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security analysis and secured access design for networks of image remote sensing

Digital Communications and networks 2025年第1期11卷 136-144页

作者： Juan Zhao Haibo Dai Xiaolong Xu Hao Yan Zheng Zhang Chunguo Li School of Network Security Jinling Institute of TechnologyNanjing 211169China School of IoT Nanjing University of Posts and Telecommunications(NJUPT)Nanjing 210003China Jiangsu Key Lab.of Big Data Security and Intelligent Processing NJUPTNanJing 210003China School of Information Science and Engineering Southeast UniversityNanjing 210096China

The secured access is studied in this paper for the network of the image remote *** sensor in this network encounters the information security when uploading information of the images wirelessly from the sensor to the central collection *** order to enhance the sensing quality for the remote uploading,the passive reflection surface technique is *** one eavesdropper that exists nearby this sensor is keeping on accessing the same networks,he may receive the same image from this *** goal in this paper is to improve the SNR of legitimate collection unit while cut down the SNR of the eavesdropper as much as possible by adaptively adjust the uploading power from this sensor to enhance the security of the remote sensing *** order to achieve this goal,the secured energy efficiency performance is theoretically analyzed with respect to the number of the passive reflection elements by calculating the instantaneous performance over the channel fading *** on this theoretical result,the secured access is formulated as a mathematical optimization problem by adjusting the sensor uploading power as the unknown variables with the objective of the energy efficiency maximization while satisfying any required maximum data rate of the eavesdropper ***,the analytical expression is theoretically derived for the optimum uploading *** simulations verify the design approach.

关键词： Image remote sensing Secured access Energy efficiency Sensor transmit power Secured access design

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A Weighted Discrete Wavelet Transform-Based Capsule network for Malware Classification 27th

A Weighted Discrete Wavelet Transform-Based Capsule Network ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Qiao, Tonghua Cao, Chunjie Zou, Binghui Tao, Fangjian Cheng, Yinan Zhang, Qi Sun, Jingzhang Lab of Heterogeneous Network Security and Governance Haikou China School of Cyberspace Security Hainan University Haikou China Faculty of Data Science City University of Macau China

ISBN: (纸本)9783031781278

The rise of sophisticated malware poses a grave threat to computer security, challenging traditional detection methods. Traditional malware detection techniques, which primarily rely on feature engineering and defining rules to identify malware, are gradually failing to comprehensively and accurately detect emerging malware variants. To address this problem, we proposed an efficient and accurate novel malware classification model based on capsule network with Weighted Discrete Wavelet Transform (WDWTCN). In the field of malware classification, datasets are predominantly composed of grayscale images, which present samples in a single-channel format. To more adeptly capture the key features within malware samples, WDWTCN incorporates the Weighted Discrete Wavelet Transform (WDWT) as a preprocessing step for input data. By converting the single-channel input of grayscale images into a four-channel input, our model becomes significantly more efficient in extracting feature information from malware samples. We used ResNet50 as the feature extraction layer of the capsule network. By integrating global and local features, the model can comprehensively understand and classify malware, thereby enhancing the detection performance of malware. Finally, to validate the performance of the WDWTCN, we conducted an extensive experimental evaluation on three large datasets, i.e., BIG2015, MalImg and BODMAS. The experimental results showed that WDWTCN achieves significant improvements in malware detection accuracy, reaching 98.93%, 99.21% and 96.14% accuracy, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Discrete wavelet transforms

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OSE: On-Site State of Health Estimation for Li-ion Battery using Real-Time Field data

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7452-7462页

作者： Chen, Yanwen Zhao, Cong Yang, Shusen Zhao, Peng Ren, Xuebin Han, Qing Yang, Yuqian Wu, Shuaijun Xi'an Jiaotong University National Engineering Laboratory for Big Data Analytics Shaanxi Xi'an710049 China Xi'an Jiaotong University National Engineering Laboratory for Big Data Analytics The Ministry of Education Key Lab for Intelligent Networks and Network Security Shaanxi Xi'an710049 China

On-site lithium-ion battery state of health (SoH) estimation is of crucial importance for reliable operations of electric vehicles (EVs). Yet, due to the low-quality of unlabeled real-time field data, diverse operating environments of in-service EVs, and limited computational capability of onboard devices, existing techniques established on data from well-controlled experimental environments are not practical for real world EVs' SoH estimation. Accurate and rapid SoH estimation based on field data of in-service EVs still remains quite challenging. To tackle this challenge, we present an on-site SoH estimation (OSE) method using in-service EV field data through a new knowledge embedded deep transfer learning (DTL) model. Initially, an universal data preprocessing approach integrating mechanism knowledge is designed to process low-quality data under diverse operating environments. Then, we develop a domain adaptive hybrid deep neural network (DAHDNN) model suitable for unlabeled field data, which can be deployed via edge cloud collaborative framework to meet actual computational capability. We demonstrate the superiority of our method across four real datasets, where, OSE's estimation error is decreased by up to 78.5% compared with the state-of-the-art methods. The results indicate that the proposed method has good generalizability and reliability for SoH estimation on real-time field data. © 2015 IEEE.

关键词： State of charge

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New results on quantum boomerang attacks

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Quantum Information Processing 2023年第4期22卷 1-27页

作者： Zou, Hongkai Zou, Jian Luo, Yiyuan College of Computer and Data Science Fuzhou University Fuzhou China Key Lab of Information Security of Network Systems Fuzhou University Fuzhou China School of Information Sciences and Technology Huizhou University Huizhou China

At SAC 2021, Frixons et al. proposed quantum boomerang attacks that can effectively recover the keys of block ciphers in the quantum setting. Based on their work, we further consider how to quantize the generic boomerang attacks proposed by Biham et al. at FSE 2002, so as to obtain more generic quantum boomerang attacks. Similar to Frixons et al.’s work, we only consider quantum key recovery attacks in the single-key setting. With the help of some famous quantum algorithms, this paper presents two methods to convert the attacks of Biham et al. into some new quantum key recovery attacks. In order to proof our methods, we apply our new ideas to attack Serpent-256 and ARIA-196. To sum up, for Serpent-256, we give valid 9-round and 10-round quantum key recovery attacks respectively. The quantum time complexity of 9-round and 10-round of Serpent-256 is 2 115.43 and 2 126.6 respectively. Furthermore, we show a valid quantum key attack on 6-round ARIA-196 which has a time complexity of 2 89.8 with negligible memory. The time complexity of the above quantum attacks are better than the corresponding classical attacks and quantum generic key recovery attack via Grover’s algorithm. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Quantum theory

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Ramp Scheme Based on CRT for Polynomial Ring over Finite Field

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Journal of Systems Science & Complexity 2023年第1期36卷 129-150页

作者： DING Jian KE Pinhui LIN Changlu WANG Huaxiong College of Computer and Cyber Security Fujian Normal UniversityFuzhou 350007China School of Mathematics and Big Data Chaohu UniveristyHefei 238024China School of Mathematics and Statistics and Fujian Provincial Key Lab of Network Security and CryptologyFujian Normal UniversityFuzhou 350007China School of Physical and Mathematical Sciences Nanyang Technological UniversitySingapore 639798Singapore

Chinese Reminder Theorem(CRT)for integers has been widely used to construct secret sharing schemes for different scenarios,but these schemes have lower information rates than that of Lagrange interpolation-based *** ASIACRYPT 2018,Ning,et *** a perfect(r,n)-threshold scheme based on CRT for polynomial ring over finite field,and the corresponding information rate is one which is the greatest case for a(r,n)-threshold ***,for many practical purposes,the information rate of Ning,et *** is low and perfect security is too much *** this work,the authors generalize the Ning,et al.(r,n)-threshold scheme to a(t,r,n)-ramp scheme based on CRT for polynomial ring over finite field,which attains the greatest information rate(r−t)for a(t,r,n)-ramp ***,for any given 2≤r_(1)

关键词： Chinese Reminder Theorem polynomial ring ramp scheme threshold changeable secret sharing

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STGAT: A Spatio-Temporal Graph Attention network for Travel Demand Prediction

STGAT: A Spatio-Temporal Graph Attention Network for Travel ...

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2023 International Conference on networking and network Applications, NaNA 2023

作者： Zhang, Tao Yi Wang, YuBo Wei, ZhiCheng Key Lab of Network and Information Security China College of Computer and Cyberspace Security China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Security Hebei Normal University Hebei Province Shijiazhuang China

ISBN: (纸本)9798350327380

Forecasting travel demand is a challenging task due to the complex spatial dependencies and dynamic temporal correlation of the traffic data. Furthermore, a limited representation of the given spatial graph structure may restrict the model's ability to effectively learn spatial-temporal dependencies. To extract latent semantic features from traffic data, this paper proposes a method to construct a traffic graph using the Markov cluster algorithm. The traffic semantic correlation matrix is constructed based on a traffic graph to obtain the deep semantic information. Specifically, this study proposes a Spatio-Temporal Graph Attention network(STGAT) for travel demand prediction. STGAT uses a graph attention layer based on the Node2Vec graph embedding algorithm, two convolutional layers based on the Markov cluster algorithm, and a long short-term memory network to capture spatial-temporal dependencies. Experimental results on the NYC Taxi dataset and the Chengdu online car dataset demonstrate that STGAT achieved state-of-the-art performance compared to other baseline models. © 2023 IEEE.

关键词： Forecasting

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A Deep Reinforcement Learning Approach for Online Taxi Dispatching

A Deep Reinforcement Learning Approach for Online Taxi Dispa...

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2023 International Conference on networking and network Applications, NaNA 2023

作者： Wang, YuBo Zhang, TaoYi Wei, ZhiCheng Key Lab of Network and Information Security China College of Computer and Cyber Security China Hebei Prov. Eng. Res. Center for Supply Chain Big Data Analytics Security Hebei Normal University Hebei Province Shijiazhuang China

ISBN: (纸本)9798350327380

With the development of smart city transportation systems, developing reasonable dispatching strategies for idle ride-hailing vehicles has become an urgent research problem. In this paper, we address the short-sightedness issue of existing dispatching strategies and design a ride-hailing dispatching model based on deep reinforcement learning in the Markov decision process(MDP) framework to obtain long-term dispatching strategies with foresight. Firstly, we define the basic framework of agent and optimize the dispatching strategy of ride-hailing services by defining a metric that combines instant revenue with the demand situation of the next state in the action space as a reward function. Then, we construct an order-matching system simulator based on historical order data sets to facilitate interaction between the intelligent agent and the environment. Finally, based on the Bellman optimality equation, we approximate the value function using the Eval Net and Target Net, and train the model using Temporal Difference Learning. Simulation experiments demonstrate that this approach can increase driver income, provide more services to passengers, and effectively improve the operating efficiency of the transportation network. © 2023 IEEE.

关键词： Reinforcement learning

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Taxi Station Location Model Based on Spatio-Temporal Demand Cube

Taxi Station Location Model Based on Spatio-Temporal Demand ...

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2023 International Conference on networking and network Applications, NaNA 2023

作者： Gao, LuLu Li, XiaoNan Wei, ZhiCheng Key Lab of Network and Information Security Hebei Province Shijiazhuang China College of Computer and Cyber Security Hebei Province Shijiazhuang China Hebei Normal University Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Security Hebei Province Shijiazhuang China

ISBN: (纸本)9798350327380

With the widespread use of mobile apps, empty taxis can easily receive new orders without having to roam around. Therefore, it is necessary to plan taxi stations where empty taxis can park. In the location selection problem, the spatial and temporal distribution of passenger demand needs to be considered to ensure that the selected location can meet the actual needs of passengers. In this paper, a Spatio-Temporal Cube Demand Coverage Model (STC-DCM) is proposed for taxi station location selection. Firstly, a spatiotemporal demand cube is constructed to obtain fine-grained user demands based on time and space, and genetic algorithm is used to maximize the demand coverage rate. Then, the final location selection results are obtained using the fusion module and adjustment optimization module, where the fusion module integrates multiple location selection solutions using ensemble learning method. Experimental results show that our proposed model can better cover dynamic passenger demand compared to traditional models. © 2023 IEEE.

关键词： Genetic algorithms

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An enhanced genetic algorithm for computation task offloading in MEC scenario

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International Journal of Wireless and Mobile Computing 2023年第2期25卷 118-127页

作者： Zhao, Jiacheng Li, Wenzao Liu, Hantao Yu, Peizhen Li, Hanyun Wen, Zhan School of Communication Engineering Chengdu University of Information Technology Sichuan Chengdu China Chengdu University of Information Technology Sichuan Chengdu China Network and Data Security Key Lab of Sichuan Pro. University of Electronic Science and Technology of China Sichuan Chengdu China Educational Informationisation and Big Data Centre Education Department of Sichuan Province Sichuan Chengdu China

The explosive growth of Internet of Things (IoT) and 5G communication technologies has driven the increasing computing demands for wireless devices. Mobile edge computing in the 5G scenario is a promising solution for energy-efficient and low latency applications. However, due to limited bandwidth, the selection of appropriate computing tasks greatly affects the user experience and system performance. Under the wireless bandwidth constraint, the reasonable choice of offloading objects is an NP-hard problem. The genetic algorithm has a great ability to solve this problem, but the performance of the algorithm varies with different scenarios. This paper proposes a task offloading strategy based on an enhanced genetic algorithm for small-scale computing tasks with an ultra-dense terminal distribution. Numerical experiments show that the convergence speed and optimisation effect of the enhanced genetic algorithm are significantly improved compared to the conventional genetic algorithm. © 2023 Inderscience Enterprises Ltd.

关键词： Genetic algorithms

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A Novel Multi-Edge Cooperative Offloading Genetic Algorithm towards Environmental Monitoring System 5

A Novel Multi-Edge Cooperative Offloading Genetic Algorithm ...

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5th International Conference on Telecommunications and Communication Engineering, ICTCE 2022

作者： Fang, Jie Li, Wenzao Wang, Yue Sun, Xiulan College of Communication Engineering Chengdu University of Information Technology China Network and Data Security Key Lab of Sichuan Pro. University of Electronic Science and Technology of China China Institute of Data Application Research Educational Informalization and Big Data Center China

ISBN: (纸本)9781450397797

In environmental monitor architecture, the usual way of processing data is centralized. On the one hand, this leads to a long time of data processing, and with the increasing number of monitoring sensors, the pressure of data processing in data centers increases sharply. The edge computing provides a feasible solution to relieve the processing pressure and decrease the delay of data processing in data center. However, in the environmental monitor system, the edge servers are often load unbalance because of the nonuniform distribution of sensors. While, it is usually ignored in recently researches. In this paper, we proposed a cooperative offloading strategy (MECOGA) based on genetic algorithm (GA). It aims to ease the data processing pressure and reduce the data processing time in the data center. In addition, the load balance of edge servers is also one of our optimization goals. In this paper, we aim at decreasing task processing delay and improving the system load balancing. Contrast to other algorithms, our MECOGA decreases the delay near 20% before collaborative loading. And the balance variance is 15% smaller than the Balance strategy. © 2022 Association for Computing Machinery.

关键词： Genetic algorithms

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