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Anomaly Detection in Encrypted Identity Resolution Traffic based on Machine learning

Anomaly Detection in Encrypted Identity Resolution Traffic b...

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IEEE international conference on Software Quality, Reliability and Security (QRS)

作者： Zhishen Zhu Hao Zhou Qingya Yang Chonghua Wang Zhen Li Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China China Industrial Control Systems Cyber Emergency Response Team Beijing China

Identity resolution is an emerging network resource widely applied in Industrial Internet of Things. Although encryption improves the privacy of identity resolution, it also challenges DPI-based anomaly detection. Therefore, it is imperative to recognize and supplement the encrypted information of IDS. In this paper, we design a machine learning-based framework to automatically extract critical information of identity resolution system from network traffic. According to the characteristics of traffic, we use the hybrid feature of statistics and sequences to describe encrypted traffic. Besides, a supervised classification algorithm is applied to explore the effective classification of two communication processes, which are service attribution information for node addressing and operation behavior for data management. We tested this method based on the encrypted traffic collected from a realistic identity resolution system. The results indicate that our approach exhibits good performance, outperforms related works, and can be applied in resource-constrained industrial scenario. This is the first work analysing the identity resolution system from the perspective of traffic analysis.

关键词： Privacy Protocols Telecommunication traffic Software quality Machine learning Feature extraction Encryption

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Performance Comparison of Backbone Networks for Multi-Tasking in Self-Driving Operations

Performance Comparison of Backbone Networks for Multi-Taskin...

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international conference on Control, Automation and Systems ( ICCAS)

作者： Shakhboz Abdigapporov Shokhrukh Miraliev Jumabek Alikhanov Vijay Kakani Hakil Kim Department of Electrical and Computer Engineering Inha University Incheon Korea Department of Integrated System Engineering Inha University Incheon Korea

In the era of big data, increased focus has been on improving neural network based Deep learning models. This led to various classification networks which can be used as a backbone in multi-task learning. However, depending on the selected backbone, multi-tasking performance differs. While given backbone network shows better performance on a detection task, does not mean such performance generalizes in segmentation task as well. Detailed investigations should be conducted to achieve best inference speed-accuracy trade-off prior to implementing a single neural network, which handles multiple tasks. In this research, the performance comparison among EfficientNet, ResNet101, VGG16, ResNet50 and MobilenetV2 on the Berkeley Driving dataset (BDD100K) for autonomous driving using multi-tasking architecture are provided. Backbones that offer best time-accuracy trade-off for multi-task learning are evaluated. Implemented architecture contains three most crucial tasks in self-driving operations, object detection, drivable area segmentation and lane detection. EfficientNet based model showed the best mAP on the object detection task, as well as on the segmentation tasks, extracting both the long and wide roads with accurate lane lines. The model with MobilenetV2 backbone however, demonstrates the fastest inference speed with relatively lower performance in all tasks.

关键词： Deep learning Performance evaluation Lane detection Roads Neural networks Object detection Multitasking

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Deep learning based object detection for agricultural machinery

Deep learning based object detection for agricultural machin...

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international Symposium on Computational Intelligence and Informatics

作者： Sándor Tihamér Brassai András NÉMeth Attila Hammas Szabolcs László GÁBor Department of Electrical Engineering Sapientia Hungarian University of Transylvania Corunca Romania Department of Electronic Warfare National University of Public Service Budapest Hungary Intelligent Control Systems Sapientia Hungarian University of Transylvania Corunca Romania Ph.D. in Military Engineering National University of Public Service Budapest Hungary

ISBN: (纸本)9798350398830

Drone imagery based object supervising has become more and more widespread. In the paper the Single shot Alignment Network is used to classify and localize the objects. The images were acquired by using two types of drones, DJI Tello and Zll SG906 Pro 2 in about thirty classes, and about nineteen were processed and detailed in the paper. The objects labeling was realized in CVAT labeling tool. For neural network management the mmdetection framework was used, and the obtained results were detailed on s2a-net. The paper focuses on the preparation of the neural network system to be used for agricultural machine detection. The network was trained on a PC with reduced processing capabilities. The dataset was cut in smaller tasks. An architecture is proposed to be used in future for dataset management during the training process.

关键词： Training Mechatronics Neural networks Object detection Agricultural machinery Labeling Task analysis

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EventPointNet: Robust Keypoint Detection with Neuromorphic Camera data

EventPointNet: Robust Keypoint Detection with Neuromorphic C...

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international conference on Control, Automation and Systems ( ICCAS)

作者： Howoong Jun Sangil Lee Songhwai Oh Graduate School of Artificial Intelligence (GSAI) Seoul National University Seoul Korea Department of Mechanical and Aerospace Engineering (MAE) Seoul National University Seoul Korea Department of Electrical and Computer Engineering (ECE) and Graduate School of Artifical Intelligence (GSAI) Seoul National University Seoul Korea

In this paper, we propose a new method for keypoint detection using neuromorphic camera data. Robust keypoint detection in diverse conditions is a major issue for visual simultaneous localization and mapping (SLAM), place recognition, and computer vision. Recently, many methods adopt supervised learning to solve the problem. However, it is hard to define the exact reference keypoints on natural scenes, so the training process can be ambiguous for the problem. To handle this issue, we propose a new method named EventPointNet which is trained from data collected from a neuromorphic camera, also known as an event-based camera. Since the event-based camera captures natural edge points from any scenes regardless of illumination and viewpoint changes, the data can be used as proper references for keypoint detection. Therefore, a network trained with these data can detect distinct keypoints on a gray-scale image captured from a conventional camera. The proposed method is validated by comparing with both handcrafted and learning-based approaches on HPatches dataset. The experimental results show that EventPointNet detects more valid keypoints than the other methods in terms of both qualitative and quantitative results, especially on the illumination conditions with 1.31% higher matching score compared to the second best method. We also perform the visual odometry experiments on the KITTI dataset to show that EventPointNet can be applied to robotic applications. In particular, EventPointNet shows a reduction of 30.74% in the trajectory error compared to the second best algorithm.

关键词： Training Visualization Simultaneous localization and mapping Neuromorphics Robot vision systems Lighting Detectors

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Customer Segment Application of Machine learning in Business Operation of China Mobile 22

Customer Segment Application of Machine Learning in Business...

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22nd IEEE international conference on High Performance Computing and Communications, 18th IEEE international conference on Smart City and 6th IEEE international conference on data Science and Systems, HPCC-SmartCity-DSS 2020

作者： Li, Qiaoyu Yang, Wei Liu, Ying Liu, Xiaofang Zhao, Yaohong Chu, Jun Zeng, Haitao Zhao, Xuefeng Deng, Chao Feng, Junlan China Mobile Research Institute Beijing China

ISBN: (纸本)9781728176499

The purpose of this paper is to use machine learning technology to build more user profiles in the process of customer segmentation to assist marketing decision-making. Specifically, by introducing cross features into the construction of Feature engineering, the accuracy of target group prediction can be further improved and the user subgroup after feature crossing can be mined. In addition, this paper also proposes an improved decision tree model, which can build more comprehensive user profiles, and then assist in intelligent marketing. Firstly, both methods need to preprocess the data. In addition, for the subsequent feature crossing, it is necessary to discretize the continuous features and implement one-hot encoding for all data. In the process of discretization, the paper realizes the automatic selection of better discrete granularity value. A specific example is given and a comparative experiment is carried out to verify the effectiveness of the method. © 2020 IEEE.

关键词： Machine learning

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Crowdsourced Testing Task Assignment based on Knowledge Graphs

Crowdsourced Testing Task Assignment based on Knowledge Grap...

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IEEE international conference on Software Quality, Reliability and Security (QRS)

作者： Peng Yang Chao Chang Yong Tang School of Computer South China Normal University Guangzhou China School of Information Engineering Guangzhou Panyu Polytechnic Guangzhou China Pazhou Lab Guangzhou China

The non-professional and uncertain testers in crowdsourced testing could lead to the problems of uneven test report quality, substandard test requirement coverage, a large number of repeated bug reports, and low efficiency of report reviewing. This paper designs a crowdsourced testing task assignment approach based on knowledge graph, trying to make full use of the individual advantages and crowd intelligence of crowdsourced workers in crowdsourced testing through personalized task assignment, with the goal to improve the quality of test reports and test completion efficiency. The approach includes three modules: 1) knowledge graph data acquisition: the concept of collaborative crowdsourced test is introduced, and a complete crowdsourced report submission platform is built to obtain the required data for the knowledge graph. 2) Knowledge graph feature learning: building an internal knowledge graph of the crowdsourced testing field based on the data in the platform and combining the historical task records of crowdsourced workers as input, using the machine learning model to get the crowdsourced workers’ preference for specific tasks, and integrates the three-level page coverage and bug-like status. 3) Knowledge graph task assignment: assign test tasks and audit tasks to crowdsourced workers in order to improve the coverage of test requirements and overall test efficiency. We compare the quantity and quality of bug reports in a crowdsourced test task between the task assignment system based on a knowledge graph and the system based on collaborative filtering, which proves the effectiveness of our task assignment technique.

关键词： Representation learning Deep learning Computer bugs Knowledge graphs Software quality data models Software reliability

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Optimizing broad learning system hyper-parameters through particle swarm optimization for predicting COVID-19 in 184 Countries 22

Optimizing broad learning system hyper-parameters through pa...

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22nd IEEE international conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Zhan, Choujun Wu, Zhengdong Wen, Quansi Gao, Ying Zhang, Haijun School of Computing South China Normal University Guangzhou China School of Electrical and Computer Engineering Nanfang College of Sun Yat-Sen University Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Shenzhen Graduate School Harbin Institute of Technology Shenzhen China

ISBN: (纸本)9781728162676

The Coronavirus Disease 2019 (COVID-19) began to outbreak since December 2019 and widely spread over the world. How to accurately predict the spread of COVID-19 is one of the essential issues for controlling the pandemic. This study establishes a general model that can predict the trend of COVID-19 in a country based on historical COVID-19 data in 184 countries. First, Savitzky-Golay (S-G) filter is utilized to detect multiple waves of COVID-19 in a country. Then, a PSO-SIR (particle swarm optimization susceptible-infected-recovery) model is provided for data augmentation. Finally, a novel PSO-BLS (particle swarm optimization broad learning system) is proposed for predicting the trend of COVID-19. Experimental results show that compared with the deep learning models (ANN, CNN, LSTM, and GRU), the PSO-BLS algorithm has higher accuracy and stability in predicting the number of active infected cases and removed cases. © 2021 IEEE.

关键词： COVID-19

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CELA: An Accurate Learned Cardinality Estimator with Strong Generalization Ability and Dimensional Adaptability 22nd

CELA: An Accurate Learned Cardinality Estimator with Strong ...

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22nd international conference on Web Information Systems engineering (WISE)

作者： Zhou, Weiqing Zhan, Siyu Guo, Lei Dai, Bo Univ Elect Sci & Technol China West Hitech Zone 2006 Xiyuan Ave Chengdu 611731 Sichuan Peoples R China

ISBN: (纸本)9783030908881;9783030908874

Accurate cardinality estimation contributes significantly to query optimization whereas traditional approaches such as histogram-based or sketching-based approaches relying on assumption of uniform distribution of data and appropriate pre-set parameters, often leading to dilemma in practical applications. In this paper, an accurate lightweight, dimensionally adaptive, strongly generalizable learned cardinality estimator for multi-dimensional range queries, CELA is proposed reflecting on the characteristics of desirable cardinality estimators. For the purpose of capturing relationship between dimensions, CELA raises a query-oriented approach of constructing constraint matrices to apply convolution. Experiments illustrates that CELA performs superbly on each defined indicators far superior to PostgreSQL. Furthermore, the strong generalization ability of CELA is demonstrated by the excellent performance trained with continuously scaled-down training set.

关键词： Cardinality estimation Dynamic architecture Deep learning Generalization ability Dimensional adaptability

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From Ivory Tower to Democratization and Industrialization: A Landscape View of Real-World Adaptation of Artificial Intelligence 22nd

From Ivory Tower to Democratization and Industrialization: A...

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11th international conference on intelligent Networking and Collaborative Systems (INCoS) / 22nd international conference on Network-Based Information Systems (NBiS)

作者： Yamakami, Toshihiko ACCESS Tokyo Japan

ISBN: (纸本)9783030290290;9783030290283

Deep learning is a hot research topic during 2010s. The rapid advances and data and computing-intensive characteristics of deep learning isolated ordinary people from the technology. However, as the technology advances, there are some visible signs of socialization in deep learning. The author presents the concept of democratization and industrialization of deep learning. Then, the author describes this trend as a new landscape view of AI technology. Finally, the author describes a 3-stage model of interaction between social community and technology in the era of digital transformation.

关键词： Deep learning

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Poster: Design of PBL Educational Program in Collaboration with Printing Company Paper Toy/Paper Craft Development Project 2nd

Poster: Design of PBL Educational Program in Collaboration w...

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22nd international conference on Interactive Collaborative learning (ICL) / 48th IGIP international conference on engineering Pedagogy - Impact of the 4th Industrial Revolution on engineering Education

作者： Minamide, Akiyuki Takemata, Kazuya Int Coll Technol Kanazawa Ishikawa Japan

ISBN: (纸本)9783030402747;9783030402730

To educate engineers, we need a method for social implementation education. Therefore, educational innovation including social cooperation has started in Japan. Under this program, students are asked to identify social issues and develop solutions. This paper describes the social implementation project utilized with fourth-year students of international College of Technology, Kanazawa.

关键词： engineering design Active learning Project based learning

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