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Dynamic-Static-based Spatiotemporal Multi-Graph Neural networks for Passenger Flow Prediction 26

Dynamic-Static-based Spatiotemporal Multi-Graph Neural Netwo...

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26th IEEE international conference on parallel and distributed Systems (IEEE ICPADS)

作者： Ma, Jingyan Gu, Jingjing Zhou, Qiang Wang, Qiuhong Sun, Ming Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9781728190747

Various sensing and computing technologies have gradually outlined the future of the intelligent city. Passenger flow prediction of public transports has become an important task in Intelligent Transportation System (ITS), which is the prerequisite for traffic management and urban planning. There exist many methods based on deep learning for learning the spatiotemporal features from high non-linearity and complexity of traffic flows. However, they only utilize temporal correlation and static spatial correlation, such as geographical distance, which is insufficient in the mining of dynamic spatial correlation. In this paper, we propose the Dynamic-Static based Spatiotemporal Multi-Graph Neural networks model (DSSTMG) for predicting traffic passenger flows, which can concurrently incorporate the temporal and multiple static and dynamic spatial correlations. Firstly, we exploit the multiple static spatial correlations by multi-graph fusion convolution operator, including adjacent relation, station functional zone similarity and geographical distance. Secondly, we exploit the spatial dynamic correlations by calculating the similarity between the flow pattern of stations over a period of time, and build the dynamic spatial attention. Moreover, we use time attention and encoder-decoder architecture to capture temporal correlation. The experimental results on two real world datasets show that the proposed DSSTMG outperforms state-of-the-art methods.

关键词： Passenger Flow Prediction Multi-Graph Fusion Spatiotemporal Correlations Mining Attention

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An Unsupervised Approach For Detecting Lateral Movement Logins Based On Knowledge Graph

An Unsupervised Approach For Detecting Lateral Movement Logi...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Xiaohan Ma Chen Li Bibo Tu Chinese Academy of Sciences Institute of Information Engineering Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

The lateral movement that relies on privacy theft is receiving increasing attention. This type of attack can be achieved by Cross-Host Login. Constructing an authentication graph between hosts is a common way to detect lateral movement, but the previous graph-based detection methods lack an effective means to integrate the account information into graph-based features. We abstract the login events as edges on the knowledge graph and account as the relations on the edges. Then we utilize a graph auto-encoder model comprised of an entity encoder and a scoring function (decoder), where entities are represented as low-dimensional vectors learned from the relational graph neural network. The decoder scores the edges to get the likelihood of the edges belonging to normal behaviors. We also propose a novel negative sample approach to reduce the false alarm rate. We apply this technique to authentication data derived from a large-scale real-world environment. The results show our approach can detect malicious authentication events associated with lateral movement with a true positive rate of 95 % and false positive rate of 10%, compared to 85% and 0.9% by the previous random walk approach as a baseline.

关键词： Privacy Image edge detection Authentication Knowledge graphs Feature extraction Resource description framework Graph neural networks

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A Graph Convolution Neural Network Based Method for Insider Threat Detection

A Graph Convolution Neural Network Based Method for Insider ...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Kexiong Fei Jiang Zhou Lin Su Weiping Wang Yong Chen Fan Zhang School of Cyber Security University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China Department of Computer Science Texas Tech University Lubbock USA Information Technology Center China Mobile Beijing China

In this research, we propose Log2Graph, a new insider threat detection method based on graph convolution neural network (GCN). This method first retrieves the corresponding logs and features from log files through feature extraction. Specifically, we use an auxiliary feature of anomaly index to describe relationship between entities, such as users and hosts, instead of establish complex connections between them. Second, these logs and features are augmented through a combination of oversampling and downsampling, to prepare for the next-stage supervised learning process. Third, we use three elaborated rules to construct the graph of each user by connecting the logs according to chronological and logical relationship. At last, the graph convolution neural network constructed is used to detect insider threats. Our validation and evaluation results confirm that Log2Graph can greatly improve the performance of detecting insider threats compared against baseline and existing methods.

关键词： Knowledge engineering Convolution Neural networks Supervised learning Feature extraction Time measurement Data models

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An adaptive traffic sign recognition scheme based on deep learning in complex environment

An adaptive traffic sign recognition scheme based on deep le...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Junguo Liao Yixun Zhou Qiangqiang Qin School of computer science and Engineering Hunan University of Science and Technology Xiangtan China

Since detection and recognition of traffic signs is one of three modules of autonomous driving, it has been concerned by many researchers. But the traffic sign images, which are gathered in complex environments such as bright or foggy weather, are often obscured or blurred. This may bring about serious challenge to recognize traffic sign. In this paper, a novel and adaptive traffic sign recognition scheme is proposed, which addresses the issue of traffic sign recognition in complex environments. First, according to the environmental conditions, the traffic sign images are classified into four categories by Swin Transformer. Then, based on INDANE and ACE algorithms, an adaptive dataset enhancement algorithm is proposed to enhance the classified dataset and strengthen the image features. Finally, yolov5 algorithm is improved to effectively accomplish traffic sign recognition by reducing the maximum down sampling multiple, adjusting the stacking proportion of residual blocks, increasing the convolution kernel, and replacing the normalization method. Based on the GTSRB dataset, the experiments are conducted to evaluate the performance of the proposed scheme. The experimental results show that the mAP of our proposed scheme is 99.86% when traffic sign is recognized in complex environment. Compared with the existing schemes, our proposed scheme has higher recognition accuracy and wider application.

关键词： Image recognition Convolution Stacking Neural networks Transformers Classification algorithms Kernel

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Whether Cache Advertisement is Necessary for Content Providers in CDNs? Model and Analysis

Whether Cache Advertisement is Necessary for Content Provide...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Xiaodong Dong Binlei Cai College of Computer Science Nankai University Tianjin China Shandong Computer Science Center Qilu University of Technology Jinan Shandong provience China

Cache advertisement is a widely adopted technique to reduce cache access costs by sending indicators to end-users to inform them of the cached content in Content Delivery networks (CDNs). However, determining the cache advertisement interval is still a problem. A short cache advertisement interval ensures end-users get information as approximate to real-time cached contents as possible but leads to high bandwidth costs. A long cache advertisement interval reduces bandwidth costs. But end-users will have a higher probability of mistakenly asserting that a content file is or is not stored in the cache, causing redundant cache accesses or mistakenly bypassing the cache, referred to as False Positive (FP) and False Negative (FN). Although, existing methods argue to make the cache advertisement interval self-adjustable. However, no rigorous mathematical proof has been given theoretically. In this paper, we answer whether cache advertisement is necessary and when a cache advertisement indicator should be sent through mathematical analysis. However, estimating the probability of FP and FP for a certain period is challenging as it depends on the specific request arrival sequence. Hence, instead of modeling the probability of FN and FP during a cache advertisement interval, we model them for serving next request. Then, we analyze the necessity of cache advertisement and derive the optimal cache advertisement interval. Next we design an online heuristic algorithm to find an approximate optimal cache advertisement interval without knowing the request arrival sequence. Experimental results show that cache advertisement is efficient in increasing content provider's revenue especially when the cache hit rate is low.

关键词： Analytical models Costs Content distribution networks Heuristic algorithms Mathematical analysis Bandwidth Approximation algorithms

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Herring: Rethinking the Parameter Server at Scale for the Cloud

Herring: Rethinking the Parameter Server at Scale for the Cl...

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international conference on High Performance computing, Networking, Storage and Analysis (SC)

作者： Thangakrishnan, Indu Cavdar, Derya Karakus, Can Ghai, Piyush Selivonchyk, Yauheni Pruce, Cory Amazon Web Serv Seattle WA 98109 USA

ISBN: (纸本)9781728199986

Training large deep neural networks is timeconsuming and may take days or even weeks to complete. Although parameter-server-based approaches were initially popular in distributed training, scalability issues led the field to move towards all -reduce -based approaches. Recent developments in cloud networking technologies, however, such as the Elastic Fabric Adapter (EFA) and Scalable Reliable Datagram (SRD), motivate a re -thinking of the parameter-server approach to address its fundamental inefficiencies. To this end, we introduce a novel communication library, Herring, which is designed to alleviate the performance bottlenecks in parameter-server-based training. We show that gradient reduction with Herring is twice as fast as all -reduce -based methods. We further demonstrate that training deep learning models like BERTiarge using Herring outperforms all -reduce -based training, achieving 85% scaling efficiency on large clusters with up to 2048 MINA V100 GPUs without accuracy drop.

关键词： Deep Learning Data parallel Training Scalability distributed parallel computing

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distributed Unsupervised Learning-Based Task Offloading for Mobile Edge computing Systems 15th

Distributed Unsupervised Learning-Based Task Offloading for ...

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15th EAI international conference on Communications and Networking, ChinaCom 2020

作者： Wei, Jianming Liu, Qiuming Liu, Shumin Zeng, Yiping Xiong, Xin Department of Software Engineering Jiangxi University of Science and Technology Nanchang China

ISBN: (纸本)9783030677190

Mobile edge computing has emerged as a new paradigm to enhance computing capabilities by offloading complicated tasks to nearby cloud server. To conserve energy as well as maintain quality of service, algorithms with low time complexity for task offloading is required. In this paper, a multi-user with multiple tasks scenario is considered, taking full account of factors including data size, bandwidth, channel state information, we propose a distributed unsupervised learning-based offloading algorithm for task offloading, where distributed parallel networks are employed to guarantee the robustness of algorithm. Additionally, we exploit a memory pool to store input data and corresponding decisions as key-value pairs. Based on the experience mechanism, the proposed algorithm can omit the step of data calibration compared with supervised learning method. To further reduce the communication cost, we analyze four bandwidth allocation schemes. Results reveal that the channel state-based strategy cost 3% less than data size-based, mixed and user size-based. Besides, the proposed algorithm can save 14, 18% cost than local and edge schemes respectively. Numerous results show that the proposed algorithm can achieve near-optimal decisions timely as well as having high reliability. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Energy efficiency

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Non-Cooperative Game for Edge Server Placement in Wireless Body Area Network

Non-Cooperative Game for Edge Server Placement in Wireless B...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Xiaolong Xu Yiming Fei Xiaokang Zhou Xiaoyong Li Yong Cheng Lianyong Qi Wanchun Dou School of Computer Science Nanjing University of Information Science and Technology Nanjing China Faculty of Data Science Shiga University Tokyo Japan College of Meteorology and Oceanography National University of Defense Technology Changsha China College of Computer Science and Technology China University of Petroleum (East China) China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

The outbreak of the coronavirus disease 2019 (COVID-19) has become the worst public health event in the whole world, threatening the physical and mental health of hundreds of millions of people. However, because of the high survivability of the virus, it is impossible for humans to eliminate viruses completely. For this reason, it is particularly important to strengthen the prevention of the transmission of viruses and monitor the physical status of the crowd. Wireless sensors are a key player in the fight against the current global outbreak of the Covid-19 pandemic, where they are playing an important role in monitoring human health. The Wireless Body Area Network (WBAN) composed of these wireless sensor devices can monitor human health data without interference for a long time, and update the data in almost real time through the Internet of Things (IoT). However, because the data monitored by the devices is relatively large and the transmission distance is long, only transmitting the data to medical centers through the personal devices (PB) cannot get feedback in time. We propose a non-cooperative game-based server placement method, which is named ESP-19 to improve the efficiency of transmission data of wireless sensors. In this paper, experimental tests are conducted based on the distribution of Shanghai Telecom's base stations, and then the performance of ESP-19 is evaluated. The results show that the proposed method in this paper outperforms the comparison method in terms of service delay.

关键词： Wireless communication COVID-19 Wireless sensor networks Computer viruses Games Body area networks Sensors

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A CVAE-GAN-based Approach to Process Imbalanced Datasets for Intrusion Detection in Marine Meteorological Sensor networks

A CVAE-GAN-based Approach to Process Imbalanced Datasets for...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Xin Su Tian Tian Lei Cai Baoliu Ye Hongyan Xing College of IOT Engineering Hohai University Changzhou Jiangsu China Information Department Hohai University Nanjing Jiangsu China Collaborative Innovation Center for Meteorological Disaster Prediction and Evaluation Nanjing University of Information Science and Technology Nanjing Jiangsu China Jiangsu Key Laboratory of Meteorological Detection and Information Processing Nanjing University of Information Science and Technology Nanjing Jiangsu China

In marine meteorological sensor networks (MMSN), there are massive data flows transmitted within numerous nodes, resulting in serious potential consequences once any anomalous traffic implied launches an attack. Therefore, accurate identification and fast response to abnormal traffic is vital for intrusion detection system (IDS) constructions. Dataset imbalances cause classification models to erroneously bias to normal traffic, significantly restricting IDS developments and applications. This paper proposes an approach to deal with dataset imbalances in intrusion detections. This approach mitigates dataset imbalance impacts on IDSs from the data perspective, which is liable to process the input data in classification models. In this approach, CVAE-GAN is adopted as the data generation module to synthesize specified minority class samples, thus reducing dataset imbalance rate. ordering points to identify the clustering structure (OPTICS) is taken as the denoising algorithm to remove outliers and decrease the overlap extent between majority classes. An experiment on NSL-KDD dataset demonstrates that the proposed method obtains a high-quality dataset with reasonable distribution. This approach improves the classifier's identification ability for potential anomalous traffic.

关键词： Training Measurement Noise reduction Intrusion detection Clustering algorithms Optics Data models

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Towards Secure Bilateral Friend Query with Conjunctive Policy Matching in Social networks

Towards Secure Bilateral Friend Query with Conjunctive Polic...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Chuan Zhang Mingyang Zhao Tong Wu Weiting Zhang Qing Fan Liehuang Zhu Cyberspace Science and Technology Beijing Institute of Technology Beijing China Cyberspace Science and Technology Beijing Institute of Technology Defense Innovation Institute Chinese Academy of Military Science Beijing China Electronic and Information Engineering Beijing Jiaotong University Beijing China

Bilateral friend queries have attracted increasing interest in social networks, as each user has a common requirement to specify a policy for the other. However, existing bilateral friend query schemes either only cannot support conjunctive policy matching, where the match is successful if the policy is a subset of attributes, or compromise user privacy, which reduces users' enthusiasm for friend query services. In this paper, we propose a secure bilateral friend query with conjunctive policy matching named BFQ. Specifically, the matchmaking encryption technique is utilized to achieve bilateral policy specification, where both sender and requester can specify policies for the other. Then, based on the Newton interpolation theorem, we design a novel conjunctive policy matching algorithm, where the match succeeds if and only if user authenticity holds and the policies of both parties are the subset of attributes. Otherwise, nothing is revealed except whether the match occurs or not. Security analysis proves that user authenticity and privacy, i.e., attribute privacy, policy privacy, and match privacy, are well guaranteed under the chosen-plaintext attack. We then implement a real prototype application and demonstrate that BFQ has practical efficiency through theoretical and experimental evaluation.

关键词： Access control Privacy Interpolation Analytical models Social networking (online) Prototypes Encryption

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