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A Scalable Algorithm for Fair Influence Maximization with Unbiased Estimator

IEEE Transactions on Knowledge and Data engineering 2025年第7期37卷 3881-3895页

作者： Rui, Xiaobin Wang, Zhixiao Peng, Hao Chen, Wei Yu, Philip S. China University of Mining and Technology School of Computer Science Jiangsu Xuzhou221116 China Mine Digitization Engineering Research Center of the Ministry of Education Jiangsu Xuzhou221116 China Beihang University School of Cyber Science and Technology Beijing100191 China Theory Center of Microsoft Research Asia Beijing100080 China University of Illinois at Chicago Department of Computer Science ChicagoIL60607 United States

This paper studies the fair influence maximization problem with efficient algorithms. In particular, given a graph G, a community structure C consisting of disjoint communities, and a budget k, the problem asks to select a seed set S (||S|| = k) that maximizes the influence spread while narrowing the influence gap between different communities. This problem derives from some significant social scenarios, such as health interventions (e.g. suicide/HIV prevention) where individuals from racial minorities or LGBTQ communities may be disproportionately excluded from the benefits of the intervention. To depict the concept of fairness in the context of influence maximization, researchers have proposed various notions of fairness, where the welfare fairness notion that better balances fairness level and influence spread has shown promising effectiveness. However, the lack of efficient algorithms for optimizing the objective function under welfare fairness restricts its application to networks of only a few hundred nodes. In this paper, we modify the objective function of welfare fairness to maximize the exponentially weighted sum and the logarithmically weighted sum over all communities' influenced fractions (utility). To achieve efficient algorithms with theoretical guarantees, we first introduce two unbiased estimators: one for the fractional power of the arithmetic mean and the other for the logarithm of the arithmetic mean. Then, by adapting the Reverse Influence Sampling (RIS) approach, we convert the optimization problem to a weighted maximum coverage problem. We also analyze the number of reverse reachable sets needed to approximate the fair influence at a high probability. Finally, we present an efficient algorithm that guarantees 1 - 1/e - ϵ (positive objective function) or 1 + 1/e + ϵ (negative objective function) approximation for any small ϵ > 0. Experiments demonstrate that our proposed algorithm could efficiently handle large-scale networks with good performa

关键词： Approximation algorithms

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Stranger Trust Architecture: An Advancement to Zero Trust Architecture 2nd

Stranger Trust Architecture: An Advancement to Zero Trust Ar...

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2nd International Conference on cybersecurity and Evolutionary Data engineering, ICCEDE 2022

作者： Choudhary, Arjun Chahar, Arun Sharma, Aditi Tripathi, Ashish Center for Cyber Security Security and Criminal Justice Sardar Patel University of Police Jodhpur India School of Engineering and Digital Sciences Nazarbayev University Asthana Kazakhstan Department of Information Technology G. L. Bajaj Institute of Technology and Management Greater Noida India

ISBN: (纸本)9789819950799

security in ever changing environment is the need of the hour and no business or establishment can survive without a proper and suitable security mechanism. There have been several mechanisms for attending the needs of different sectors. In recent years Zero Trust Architecture has emerged as one of the promising security mechanisms to ensure the utmost privacy and setback resistant authentication mechanism. In this research paper we have tried to propose an advanced level of authentication mechanism known as the stranger trust mechanism. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Architecture

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Transfer Learning-Based Lithium-Ion Battery State of Health Estimation With Electrochemical Impedance Spectroscopy

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7910-7920页

作者： Lin, Mingqiang Hu, Dingcai Meng, Jinhao Wu, Ji Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences Quanzhou Institute of Equipment Manufacturing Jinjiang362200 China Fujian Normal University College of Computer and Cyber Security Fuzhou350007 China University of Chinese Academy of Sciences Fujian College Fuzhou350002 China Xi’an Jiaotong University School of Electrical Engineering Shaanxi Xi’an710049 China Hefei University of Technology School of Automotive and Transportation Engineering Anhui Hefei230009 China

Lithium-ion batteries are utilized as energy storage units in mobile devices, electric vehicles, and other fields. To ensure the safety and reliability of batteries, the prediction of the batteries’ state of health (SOH) is one of the key technologies. This article proposes a transfer learning-based lithium-ion battery SOH estimation method using explainable electrochemical impedance spectroscopy (EIS). EIS has advantages such as explainability, rapid response, and noninvasiveness. Benefiting these, the physical parameters are extracted as the battery aging features by fitting an equivalent circuit model with the EIS measurement. To increase the representational power of the model and capture the complex sequential styles, a spatiotemporal long short-term memory (LSTM) network model is built to extract the time series features. Finally, the battery degradation features are fit through a fully connected layer. To improve the model’s generalization, a transfer learning strategy is added to estimate the SOH of the target cell by fine-tuning the initial model parameters on different temperatures and different types of cells. The proposed method, TL-ST-LSTM, has been validated on two public datasets, with an overall root-mean-square error (RMSE) error controlled within 1.9%. Compared to the spatiotemporal LSTM (ST-LSTM) method without transfer learning, the accuracy has been improved by over 80%. In addition, it also demonstrates an improvement in accuracy compared to existing transfer learning methods, such as TL-CNN and TL-LSTM. © 2015 IEEE.

关键词： Lithium-ion batteries

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Network Intrusion Detection in Internet of Blended Environment Using Ensemble of Heterogeneous Autoencoders(E-HAE)

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computer Systems Science & engineering 2023年第9期46卷 3261-3284页

作者： Lelisa Adeba Jilcha Deuk-Hun Kim Julian Jang-Jaccard Jin Kwak ISAA Lab. Department of AI Convergence NetworkAjou UniversitySuwon16499Korea ISAA Lab. Institute for Information and CommunicationAjou UniversitySuwon16499Korea Department of Computer Science and Information Technology Massey UniversityAuckland0745New Zealand Department of Cyber Security Ajou UniversitySuwon16499Korea

Contemporary attackers,mainly motivated by financial gain,consistently devise sophisticated penetration techniques to access important information or *** growing use of Internet of Things(IoT)technology in the contemporary convergence environment to connect to corporate networks and cloud-based applications only worsens this situation,as it facilitates multiple new attack vectors to emerge *** such,existing intrusion detection systems suffer from performance degradation mainly because of insufficient considerations and poorly modeled detection *** address this problem,we designed a blended threat detection approach,considering the possible impact and dimensionality of new attack surfaces due to the aforementioned *** collectively refer to the convergence of different technology sectors as the internet of blended *** proposed approach encompasses an ensemble of heterogeneous probabilistic autoencoders that leverage the corresponding advantages of a convolutional variational autoencoder and long short-term memory variational *** extensive experimental analysis conducted on the TON_IoT dataset demonstrated 96.02%detection ***,performance of the proposed approach was compared with various single model(autoencoder)-based network intrusion detection approaches:autoencoder,variational autoencoder,convolutional variational autoencoder,and long short-term memory variational *** proposed model outperformed all compared models,demonstrating F1-score improvements of 4.99%,2.25%,1.92%,and 3.69%,respectively.

关键词： Network intrusion detection anomaly detection TON_IoT dataset smart grid smart city smart factory digital healthcare autoencoder variational autoencoder LSTM convolutional variational autoencoder ensemble learning

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Bullyproof Sentinel: Fortifying Online Spaces Against Bullying using Machine Learning 7

Bullyproof Sentinel: Fortifying Online Spaces Against Bullyi...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Dilshani, M.M.T. Vinoshan, T. Wijesinghe, W.A.S.D. Jathushan, V. Senarathne, Amila Gunasinghe, Amali Sri Lanka Institute of Information Technology Faculty of Computing Cyber Security Specialization Malabe Sri Lanka Sliit Cisco Academy Sri Lanka Institute of Information Technology Industry Engagement Unit Department of Computer Systems Engineering Manager Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350341485

In the current social media landscape, cyberbullying and online harassment remain pressing issues. Therefore, we planned a cyberbullying detection system for social media platforms. It can protect online communities from toxic behaviors and make a global impact by fostering a safer and more constructive online environment. We are mainly focused on developing an improved technique to detect bot profiles on social media, developing a mechanism to detect abusive content and sarcasm, developing a mechanism to detect defamatory comments on social media, and developing an effective method for detecting offensive and abusive content in image-format reviews on social media using machine learning algorithms. © 2023 IEEE.

关键词： Machine learning

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Application of Neural Networks for Network Traffic Monitoring and Analysis 8

Application of Neural Networks for Network Traffic Monitorin...

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8th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2024

作者： Kushnerov, Oleksandr Murr, Pierre Herasymov, Serhii Milevskyi, Stanislav Melnyk, Marharyta Golovashych, Sergii Sumy State University Department of Economic Cybernetics Sumy Ukraine International University of Science and Technology in Kuwait Computer Engineering Department Kuwait Kuwait 'Kharkiv polytechnic institute' Cybersecurity department National Technical University Kharkiv Ukraine Set University Department of Cyber Security and information protection Kyiv Ukraine National Technical University 'Kharkiv polytechnic institute' Department of Software Engineering and Management Intelligent Technologies Kharkiv Ukraine

ISBN: (纸本)9798350354423

Monitoring and analyzing network traffic are critically important tasks in the context of increasing cyber threats. Traditional data processing methods often struggle to handle the large volumes and complexity of modern network traffic, necessitating the use of advanced technologies such as neural networks and machine learning. This article demonstrates the application of neural networks for monitoring and analyzing network traffic, highlighting their ability to learn from large datasets to detect complex patterns and anomalies. Using TensorFlow, we developed a neural network model based on an autoencoder that effectively identifies and classifies anomalies in network traffic. The model's effectiveness was validated using a synthetic dataset from Kaggle, showing high accuracy on both training and testing data. Our approach significantly improves the efficiency and accuracy of threat detection compared to traditional methods, making it a valuable tool for enhancing cybersecurity. © 2024 IEEE.

关键词： cyber attacks

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CPIMAC: MEC-assisted Message Dissemination Based on Traffic Prediction at Intersections 10

CPIMAC: MEC-assisted Message Dissemination Based on Traffic ...

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10th IEEE Smart World Congress, SWC 2024

作者： Liu, Bingyi Shao, Xun Zhao, Haoxiang Wang, Enshu Liu, Zhi Wuhan University of Technology Sanya Science and Education Innovation Park Sanya China Toyohashi University of Technology Department of Electrical and Electronic Information Engineering Aichi Japan Wuhan University School of Cyber Science and Engineering Wuhan China The University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan

ISBN: (纸本)9798331520861

The Internet of Vehicles (IoV) is gaining increasing attention for its ability to improve traffic safety and efficiency by providing situational awareness and detecting potential hazards beyond the visual range. Time division multiple access (TDMA) based communication protocol effectively prevents transmission collisions in IoV. However, road intersections pose challenges for the implementation of TDMA-based MAC protocols, as vehicles converge from different directions, leading to severe communication access and merging collisions. To address this issue, we propose a distributed MAC protocol named slot collisions prediction based MAC at intersection (CPIMAC). Specifically, We combine mobile edge computing (MEC) with a CNN-BiLSTM-Attention neural network model to predict potential transmission collisions and propose corresponding collision avoidance algorithms. In addition, we design the head-tail beacon (HTB) algorithm to reduce slot collisions during vehicle waiting periods at intersections by adjusting beacon frequency. Finally, we conduct extensive experiments to evaluate the proposed protocol. The results show that transmission collisions can be reduced by 58.1 % compared to the existing protocols. © 2024 IEEE.

关键词： Time division multiple access

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An Innovative Way to Increase the Speed and Efficiency of Reverse Mapping of Elliptic Curve Points 6

An Innovative Way to Increase the Speed and Efficiency of Re...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Ray, Ryan Saptarshi Das, Debendranath Sengupta, Aritro Kumar, Pankaj Sahani, Arun Kumar Govt. College of Engineering and Ceramic Technology Information Technology Kolkata India Indian Statistical Institute Cryptology and Security Research Unit Kolkata India Lingaya's Vidyapeeth Department of Computer Science and Engineering Haryana Faridabad India

ISBN: (纸本)9798350306613

Elliptic curve cryptography is a widely recognized method of using a public key cryptosystem for encrypting and decrypting data. The message must be transformed to points on an Elliptic Curve using a mapping algorithm in order to send the message. This process of mapping the message to the point on the elliptic curve is done by applying a well-known mapping algorithm. Elliptic curve cryptography is widely acknowledged to have simpler mathematical operations compared to other cryptographic methods. However, researchers in the field of cryptography have faced challenges in mapping plain messages onto points on an elliptic curve and reversing the process at the receiver's end for decryption. Recent advancements have led to the development of mapping techniques that are resistant to frequency analysis. In this paper, we have discussed a well-known mapping scheme that has been used to map plain text messages to points on elliptic curves. However, the time taken to map a message and encrypt the points takes a substantial amount of time. This latency can be reduced if we parallelize the mapping process. In this paper, we have parallelized the reverse mapping phase of the scheme at the receiver's end to reduce the total communication time. Performance results after parallelization have been recorded and provided in the later section of the paper in tabular and graphical form. © 2024 IEEE.

关键词： Elliptic Curve Cryptography Parallel Processing Points Mapping Reverse Mapping Thread Programming

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Privacy-Preserving Edge-Aided Eigenvalue Decomposition in Internet of Things

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IEEE Internet of Things Journal 2025年第12期12卷 19901-19914页

作者： Zhao, Xiaotong Zhang, Hanlin Lin, Jie Liang, Fan Kong, Fanyu Xu, Hansong Hua, Kun Qingdao University College of Computer Science and Technology Qingdao266071 China Xian Jiaotong University School of Electronic and Information Engineering Xian710049 China Sam Houston State University Department of Computer Science HuntsvilleTX77340 United States Shandong University School of Software Jinan250101 China Shanghai Jiao Tong University School of Cyber Science and Engineering Shanghai200240 China California Polytechnic State University Department of Electrical Engineering San Luis ObispoCA93407 United States

Eigenvalue decomposition (EVD) is a fundamental yet time-consuming operation with extensive applications in IoT. When the matrix dimension reaches millions, resource-limited IoT devices struggle to perform such computationally expensive operations. Edge computing, with its plentiful computing resources, offers an effective solution to this problem. However, privacy concerns arise because outsourced tasks may contain sensitive user data. In this paper, we propose the first privacy-preserving, edge-assisted EVD outsourcing scheme that securely enables users to outsource EVD tasks to edge servers. We design a privacy-preserving matrix transformation method to encode the original data, ensuring that edge servers cannot access users' private information. Additionally, we design a verification scheme that enables the user to verify the correctness of the results returned by the edge servers. Our protocol supports parallel computation by multiple edge servers, thus enhancing the efficiency of EVD. The feasibility of our proposed scheme is demonstrated through both theoretical and experimental perspectives. © 2025 IEEE.

关键词： Differential privacy

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Novel Ransomware Hiding Model Using HEVC Steganography Approach

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computers, Materials & Continua 2022年第1期70卷 1209-1228页

作者： Iman Almomani Aala AlKhayer Walid El-Shafai Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Computer Science Department King Abdullah II School for Information TechnologyThe University of Jordan11942Jordan Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Ransomware is considered one of the most threatening *** solutions have focused mainly on discriminating ransomware by analyzing the apps themselves,but they have overlooked possible ways of hiding ransomware apps and making them difficult to be detected and then ***,this paper proposes a novel ransomware hiding model by utilizing a block-based High-Efficiency Video Coding(HEVC)steganography *** main idea of the proposed steganography approach is the division of the secret ransomware data and cover HEVC frames into different *** that,the Least Significant Bit(LSB)based Hamming Distance(HD)calculation is performed amongst the secret data’s divided blocks and cover ***,the secret data bits are hidden into the marked bits of the cover HEVC frame-blocks based on the calculated HD *** main advantage of the suggested steganography approach is the minor impact on the cover HEVC frames after embedding the ransomware while preserving the histogram attributes of the cover video frame with a high *** is due to the utilization of an adaptive steganography cost function during the embedding *** proposed ransomware hiding approach was heavily examined using subjective and objective tests and applying different HEVC streams with diverse resolutions and different secret ransomware apps of various *** obtained results prove the efficiency of the proposed steganography approach by achieving high capacity and successful embedding process while ensuring the hidden ransomware’s undetectability within the video *** example,in terms of embedding quality,the proposed model achieved a high peak signal-to-noise ratio that reached 59.3 dB and a low mean-square-error of 0.07 for the examined HEVC ***,out of 65 antivirus engines,no engine could detect the existence of the embedded ransomware app.

关键词： Ransomware embedding steganography HEVC LSB hamming distance applications apk stego security confidentiality

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