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2021 IEEE International Conference on Power Electronics, computer Applications, ICPECA 2021

作者： Yihan, Wang Yongzhen, Li Yanbian University Network Communication and Information Security Lab Dept. of Computer Science and Technology Yanji China

ISBN: (纸本)9781728190037

To address issues in security the threat posed to the DES algorithm by brute force cracking, as well as the low efficiency of the 3DES algorithm, I propose extending the length of the DES algorithm's key. First, change the block length of the DES algorithm from 64 bits to 128 bits and expand the key length of the DES algorithm to double length;secondly, iterative encryption is performed according to the new plaintext grouping and the expanded key length. The two groups are iteratively encrypted, and after every single iteration the two sets of R part data obtained from this round of encryption are exchanged, and then the next round of iteration;finally, the improved DES algorithm is compared with the DES algorithm and the 3DES algorithm by experiment. The results showing that the operating efficiency of the improved DES algorithm is better than 3DES algorithm. Therefore, the improved algorithm is proposed here greatly improves the security of the algorithm under the premise of ensuring operating efficiency and has good application value. © 2021 IEEE.

关键词： Cryptography

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Smart Energy Management System Using Machine Learning

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computers, Materials & Continua 2024年第1期78卷 959-973页

作者： Ali Sheraz Akram Sagheer Abbas Muhammad Adnan Khan Atifa Athar Taher M.Ghazal Hussam Al Hamadi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science COMSATS University IslamabadLahore CampusLahore54000Pakistan School of Information Technology Skyline University CollegeSharjah1797United Arab Emirates Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia College of Engineering and IT University of DubaiDubaiUnited Arab Emirates

Energy management is an inspiring domain in developing of renewable energy ***,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and *** major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental *** energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user *** address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve *** inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy *** Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)*** IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource *** this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management *** proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

关键词： Intelligent energy management system smart cities machine learning

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Attribute-Consistent Knowledge Graph Representation Learning for Multi-Modal Entity Alignment 23

Attribute-Consistent Knowledge Graph Representation Learning...

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2023 World Wide Web Conference, WWW 2023

作者： Li, Qian Guo, Shu Luo, Yangyifei Ji, Cheng Wang, Lihong Sheng, Jiawei Li, Jianxin School of Computer Science and Engineering Bdbc Beihang University Beijing China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing China Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security Ucas Beijing China Scse Beihang University Zhongguancun Lab Beijing China

ISBN: (纸本)9781450394161

The multi-modal entity alignment (MMEA) aims to find all equivalent entity pairs between multi-modal knowledge graphs (MMKGs). Rich attributes and neighboring entities are valuable for the alignment task, but existing works ignore contextual gap problems that the aligned entities have different numbers of attributes on specific modality when learning entity representations. In this paper, we propose a novel attribute-consistent knowledge graph representation learning framework for MMEA (ACK-MMEA) to compensate the contextual gaps through incorporating consistent alignment knowledge. Attribute-consistent KGs (ACKGs) are first constructed via multi-modal attribute uniformization with merge and generate operators so that each entity has one and only one uniform feature in each modality. The ACKGs are then fed into a relation-aware graph neural network with random dropouts, to obtain aggregated relation representations and robust entity representations. In order to evaluate the ACK-MMEA facilitated for entity alignment, we specially design a joint alignment loss for both entity and attribute evaluation. Extensive experiments conducted on two benchmark datasets show that our approach achieves excellent performance compared to its competitors. © 2023 ACM.

关键词： Knowledge graph

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Double-Blockchain Assisted Secure and Anonymous Data Aggregation for Fog-Enabled Smart Grid

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Engineering 2022年第1期8卷 159-169页

作者： Siguang Chen Li Yang Chuanxin Zhao Vijayakumar Varadarajan Kun Wang Jiangsu Key Lab of Broadband Wireless Communication and Internet of Things Nanjing University of Posts and TelecommunicationsNanjing 210003China Jiangsu Engineering Research Center of Communication and Network Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China Anhui Provincial Key Laboratory of Network and Information Security Anhui Normal UniversityWuhu 241000China School of Computer Science and Engineering Vellore Institute of TechnologyChennai 600127India Department of Electrical and Computer Engineering University of CaliforniaLos AngelesCA 90095USA

As a future energy system,the smart grid is designed to improve the efficiency of traditional power systems while providing more stable and reliable ***,this efficient and reliable service relies on collecting and analyzing users’electricity consumption data frequently,which induces various security and privacy *** address these challenges,we propose a double-blockchain assisted secure and anonymous data aggregation scheme for fog-enabled smart grid named ***,we design a three-tier architecture-based data aggregation framework by integrating fog computing and the blockchain,which provides strong support for achieving efficient and secure data collection in smart ***,we develop a secure and anonymous data aggregation mechanism with low computational overhead by jointly leveraging the Paillier encryption,batch aggregation signature and anonymous *** particular,the system achieves fine-grained data aggregation and provides effective support for power dispatching and price adjustment by the designed double-blockchain and two-level data ***,the superiority of the proposed scheme is illustrated by a series of security and computation cost analyses.

关键词： Blockchain Fog computing Homomorphic encryption Smart grid Anonymity

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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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International Conference on networking and network Applications (NaNA)

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

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.

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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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International Conference on networking and network Applications (NaNA)

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

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.

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Intelligent Model to Predict Early Liver Disease using Machine Learning Technique

Intelligent Model to Predict Early Liver Disease using Machi...

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2022 International Conference on Business Analytics for Technology and security, ICBATS 2022

作者： Ghazal, Taher M. Rehman, Aziz Ur Saleem, Muhammad Ahmad, Munir Ahmad, Shabir Mehmood, Faisal Faculty of Information Science and Technology Ukm Network and Communication Technology Lab Center for Cyber Security Malaysia School of Computer Science NCBAandE Lahore Pakistan Gachon University Department of Computer Engineering Korea Republic of Gachon University Department of It Convergence Engineering Korea Republic of

ISBN: (纸本)9781665409209

Liver Disease (LD) is the main cause of death worldwide, affecting a large number of people. A variety of factors affect the liver, resulting in this disease. The diagnosis of this condition is both expensive and time-consuming. Machine Learning offers a lot of potential in terms of automated disease diagnosis. As a result, the purpose of this research is to assess the efficacy of various Machine Learning (ML) algorithms to lower the high cost of liver disease diagnosis through prediction. With the current rise in numerous liver disorders, it's more important than ever to detect liver disease early on. This research proposed intelligent model to predict liver disease using machine learning technique. This proposed model is more effective and comprehensive in terms of performance of 0.884 accuracy, and 0.116 miss-rate. © 2022 IEEE.

关键词： Forecasting

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An Ensemble of CapsNet and KSVM Using Feature Fusion: Application to COVID-19 Detection 3

An Ensemble of CapsNet and KSVM Using Feature Fusion: Applic...

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3rd International Conference on Pattern Recognition and Machine Learning, PRML 2022

作者： Aggrey, Esther Stacy E. B. Zhen, Qin Kodjiku, Seth Larweh Aidoo, Evans Fiasam, Linda Delali Mensah, Acheampong Edward University of Electronic Science & Technology of China School of Information & Software Engineering Chengdu China University of Electronic Science & Technology of China School of Information & Software Engineering Network and Data Security Key Lab of Sichuan Province Chengdu China Zhejiang Gongshang University School of Computer & Information Engineering Hangzhou China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

COVID-19 virus is a major worldwide pandemic that is growing at a fast pace throughout the world. The usual approach for diagnosing COVID-19 is the use of a real-time polymerase chain reaction (RT-PCR) based nucleic acid test. However, RT-PCR has lower sensitivity in the early phases of COVID-19 detection. Recent studies have indicated that X-ray images may be useful throughout the early detection of the virus. Human screening has been shown to be cost-effective, susceptible to mistakes, and time-demanding, which has sparked an interest in using Convolutional Neural networks (CNNs) to automate the process. CNNs, on the other hand, fail to view the exact placement of features as advantageous in medical imaging. Furthermore, for successful training and prediction, CNNs need a huge quantity of datasets. CNNs are rapidly reducing picture resolution, resulting in worsening accuracy in classification. We used newly created capsule networks (CapsNets) in our study to circumvent these disadvantages. The primary contribution is to improve the identification of SARS-CoV-2 with images obtained from X-ray by coupling capsule network with a kernel support vector machine (KSVM). The technique was evaluated using a publicly available dataset, and the proposed model shows that the accuracy of the CapsNet-KSVM based model is improved by 94.6% accuracy, 95% sensitivity, and 98% specificity, which outperforms the traditional CNN and other existing ensemble models. The proposed CapsNet-KSVM based system can be employed to identify the presence of COVID-19 in the human body using X-ray images. © 2022 IEEE.

关键词： COVID-19

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Trajectory Privacy Protection with Pricing Awareness on Ride-on-Demand System 10

Trajectory Privacy Protection with Pricing Awareness on Ride...

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10th IEEE International Conference on Cyber security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Jia, Sihui Long, Saiqin Zheng, Zhirun Deng, Qingyong Wang, Ping Tian, Shujuan Xiangtan University School of Computer Science and School of Cyberspace Science Hunan International Scientific and Technological Cooperation Base of Intelligent network Xiangtan China Jinan University School of Information Science and Technology Guangzhou China Xiangtan University School of Mathematics and Compulational Science Xiangtan China Guangxi Normal University Guangxi Key Lab of Multi-source Information Mining and Security Guilin China

ISBN: (纸本)9798350312461

With the widespread use of the Ride-on Demand (RoD) system, many privacy issues have been exposed, and there is growing concern about whether private information will be leaked. For this problem, our previous work addressed the issue of the user's initial and final location leakage and provided a strong utility guarantee in the RoD system. Further, the trajectory information is also important in the RoD system, it could contain a lot of private information about the user, such as health or identity, so it's important to publish a distorted and productive trajectory. For this purpose, in this paper, we provide our trajectory protection method with pricing awareness based on previous work;the method uses supply and demand density function to guide the division of a discrete spatial grid, then uses the Markov chain to generate distorted trajectories on the grid to ensure trajectory continuity, and makes corresponding defenses against several attacks, such as Bayesian. The experiment results on real-world datasets prove the validity and robustness of the method. © 2023 IEEE.

关键词： Costs

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Task Offloading and Resource Allocation Based on Reinforcement Learning and Load Balancing in Vehicular networking

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IEEE Transactions on Consumer Electronics 2025年

作者： Tian, Shujuan Xiang, Shuhuan Zhou, Ziqi Dai, Haipeng Yu, Enze Deng, Qingyong Xiangtan University Key Laboratory of Hunan Province for Internet of Things and Information Security Hunan International Scientific and Technological Cooperation Base of Intelligent network School of Computer Science and School of Cyberspace Security Xiangtan411105 China Nanjing University School of Computer Science and Technology Nanjing211189 China Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-source Information Mining and Security Guilin541004 China

Due to limited on-board resources and the mobility characteristics of vehicles in a multi-access edge computing (MEC)-based vehicular network, efficient task offloading and resource allocation schemes are essential for achieving low-latency and low-energy consumption applications in the Internet of Vehicles (IoV). The spatial distribution of vehicles, influenced by various factors, leads to significant workload variations across MEC servers. In this paper, we address task offloading and resource allocation as a joint optimization problem and propose a Load-Balancing Deep Deterministic Policy Gradient (LBDDPG) algorithm to achieve optimal results. The joint optimization problem is modeled as a Markov Decision Process (MDP), enabling the LBDDPG algorithm to systematically address the challenges of workload imbalance and resource inefficiency. The algorithm incorporates a load optimization strategy to balance workload distribution across MEC servers, mitigating disparities caused by uneven vehicle distributions. The reward function is designed to account for both energy consumption and delay, ensuring an optimal trade-off between these critical factors. To enhance training efficiency, a noise-based exploration strategy is employed, preventing ineffective exploration during the early stages. Additionally, constraints such as computational capacity and latency thresholds are embedded to ensure the algorithm's practical applicability. Experimental results demonstrate that the proposed LBDDPG algorithm achieves faster convergence and superior performance in terms of energy consumption and latency compared to other reinforcement learning algorithms. © 1975-2011 IEEE.

关键词： Markov processes

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