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IEEE International Conference on Web services (ICWS)

作者： Cheng Wang Songyao Chai Hangyu Zhu Department of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education China Shanghai Artificial Intelligence Laboratory Shanghai China

The rapid growth of electronic commerce brings convenience to modern life but comes with security risks by various cybercrimes in online payment services. Most existing security methods for fraud detection depend on the static learning paradigm, which trains a model over a static training dataset and deploys the trained model for inference with the frozen model parameters under the i.i.d. assumption. Unfortunately, this paradigm becomes incommensurate with the increasingly complicated and varying fraud patterns due to the untimely and delayed responses in the offline environment. Without sensing the evolution of fraud timely, it is challenging to train and deploy targeted countermeasures. The emerging means of fraud are not only reflected in the openness of their category, but also in the drift of their superimposed risk features. The interweaving of open-category and concept drift accelerates the process of existing methods becoming powerless. In this paper, we propose EvoFD, an online evolving fraud detection framework to enable continual learning to cope with undercurrent surges of evolving fraud. The core idea of EvoFD is to weaken the bias caused by the anchoring effect on the learned information. It learns in an online streaming fashion by using instructive representations as anchors. Specially, we maintain the progressively updatable class anchors and optimize the representation network to embed features and class anchors into a unified normalized space, where the training and predicting can be conducted simultaneously or independently. In the framework, we preserve the balanced replay memory for each class to accumulate knowledge and avoid forgetting. The advantages of our method are validated by extensive experiments over the real-world dataset from a prestigious bank.

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Rough Sets and Knowledge Technology: 9th International Conference, RSKT 2014, Shanghai, China, October 24-26, 2014, Proceedings 1

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丛书名： Lecture Notes in Computer Science 8818 Lecture Notes in Artificial Intelligence

2014年

作者： Duoqian Miao, Witold Pedrycz, Dominik Ślȩzak, Georg Peters, Qinghua Hu, Ruizhi Wang (eds.)

ISBN: (数字)9783319117409

ISBN: (纸本)9783319117393;9783319117409

This book constitutes the thoroughly refereed conference proceedings of the 9th International Conference on Rough Sets and Knowledge Technology, RSKT 2014, held in Shanghai, China, in October 2014. The 70 papers presented were carefully reviewed and selected from 162 submissions. The papers in this volume cover topics such as foundations and generalizations of rough sets, attribute reduction and feature selection, applications of rough sets, intelligent systems and applications, knowledge technology, domain-oriented data-driven data mining, uncertainty in granular computing, advances in granular computing, big data to wise decisions, rough set theory, and three-way decisions, uncertainty, and granular computing.

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Novel Clustering-Based Approach for Local Outlier Detection

Novel Clustering-Based Approach for Local Outlier Detection

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IEEE Conference on Computer Communications Workshops

作者： Haizhou Du Shengjie Zhao Daqiang Zhang Jinsong Wu Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University School of Software Engineering Tongji University Department of Electrical Engineering Universidad de Chile

ISBN: (纸本)9781467399562

With the rapid expansion of data scale, big data mining and analysis have attracted increasing attention. Outlier detection as an important task of data mining is widely used in many applications. However, conventional outlier detection methods have difficulty handling large-scale datasets. In addition, most of them typically can only identify global outliers and are over sensitive to parameters variation. In this paper, we propose a novel method for robust local outlier detection with statistical parameters, which incorporates the clustering-based ideas in dealing with big data. Firstly, this method finds some density peaks of dataset by 3σ standard. Secondly, each remaining data object in the dataset is assigned to the same cluster as its nearest neighbor of higher density. Finally, we use Chebyshev's inequality and density peak reachability to identify local outliers of each group. The experimental results demonstrate the efficiency and accuracy of the proposed method in identifying both global and local outliers. Moreover, the method is also proved to be more stability analysis than typical outlier detection methods, such as LOF (Local Outlier Factor) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise).

关键词： Outlier detection Big data Data mining Clustering-based

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Associating semantic sensor web with domain ontology: The way to obtain meaningful sensor data

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International Journal of u- and e- service, Science and Technology 2014年第5期7卷 271-282页

作者： Ji, Chaoqun Liu, Jin Wang, Xiaofeng College of Info. Engineering Shanghai Maritime University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

To realize the sharing and reuse of sensor data and improve interoperability, semantic sensor web(SSW)is proposed to add semantics information to existing sensor networksby utilizing domain, spatial and temporal anthologies and other related semantic *** these is seldom research on how to fully utilize the sensor data through a semantic way such as domain ontology based inference. This paperpresents stateofthe art of SSWin various aspects, and proposesthe method to associate data from semantic sensor web with domain ontology to realize the communication between different domain ontologies and SSW. In addition, this paperalso proposes a new calculation method of semantic similarity amongdifferent entities in different ontology. Experiments show that this method can effectively find the similar entitiesandrealize the knowledge sharing and ontologies reuse. © 2014 SERSC.

关键词： Ontology

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Implementation Analysis of Reachability Graph and Its Application in Net Learning

Implementation Analysis of Reachability Graph and Its Applic...

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Computer, Control and Robotics (ICCCR), International Conference on

作者： Mingjian Guang Chungang Yan Zihao Lu Rui Duan Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China

ISBN: (数字)9798350373141

ISBN: (纸本)9798350373158

Reachability represents the most fundamental dynamic property of Petri nets. The reachability graph serves as a crucial tool for analyzing reachability, and its generation algorithm has garnered widespread attention. However, most of the existing work has focused primarily on the accuracy of the generation algorithm, often overlooking computational and time costs. Therefore, this paper concentrates on the implementation of reachability graphs and proposes three implementation methods: definition-based (DB), matrix-based (MB), and a fast generation method without self-loops (FG). Through comprehensive experiments, we evaluate and analyze the running speed and applicable scenarios of these three methods. Furthermore, we have successfully applied the proposed methods in net learning.

关键词： Digital control Costs Accuracy Heuristic algorithms Petri nets Benchmark testing Computational efficiency

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Virtual Source-Based Particle Swarm Optimizer for Multi-Source Location Using Mobile Swarm Robotics

Virtual Source-Based Particle Swarm Optimizer for Multi-Sour...

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第33届中国过程控制会议

作者： JunQi Zhang YuXuan Lin Qi Kang Department of Computer Science and Technology the Key Laboratory of Embedded System and Service ComputingTongji UniversityMinistry of EducationShanghai Electronic Trans actions and Information Service Collaborative Innovation Center IEEE

Source location is an interesting topic in the field of swarm ***-source location is a vital branch of *** with a single-source location problem,the main difficulty for the multi-source location lies in that the number and distribution of sources are *** Swarm Optimizer(PSO) has been widely adopted for the single-source location ***,its performance deteriorates greatly when it faces multiple sources because it is designed to find the single global best solution,not multiple *** handle such a challenge,a virtual source mechanism is proposed to help PSO overcome this *** key idea of the virtual source mechanism is to divide the search area into multiple cells,each of which has a virtual *** virtual source leads the swarm to cover each cell such that particles can cover all cells in a search space before they *** results demonstrate that the virtual source mechanism-based PSO effectively locates multiple sources.

关键词： Particle swarm optimizer virtual source mobile swarm robotics multi-source location

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Asymptotic bounds on lifetime of large scale clustering UWB sensor network

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Tien Tzu Hsueh Pao/Acta Electronica Sinica 2011年第10期39卷 2263-2268+2281页

作者： Xu, Juan Fang, Yu Xu, Hua-Jie School of Electronics and Information Engineering Tongji University Shanghai 200092 China Key Laboratory of Embedded System and Service Computing of the Ministry of Education Tongji University Shanghai 200092 China School of Computer and Electronic Information Guangxi University Nanning Guangxi 530004 China

Considering time hopping impulse radio ultra wide band (TH-IR UWB) sensor networks composed n sensor nodes which are placed according to a Poisson point process on a square and a Sink. The results demonstrate that the bounds on the lifetime of dense network increase with the increase of the number of nodes n, and that of extended network decrease with the increase of the number of nodes n. The results also indicate the bounds on the lifetime of clustering sensor networks is far more than that of non-clustering network, thus clustering can evidently improve network lifetime. Furthermore, the bounds on the lifetime of clustering TH-IR UWB sensor network which the nodes are distributed according to a Poisson point process are different from that of clustering network which the nodes are placed according to uniform distribution, thus the behavior of nodes deploying can remarkably affect the lifetime bound.

关键词： Energy dissipation

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Identification of mild cognitive impairment using extreme learning machines model 11th

Identification of mild cognitive impairment using extreme le...

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11th International Conference on Intelligent computing, ICIC 2015

作者： Zhang, Wen Shen, Hao Ji, Zhiwei Meng, Guanmin Wang, Bing School of Electronics and Information Engineering Tongji University Shanghai201804 China The Advanced Research Institute of Intelligent Sensing Network Tongji University Shanghai201804 China The Key Laboratory of Embedded System and Service Computing Tongji University Shanghai201804 China Department of Clinical Laboratory Tongde Hospital of Zhejiang Province 234th Gucui Road Hangzhou310012 China

ISBN: (纸本)9783319221854

Alzheimer’s disease (AD) is very common disorder among the older people. Predicting mild cognitive impairment (MCI), an intermediate stage between normal cognition and dementia, in individuals with some symptoms of cognitive decline may have great influence on treatment choice and disease progression. In this work, a novel prediction model based on extreme learning machines algorithm was proposed to identify mild cognitive impairment using the information of some biomarkers, i.e., Position Emission Tomography (PET), Magnetic Resonance Imaging (MRI), and the cerebrospinal fluid (CSF). Firstly, each subject will be represented by three biomarkers using a vector with 201 dimensions after some essential pre-processing. Then, a prediction model based on extreme learning machine algorithm was trained to catch the difference between the patients and healthy persons. Finally, the model had been employed to identify identifying MCI participants from the normal people. Based on a dataset, including 99 MCI patients and 52 health controls, our proposed method achieved 70.86 % prediction accuracy with 67.68 % sensitivity at the precision of 76.92 %. Extensive experiments are performed to compare our method with state-of-the-art techniques, i.e., support vector machine (SVM) and SVM with fusion kernels. Experimental results demonstrate that proposed extreme learning machine is a powerful tool for predicting MCI with excellent performance and less time. © Springer International Publishing Switzerland 2015.

关键词： Support vector machines

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Efficient poisoning attacks and defenses for unlabeled data in DDoS prediction of intelligent transportation systems

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Security and Safety 2022年第1期1卷 121-141页

作者： Zhong Li Xianke Wu Changjun Jiang College of Information Science and Technology Donghua University Key Laboratory of the Ministry of Education for Embedded System and Service Computing Department of Computer Science Tongji University Shanghai Network Financial Security Collaborative Innovation Center Tongji University

Nowadays, large numbers of smart sensors(e.g., road-side cameras) which communicate with nearby base stations could launch distributed denial of services(DDoS) attack storms in intelligent transportation systems. DDoS attacks disable the services provided by base stations. Thus in this paper, considering the uneven communication traffic flows and privacy preserving, we give a hidden Markov model-based prediction model by utilizing the multi-step characteristic of DDoS with a federated learning framework to predict whether DDoS attacks will happen on base stations in the future. However, in the federated learning,we need to consider the problem of poisoning attacks due to malicious participants. The poisoning attacks will lead to the intelligent transportation systems paralysis without security protection. Traditional poisoning attacks mainly apply to the classification model with labeled data. In this paper, we propose a reinforcement learning-based poisoning method specifically for poisoning the prediction model with unlabeled data. Besides, previous related defense strategies rely on validation datasets with labeled data in the server. However, it is unrealistic since the local training datasets are not uploaded to the server due to privacy preserving, and our datasets are also unlabeled. Furthermore, we give a validation dataset-free defense strategy based on Dempster–Shafer(D–S) evidence theory avoiding anomaly aggregation to obtain a robust global model for precise DDoS prediction. In our experiments, we simulate 3000 points in combination with DARPA2000 dataset to carry out evaluations. The results indicate that our poisoning method can successfully poison the global prediction model with unlabeled data in a short time. Meanwhile, we compare our proposed defense algorithm with three popularly used defense algorithms. The results show that our defense method has a high accuracy rate of excluding poisoners and can obtain a high attack prediction probability.

关键词： Poisoning attacks Defenses Multi-step DDoS prediction Unlabeled data Intelligent transportation systems

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Petri net based behavior description of cross-organization workflow with synchronous interaction pattern 1

Petri net based behavior description of cross-organization w...

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1st International Workshop on Process-Aware systems, PAS 2014

作者： Liu, Cong Zeng, Qingtian Duan, Hua Lu, Faming The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai200092 China College of Information Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Electronic Communication Shandong University of Science and Technology Qingdao266590 China College of Mathematics Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9783662461693

Today’s workflow systems are crossing organizational boundaries and usually involve multiple organizations or partners, and the crossorganization workflow has received much public attention from both the academia and the industry. This way the property analysis as well as system control for a cross-organization workflow is very important. In this paper, we focus on a kind of loosely coupled workflow architecture with synchronous interaction pattern, i.e. each organization owns its private workflow process and can operate independently, and they need to synchronize through certain tasks. Its behavior description approach is obtained using the synchronized shuffle operation of Petri net language. It is proved that our approach benefits the behavior characterization of cross-organizational workflow with synchronous interaction pattern and can be further used to decide and control the fairness, liveness, deadlock and trap for the cross-organization workflow. A running case of cross-organizational medical diagnosis workflow is given to validate our approaches throughout the whole paper. © Springer-Verlag Berlin Heidelberg 2015.

关键词： Petri nets

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