis of Participatory Air Pollution Monitoring Using Low-cost Sensors 参与式低成本空气污染监测数据分析
2021 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2021-09-05 DOI: 10.1109/ISCC53001.2021.9631547
M. Fekih, Walid Bechkit, H. Rivano
{"title":"On the Data Analysis of Participatory Air Pollution Monitoring Using Low-cost Sensors","authors":"M. Fekih, Walid Bechkit, H. Rivano","doi":"10.1109/ISCC53001.2021.9631547","DOIUrl":"https://doi.org/10.1109/ISCC53001.2021.9631547","url":null,"abstract":"Participatory sensing leverages population density and involves citizens in the collection of extensive data in multiple fields such as air pollution monitoring, enabling large-scale deployments and improving the knowledge of air quality. This study highlights the potential of low-cost sensors through a data analysis of pollutant concentrations collected during multiple sensing campaigns we co-organized using a participatory sensing platform we designed. We first compare the estimation quality of four statistical models and investigate the impact of sampling frequency on the quality of estimation and energy consumption of the nodes using an energy model based on the sensing duty cycle. In addition, we evaluate the capacity of regression models to recover missing data of one sensor based on the other sensors. Results are satisfactory and reveal that a small decrease in the sampling frequency slightly reduces the estimation quality, but in contrast, allows the nodes to operate on a longer period.","PeriodicalId":270786,"journal":{"name":"2021 IEEE Symposium on Computers and Communications (ISCC)","volume":"12 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126102562","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
GOFS: Geo-distributed Scheduling in OpenFaaS GOFS: OpenFaaS中的地理分布式调度
2021 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2021-09-05 DOI: 10.1109/ISCC53001.2021.9631492
Fabiana Rossi, Simone Falvo, V. Cardellini
{"title":"GOFS: Geo-distributed Scheduling in OpenFaaS","authors":"Fabiana Rossi, Simone Falvo, V. Cardellini","doi":"10.1109/ISCC53001.2021.9631492","DOIUrl":"https://doi.org/10.1109/ISCC53001.2021.9631492","url":null,"abstract":"OpenFaaS is a popular open-source serverless platform in the academic and industrial world. Based on Ku-bernetes, OpenFaaS includes a simple scheduling policy that spreads functions on cluster computing resources. As such, it is not well-suited for managing latency-sensitive applications in a geo-distributed environment, where network latencies are nonnegligible and negatively affect the application response time. To overcome this issue, in this paper we present GOFS (Geo-distributed Scheduling in OpenFaaS), which extends OpenFaaS with network-aware scheduling capabilities. GOFS addresses the serverless application scheduling in a geo-distributed environment by either solving a suitable integer linear programming problem or using a greedy network-aware heuristic. However, its modular architecture facilitates the integration of other custom scheduling policies. A wide set of prototype-based results shows the advantages of the proposed network-aware solutions over other benchmark scheduling policies.","PeriodicalId":270786,"journal":{"name":"2021 IEEE Symposium on Computers and Communications (ISCC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125181278","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
Automatic Face Mask Detection Using Deep Learning 基于深度学习的人脸自动检测
2021 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2021-09-05 DOI: 10.1109/ISCC53001.2021.9631409
Stephanie Anderson, Suma Veeravenkatappa, Priyanka Pola, Seyedamin Pouriyeh, Meng Han
{"title":"Automatic Face Mask Detection Using Deep Learning","authors":"Stephanie Anderson, Suma Veeravenkatappa, Priyanka Pola, Seyedamin Pouriyeh, Meng Han","doi":"10.1109/ISCC53001.2021.9631409","DOIUrl":"https://doi.org/10.1109/ISCC53001.2021.9631409","url":null,"abstract":"COVID-19 shook the entire world with its highly infectious transmission and death rate. As per CDC guidelines, wearing a mask can effectively reduce the spread of COVID-19 and create a protective barrier against the virus until the efficacy of currently available vaccines reaches 100% and the majority of people get vaccinated. Wearing masks is highly recommended almost everywhere, in schools, stores, movies, etc. to prevent the spread of this virus, however, monitoring people to see whether they wear masks is not an easy task. As a result, different face mask detection models were proposed. In this paper, we introduce a face mask detection model using deep learning. We have taken into consideration three different categories to train our model, Mask, No Mask, and Incorrect Mask. The proposed model has achieved 96% accuracy.","PeriodicalId":270786,"journal":{"name":"2021 IEEE Symposium on Computers and Communications (ISCC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130529176","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 Effective Data Structure for Contact Sequence Temporal Graphs 一种有效的接触序列时间图数据结构
2021 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2021-09-05 DOI: 10.1109/ISCC53001.2021.9631469
Sanaz Gheibi, Tania Banerjee-Mishra, S. Ranka, S. Sahni
{"title":"An Effective Data Structure for Contact Sequence Temporal Graphs","authors":"Sanaz Gheibi, Tania Banerjee-Mishra, S. Ranka, S. Sahni","doi":"10.1109/ISCC53001.2021.9631469","DOIUrl":"https://doi.org/10.1109/ISCC53001.2021.9631469","url":null,"abstract":"We propose a new time-respecting data structure (TRG) for contact sequence temporal graphs that is more memory efficient than previously proposed TRGs. Our new TRG alters the balance between TRG structures and the ordered sequence of edges (OSE) data structure. While TRG structures have an obvious performance advantage over OSE for problems that can be solved via a shallow neighborhood search, previous research has shown that single-source all-destinations problems are more effectively solved using OSE. The competitiveness of our TRG structure for this class of problems is demonstrated for the single-source all-destinations fastest paths and min-hop paths problems. Our TRG structure retains the advantage that other similar structures have over OSE for shallow neighborhood search problems.","PeriodicalId":270786,"journal":{"name":"2021 IEEE Symposium on Computers and Communications (ISCC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116489489","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
Improved Face Detector on Fisheye Images via Spherical-Domain Attention 基于球域注意力的改进鱼眼图像人脸检测
2021 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2021-09-05 DOI: 10.1109/ISCC53001.2021.9631545
Jingbo Miao, Yanwei Liu, Jinxia Liu, A. Argyriou, Zhen Xu, Yanni Han
{"title":"Improved Face Detector on Fisheye Images via Spherical-Domain Attention","authors":"Jingbo Miao, Yanwei Liu, Jinxia Liu, A. Argyriou, Zhen Xu, Yanni Han","doi":"10.1109/ISCC53001.2021.9631545","DOIUrl":"https://doi.org/10.1109/ISCC53001.2021.9631545","url":null,"abstract":"As one type of omnidirectional projection, fisheye images have been widely used in automatic driving and visual surveillance. However, they cannot be processed well by the traditional algorithms designed for the planar rectilinear images since they usually suffer from severe geometric distortion during image formation. In this paper, the conventional face detection algorithm is enhanced to fit the fisheye images via combining with the spherical convolution block by learning rotation-invariant features from the spherical domain. The learned features from both planar and spherical domains are subsequently mixed by the spatial attention mechanism. Consequently, the whole network can automatically learn the distorted features directly from different positions on the target image. Experimental results verify that our network can detect distorted faces on fisheye images effectively and maintain the performance on traditional planar images.","PeriodicalId":270786,"journal":{"name":"2021 IEEE Symposium on Computers and Communications (ISCC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133941503","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
STRAD: Network Intrusion Detection Algorithm Based on Zero-Positive Learning in Real Complex Network Environment 真实复杂网络环境下基于零正学习的网络入侵检测算法
2021 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2021-09-05 DOI: 10.1109/ISCC53001.2021.9631496
Ying Zhong, Ziqi Gao, Rui Li, Citong Que, Xinjie Yang, Zhiliang Wang, Jiahai Yang, Xia Yin, Xingang Shi, Keqin Li
{"title":"STRAD: Network Intrusion Detection Algorithm Based on Zero-Positive Learning in Real Complex Network Environment","authors":"Ying Zhong, Ziqi Gao, Rui Li, Citong Que, Xinjie Yang, Zhiliang Wang, Jiahai Yang, Xia Yin, Xingang Shi, Keqin Li","doi":"10.1109/ISCC53001.2021.9631496","DOIUrl":"https://doi.org/10.1109/ISCC53001.2021.9631496","url":null,"abstract":"With the increasing network security risks, network intrusion detection technology has become more important. At present, machine learning is applied in most advanced traffic anomaly detection algorithms, but these algorithms have three main shortcomings. First, algorithms using deep neural network are highly complex and not suitable for real-time online processing. Second, algorithms based on supervised learning require training on huge labeled data sets, which are limited and insufficient. Third, most algorithms have such poor generalization ability and portability that they are less suitable for real-world environments. Therefore, we propose a novel network anomaly detection model, STRAD. We use Word2vec and Damped Incremental Statistics algorithm for spatiotemporal features extraction, latent space compression (LSC) for feature vectors compression and an unsupervised one-class classifier for anomaly detection. Our evaluations show that STRAD has a better performance than other state of the art algorithms.","PeriodicalId":270786,"journal":{"name":"2021 IEEE Symposium on Computers and Communications (ISCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130772184","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
Experimental Workflow for Energy and Temperature Profiling on HPC Systems 高性能计算系统能量和温度分布的实验工作流程
2021 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2021-09-05 DOI: 10.1109/ISCC53001.2021.9631413
Kameswar Rao Vaddina, L. Lefèvre, Anne-Cécile Orgerie
{"title":"Experimental Workflow for Energy and Temperature Profiling on HPC Systems","authors":"Kameswar Rao Vaddina, L. Lefèvre, Anne-Cécile Orgerie","doi":"10.1109/ISCC53001.2021.9631413","DOIUrl":"https://doi.org/10.1109/ISCC53001.2021.9631413","url":null,"abstract":"Despite recent advances in improving the performance of high performance computing (HPC) and distributed systems, power dissipation and thermal cooling challenges persist, impacting their total cost of ownership. Making HPC systems more energy and thermal efficient will require understanding of individual power dissipation and temperature contributions of multiple hardware system components and their accompanying software. In this work, we present an experimental workflow for energy and temperature profiling on systems running parallel applications. It allows full and dynamic control over the execution of applications for the entire frequency range. Through its use, we show that the energy response to frequency scaling is highly dependent on the workload characteristics and it is convex in nature with an optimal frequency point. During the course of our experimentation, we encountered a non-intuitive finding, where we observed that the tested low-power processor is consuming more power on average than the standard processor.","PeriodicalId":270786,"journal":{"name":"2021 IEEE Symposium on Computers and Communications (ISCC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132112770","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
DSQNet: Domain SeQuence based Deep Neural Network for AGDs Detection DSQNet:基于域序列的agd检测深度神经网络
2021 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2021-09-05 DOI: 10.1109/ISCC53001.2021.9631503
Wei Xiong, Haiyang Jiang, Hongtao Guan, Fengrui Liu
{"title":"DSQNet: Domain SeQuence based Deep Neural Network for AGDs Detection","authors":"Wei Xiong, Haiyang Jiang, Hongtao Guan, Fengrui Liu","doi":"10.1109/ISCC53001.2021.9631503","DOIUrl":"https://doi.org/10.1109/ISCC53001.2021.9631503","url":null,"abstract":"Modern botnets widely rely on Algorithmically Generated Domains (AGDs) to contact with Command-and-Control (C&C) servers. Existing AGD detection solutions check the domains one by one based on the structural differences between AGD and benign ones, e.g., some AGD families show much more random character composition than legitimate ones. These methods can hardly deal with the newly emerged camouflage technology based AGD types, as each individual AGD seems benign in domain structure features of itself. In this work, the structural correlations among AGDs are analyzed and we find the inter-AGD correlation can be adopted for the AGD detection. We then propose DSQNet, a Domain SeQuence based Deep Neural Network AGD detection model, that simultaneously checks the domains in batch to take the inter-AGD correlation into consideration during the detection. Experiments on the public and real-world dataset show the superiority of the proposed approach.","PeriodicalId":270786,"journal":{"name":"2021 IEEE Symposium on Computers and Communications (ISCC)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129477840","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
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