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SSRN 2022年

作者： Wang, Jun Xu, Xiaolong Nanjing University of Posts & Telecommunications Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing210023 China Nanjing University of Posts & Telecommunications School of Computer Science Nanjing210023 China

With the continuous development of deep learning, the use of deep learning methods for intelligent violent behavior recognition has become an active research field in computer vision. However, there are relatively few studies on violent behavior recognition in dark light environment. In this paper, we propose the DarkFight network model for violent behavior recognition in dark light environment. In the proposed DarkFight model, Resnet50 is used as the basic network model to extract features. In order to obtain multi-scale spatiotemporal features, DarkFight adds a multi-scale attention module PSA to the network. The DarkFight model uses Zero-DCE for low-light image enhancement. In order not to lose the spatiotemporal characteristics of the low-light environment, DarkFight uses the initial dark-light data and the enhanced data as the input to the two paths of the model. In addition, this paper researches the replacement of the traditional temporal global average pooling layer of the network with the BERT module to better utilize the temporal information when fusing the two path features. We construct a violent behavior dataset VDID (Violence Detection in Dark) in a dark-light environment, and conduct experimental verification on this dataset. The verification results show that the recognition precision of the DarkFight model increases by 2.51%, the accuracy increases by 3.4%, and the F1-score improves by 0.032. © 2022, The Authors. All rights reserved.

关键词： Image enhancement

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Anomaly detection and traceback scheme for cloud-edge networks

Anomaly detection and traceback scheme for cloud-edge networ...

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International Conference on Computer Supported Cooperative Work in Design

作者： Xuanyan Liu Jinling He Hu Song Xinyun Cheng Luyun Hu Xiaolong Xu Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing China State Grid Jiangsu Electric Power Company Limited Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Cloud-edge networks (CEN) has revolutionized the traditional network architecture. However, there are many security issues in CEN needing to be solved. In this case, CEN administrator should be able to detect network anomalies and find out the sources of anomalies rapidly, and respond in time to block anomalies. In order to protect network systems with CEN, we propose a mechanism consisting of two modules, including anomaly detection and anomaly traceback. The self-similarity anomaly detection is introduced for the first time to detect anomalies. In addition, we also propose an anomaly traceback method which utilizes the characteristics of CEN. An CEN testbed is established to evaluate the mechanism we proposed. Results of experiments show that the proposed mechanism can discover anomalies quickly and accurately, and trace anomaly sources and find anomaly paths accurately.

关键词： Federated learning Refining Process control Network security Network architecture Control systems Indexes

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Deep representation learning for road detection using siamese network

arXiv

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arXiv 2019年

作者： Liu, Huafeng Han, Xiaofeng Li, Xiangrui Yao, Yazhou Huang, Pu Tang, Zhenming School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China

Robust road detection is a key challenge in safe autonomous driving. Recently, with the rapid development of 3D sensors, more and more researchers are trying to fuse information across different sensors to improve the performance of road detection. Although many successful works have been achieved in this field, methods for data fusion under deep learning framework is still an open problem. In this paper, we propose a Siamese deep neural network based on FCN-8s to detect road region. Our method uses data collected from a monocular color camera and a Velodyne-64 LiDAR sensor. We project the LiDAR point clouds onto the image plane to generate LiDAR images and feed them into one of the branches of the network. The RGB images are fed into another branch of our proposed network. The feature maps that these two branches extract in multiple scales are fused before each pooling layer, via padding additional fusion layers. Extensive experimental results on public dataset KITTI ROAD demonstrate the effectiveness of our proposed approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Roads and streets

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A Privacy-Preserving and Verifiable Federated Learning Scheme

A Privacy-Preserving and Verifiable Federated Learning Schem...

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IEEE International Conference on Communications (ICC)

作者： Xianglong Zhang Anmin Fu Huaqun Wang Chunyi Zhou Zhenzhu Chen School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing PR China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing PR China

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Due to the complexity of the data environment, many organizations prefer to train deep learning models together by sharing training sets. However, this process is always accompanied by the restriction of distributed storage and privacy. Federated learning addresses this challenge by only sharing gradients with the server without revealing training sets. Unfortunately, existing research has shown that the server could extract information of the training sets from shared gradients. Besides, the server may falsify the calculated result to affect the accuracy of the trained model. To solve the above problems, we propose a privacy-preserving and verifiable federated learning scheme. Our scheme focuses on processing shared gradients by combining the Chinese Remainder Theorem and the Paillier homomorphic encryption, which can realize privacy-preserving federated learning with low computation and communication costs. In addition, we introduce the bilinear aggregate signature technology into federated learning, which effectively verifies the correctness of aggregated gradient. Moreover, the experiment shows that even with the added verification function, our scheme still has high accuracy and efficiency.

关键词： Servers Machine learning Training Encryption Aggregates Computational modeling Privacy

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Proxy Re-Encryption Scheme For Complicated Access Control Factors Description in Hybrid Cloud

Proxy Re-Encryption Scheme For Complicated Access Control Fa...

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IEEE International Conference on Communications (ICC)

作者： Zheng Lian Mang Su Anmin Fu Huaqun Wang Chunyi Zhou School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing PR China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing PR China

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Hybrid cloud has both the strong computing power of public cloud and easy control of private cloud. It provides users with robust services and convenience, meanwhile faces numerous security challenges. How to implement the effective access control is one of them, the purpose of which is deploying policy in private cloud to protect the ciphertext in public cloud. Furthermore, it becomes more and more difficult to describe the access control policy, which is suitable for multi-factor and dynamic updating. Considering the issues above, we propose a proxy re-encryption (PRE) scheme for complicated access control factors description in hybrid cloud. Firstly, we build the system model combining PRE with access control in hybrid cloud. Secondly, we design the algorithm for our scheme including the key construction with multi-factor and its weight, which achieve the target of dynamic updating. Finally, we analyze the security of this scheme by the mathematical method and performance by theory, experiment and comparisons with some other works. Our scheme has made the deployment of access control in hybrid cloud more reliable and scalable.

关键词： Cloud computing Access control Cryptography XML Reliability Privacy

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Point Cloud Compression Method Based on Curvature Grading and Inter-point Compression for Industrial Products

Point Cloud Compression Method Based on Curvature Grading an...

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International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC

作者： Lei Wang Wei Huang Yiping Chen Ran Chen School of Computer Nanjing University of Posts and Telecommunications Nanjing P. R. China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing P. R. China

The traditional point cloud compression methods fail to meet the compression requirements of complex surface point cloud files in the industrial field. This paper proposes a point cloud compression method suitable for industrial products with defective and complex surfaces, which compresses the defect and non-defect parts separately using different compression approaches. A curvature-based compression method is designed for the defect points, and an inter-point compression method is developed for the non-defect part, achieving compression of both components of the point cloud. The experimental results show the advantages of the proposed compression method through a comparative analysis of time efficiency, compression ratio, and reconstruction accuracy for industrial point cloud compression.

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TFS-index-based Multi-keyword Ranked Search Scheme Over Cloud Encrypted data

TFS-index-based Multi-keyword Ranked Search Scheme Over Clou...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yinfu Deng Hua Dai Yuanlong Liu Zhangchen Li Geng Yang Xun Yi Nanjing University of Post and Telecommunication Nanjing China Jiangsu Security and Intelligent Processing Lab of Big Data Nanjing University of Post and Telecommunication Nanjing China Royal Melbourne Institute of Technology University Melbourne Australia

Traditional searchable encryption schemes for clouds are generally based on TF-IDF vector space model, but they ignore the high-dimensional sparse characteristic of encrypted vectors. It will lead to substantial computational cost of inner product, and thus slows the search speed. In this paper, we propose a two-layer fast search index-based multi-keyword ranked search scheme (TFSRS) to address this problem. In the proposed TFSRS scheme, a keyword clustering-based equal-length dictionary partition (KCEDP) strategy is adopted to compress the document and search vectors, which benefits the process of inner product. Based on the strategy, a novel KCEDP-based vector space model (KCEDP-VSM) is proposed, and based on which, a two-layer fast search index (TFS-index) is presented. By using the TFS-index, secure inner product and symmetric encryption, the efficient multi-keyword ranked search scheme over encrypted cloud data is proposed. Experimental results show the better performance of the proposed schemes in search efficiency.

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Path Planning based on Reinforcement Learning with Improved APF model for Synergistic Multi-UAVs

Path Planning based on Reinforcement Learning with Improved ...

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International Conference on Computer Supported Cooperative Work in Design

作者： Qun Ding Xiaolong Xu Wenming Gui Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Jinling Institute of Technology Nanjing China

As the emerging technology of Unmanned Aerial Vehicles (UAVs) becomes mature, UAVs are widely used in environmental monitoring, communication and other fields. In view of this, this paper analyzed the task of synergistic multi-UAVs exploration of unknown environments, and proposed a path planning method for them based on reinforcement learning. Firstly, the path planning task of the UAVs was divided into two parts: the path travel strategy module and the information exploration strategy module. Models of the two modules were based on the Deep Deterministic Policy Gradient algorithm (DDPG), and an improved Artificial Potential Field (APF) force traction mechanism was introduced in the path travel strategy module. Its aim was to assist in guiding the generation of UAV flight path trajectories. Also it could enhance the learning capability of the model. The path travel strategy module would generate the complete flight path of the whole cluster in a distributed manner. A series of temporary target points provided by the information exploration strategy module helped. In maps with 21.5%, 25.3% and 29.6% of obstacles, multi-UAVs could achieve 84.2%, 76.7% and 69.9% of environmental exploration by the designed method. Compared with the APF method, the A star method and the Breath First Search (BFS) method, the proposed method is not only able to plan feasible paths in a more complex map model, but also the curvature of the planned paths is smoother, thus achieving the goal of reducing the energy cost of UAVs.

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A particle filter based multi-objective optimization algorithm: PFOPS

arXiv

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arXiv 2018年

作者： Liu, Bin Jin, Yaochu School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Key Lab of Big Data Security and Intelligent Processing Nanjing Jiangsu210023 China Department of Computer Science University of Surrey Guildford SurreyGU2 7XH United Kingdom

This paper is concerned with a recently developed paradigm for population-based optimization, termed particle filter optimization (PFO). This paradigm is attractive in terms of coherence in theory and easiness in mathematical analysis and interpretation. Current PFO algorithms only work for single-objective optimization cases, while many real-life problems involve multiple objectives to be optimized simultaneously. To this end, we make an effort to extend the scope of application of the PFO paradigm to multi-objective optimization (MOO) cases. An idea called path sampling is adopted within the PFO scheme to balance the different objectives to be optimized. The resulting algorithm is thus termed PFO with Path Sampling (PFOPS). The validity of the presented algorithm is assessed based on three benchmark MOO experiments, in which the shapes of the Pareto fronts are convex, concave and discontinuous, respectively. Copyright © 2018, The Authors. All rights reserved.

关键词： Monte Carlo methods

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Area Charging for Wireless Rechargeable Sensors

Area Charging for Wireless Rechargeable Sensors

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International Conference on Computer Communications and Networks (ICCCN)

作者： Haipeng Dai Xiaoyu Wang Lijie Xu Chao Dong Qian Liu Lei Meng Guihai Chen State Key Laboratory for Novel Software Technology Nanjing University Nanjing CHINA Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing CHINA Nanjing University of Aeronautics and Astronautics Nanjing CHINA

ISBN: (数字)9781728166070

ISBN: (纸本)9781728166087

In this paper, we consider the problem of area charging, that is, assuming that there is a mobile charger (MC) equipped with a directional wireless charger whose charging area is in the shape of a sector, and the MC can only recharge sensors on the boundary of an Area of Interest (AOI), how to design an efficient charging scheme for the MC to recharge any sensor inside the AOI with efficient energy while its overall charging time is minimized. To address this problem, we first partition the AOI into two types of subareas, i.e., rectangle-like subareas and sector-like subareas, based on the Medial Axis of the AOI. Second, we propose rectangle-based moving strategy for the rectangle-like subareas, and sector-based rotating strategy for the sector-like subareas, respectively, for the MC. Finally, we calculate charging time of all subareas based on the above two moving strategies, and prove that our algorithm achieves a constant approximation ratio. We evaluate our algorithm by conducting extensive simulation. The results show that on average, other comparison algorithms require at least 18:70 times of charging time of that of our algorithm.

关键词： Sensors Wireless sensor networks Wireless communication Communication system security Approximation algorithms Shape security

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