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4th International Conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2023

作者： Li, Jie Wang, Zuling Hangzhou Normal University Department of Computer Science Hangzhou China Chongqing Vocational Institute of Safety & Technology Department of Network and Information Security Chongqing China

ISBN: (纸本)9798350329490

Image splicing, a prevalent form of image tampering with potential security implications, has prompted the need for reliable models to detect and locate manipulated regions. While convolutional neural network (CNN) based algorithms have shown promise, they often lack global modeling capabilities. In this paper, we propose an end-to-end image splicing and localization network based on a visual Transformer to address the uneven modeling of long-range dependencies between image pixels in existing algorithms. The model incorporates an encoder-decoder architecture that captures pixel relationships and local continuity, leveraging both shallow and deep features through skip connections. Multi-scale features are combined to enhance the accuracy of tampered area localization. The loss function utilizes weighted cross-entropy to locate splicing areas of various sizes and shapes. Evaluation on four public datasets demonstrates significant performance improvements over existing methods, with F1 scores increasing by 33.3%, 21.1%, 9.5%, and 5.4% on the IMD2020, CASIA2.0, Columbia, and NIST16 datasets, respectively. Furthermore, the model exhibits robustness to various post-processing operations. These findings highlight the efficacy of the proposed approach in addressing the challenges associated with image splicing localization. The developed model holds promise for enhancing security measures in image forensics and related applications. © 2023 IEEE.

关键词： Image enhancement

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Intermittent Fault Diagnosis of Split-Star networks and its Applications

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IEEE Transactions on Parallel and Distributed Systems 2023年第4期34卷 1253-1264页

作者： Song, Jiankang Lin, Limei Huang, Yanze Hsieh, Sun-Yuan Fujian Normal University College of Computer and Cyber Security Key Laboratory of Network Security and Cryptology Fuzhou Fujian350117 China Fujian University of Technology School of Computer Science and Mathematics Fuzhou Fujian350118 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

With the rapid increase of the number of processors in multiprocessor systems and the fast expansion of interconnection networks, the reliability of interconnection network is facing severe challenges, where the fast recognition of fault processors is crucial. In practice, most of the processor failures are intermittent faults. In this article, we first determine the intermittent fault diagnosability t I PMC(S n 2) tIPMC(Sn2) of n-dimensional split-star network Sn2 under the PMC model. In addition, we propose a fast intermittent fault probabilistic diagnosis algorithm FIFPDPMC to identify the nodes with intermittent fault in the n-dimensional split-star network Sn2 under the PMC model, and we calculated the time complexity of the algorithm FIFPDPMC. Then we implement the algorithm FIFPDPMC in the IoT-based wireless sensor network (IoTWSN) and a randomly generated network (RGN) under different number of nodes with intermittent fault, and we evaluate the performance and efficiency of the algorithm FIFPDPMC in terms of accuracy, precision, recall (TPR), F1, G-mean, FPR, TNR and FNR. Experimental results show that, as the number of stages of executing the algorithm FIFPDPMC increases, the number of nodes with intermittent fault being diagnosed by the algorithm FIFPDPMC increases, which implies that the algorithm FIFPDPMC has good performance and efficiency in both IoTWSN and RGN. © 1990-2012 IEEE.

关键词： Fault detection

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Customer Retention Using Machine Learning 10

Customer Retention Using Machine Learning

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer Engineering, UPCON 2023

作者： Das, Lipsa Salman, Rahama Sabeer, Shaista Ansari, Subuhi Kashif Aarif, Mohd Rana, Ajay Amity School of Engineering and Technology Amity University UP Greater Noida India Jazan University Department of Computer Science Saudi Arabia Global Research Network UP Noida India College of Computer Science & Information Technology Jazan University Department of Information Technology and Security Jazan Saudi Arabia College of Computer Science and Information Technology & Security Jazan University Jazan Saudi Arabia

ISBN: (纸本)9798350382471

Maintaining customer retention stands as a pivotal element in ensuring business success, given that the costs associated with acquiring new customers often surpass those linked to retaining existing ones. Recognizing the significance of retaining a loyal customer base, businesses prioritize strategies and initiatives that cultivate lasting relationships with their current clientele, understanding that sustained customer loyalty contributes substantially to long-term viability and profitability.. Machine learning techniques offer a promising approach to predicting customer behavior and developing personalized retention strategies. In this research paper, we explore the use of machine learning for customer retention, specifically focusing on predictive modeling and recommendation systems. We review recent literature on the topic, discussing different machine learning algorithms and techniques that have been applied to customer retention. We also present a case study of a company that has successfully implemented machine learning-based retention strategies, highlighting the benefits and challenges of this approach. Our findings suggest that machine learning can be an effective tool for improving customer retention, but its success depends on several factors, including data quality, model accuracy, and implementation strategy. Our conclusion involves an exploration of the consequences of our research for forthcoming studies and practical applications within business environments.. © 2023 IEEE.

关键词： customer behavior Customer retention machine learning predictive modelling

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A Novel Meta-Hierarchical Active Inference Reinforcement Learning Approach for QoS-Driven Resource Allocation in Dynamic Clouds

A Novel Meta-Hierarchical Active Inference Reinforcement Lea...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Xian, Peijie He, Ying Yu, F. Richard Du, Jianbo Shenzhen Univ. College of Computer Science and Software Engineering Shenzhen China Xi'an Univ. of Posts & Telecomm. Shaanxi Key Lab. of Information Comm. Network and Security Shaanxi Xi'an China Carleton Univ. School of Information Technology OttawaON Canada

ISBN: (纸本)9798350351255

The cloud computing environment is highly dynamic due to a variety of external factors such as seasonal changes, market trends, and social events. In this case, tenant behavior patterns exhibit significant variability. Therefore, the cloud computing resource allocation model must continuously adapt to these changes. To this end, we propose an adaptive algorithm for dynamic cloud resource allocation based on meta-hierarchical active inference reinforcement learning (MHAIRL). The algorithm combines active inference with meta-hierarchical reinforcement learning. It improves the overall performance of the algorithm, as well as quickly adapts to environmental changes, and improves the generalization performance. In addition, we design a novel polling scheduling framework combined with long short-term memory (LSTM) network. The framework ensures scheduling fairness and flexibility while greatly reducing the state and action space dimensions of the agent. Extensive simulation results show that our method outperforms baseline algorithms in quality of service (QoS) metrics and significantly improves system performance in highly dynamic cloud resource allocation. © 2024 IEEE.

关键词： Active learning

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Machine Learning-Based Malware Detection and Classification in Encrypted TLS Traffic 1

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Second International Conference on security, Privacy and Data Analytics, ISPDA 2022

作者： Kashyap, Himanshu Pais, Alwyn Roshan Kondaiah, Cheemaladinne Information Security Research Lab Department of Computer Science and Engineering National Institute of Technology Karnataka Karnataka Surathkal575025 India

ISBN: (数字)9789819935697

ISBN: (纸本)9789819935680

Malware has become a significant threat to Internet users in the modern digital era. Malware spreads quickly and poses a significant threat to cyber security. As a result, network security measures play an important role in countering these cyber threats. Existing malware detection techniques are unable to detect them effectively. A novel Ensemble Machine Learning (ML)-based malware detection technique from Transport Layer security (TLS)-encrypted traffic without decryption is proposed in this paper. The features are extracted from TLS traffic. Based on the extracted features, malware detection is performed using Ensemble ML algorithms. The benign and malware file datasets are created using features extracted from TLS traffic. According to the experimental results, the 65 new extracted features perform well in detecting malware from encrypted traffic. The proposed method achieves an accuracy of 99.85% for random forest and 97.43% for multiclass classification for identifying malware families. The ensemble model achieved an accuracy of 99.74% for binary classification and 97.45% for multiclass classification. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cryptography

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Early Detection of Autism in Children Using Transfer Learning

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Intelligent Automation & Soft Computing 2023年第4期36卷 11-22页

作者： Taher M.Ghazal Sundus Munir Sagheer Abbas Atifa Athar Hamza Alrababah Muhammad Adnan Khan School of Information Technology Skyline University CollegeSharjah1797UAE Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and Technology Universiti Kebangsaan Malaysia43600Malaysia School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Lahore Garrison University Lahore54000Pakistan Department of Computer Science COMSATS University Islamabad–Lahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnamGyeonggido13120Korea

Autism spectrum disorder(ASD)is a challenging and complex neurodevelopment syndrome that affects the child’s language,speech,social skills,communication skills,and logical thinking *** early detection of ASD is essential for delivering effective,timely *** facial features such as a lack of eye contact,showing uncommon hand or body movements,bab-bling or talking in an unusual tone,and not using common gestures could be used to detect and classify ASD at an early *** study aimed to develop a deep transfer learning model to facilitate the early detection of ASD based on facial fea-tures.A dataset of facial images of autistic and non-autistic children was collected from the Kaggle data repository and was used to develop the transfer learning AlexNet(ASDDTLA)*** model achieved a detection accuracy of 87.7%and performed better than other established ASD detection ***,this model could facilitate the early detection of ASD in clinical practice.

关键词： Autism spectrum disorder convolutional neural network loss rate transfer learning AlexNet deep learning

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Novel Architecture of security Orchestration, Automation and Response in Internet of Blended Environment

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computers, Materials & Continua 2022年第10期73卷 199-223页

作者： Minkyung Lee Julian Jang-Jaccard Jin Kwak ISAA Lab Department of Cyber SecurityAjou UniversitySuwon16499Korea Department of Computer Science and Information Technology Massey UniversityAuckland0745New Zealand Department of Cyber Security Ajou UniversitySuwon16499Korea

New technologies that take advantage of the emergence of massive Internet of Things(IoT)and a hyper-connected network environment have rapidly increased in recent *** technologies are used in diverse environments,such as smart factories,digital healthcare,and smart grids,with increased security *** intend to operate security Orchestration,Automation and Response(SOAR)in various environments through new concept definitions as the need to detect and respond automatically to rapidly increasing security incidents without the intervention of security personnel has *** facilitate the understanding of the security concern involved in this newly emerging area,we offer the definition of Internet of Blended Environment(IoBE)where various convergence environments are interconnected and the data analyzed in *** define Blended Threat(BT)as a security threat that exploits security vulnerabilities through various attack surfaces in the *** propose a novel SOAR-CUBE architecture to respond to security incidents with minimal human intervention by automating the BT response *** security Orchestration,Automation,and Response(SOAR)part of our architecture is used to link heterogeneous security technologies and the threat intelligence function that collects threat data and performs a correlation analysis of the *** is operated under Collab.rative Units of Blended Environment(CUBE)which facilitates dynamic exchanges of data according to the environment applied to the IoBE by distributing and deploying security technologies for each BT type and dynamically combining them according to the cyber kill chain stage to minimize the damage and respond efficiently to BT.

关键词： Blended threat(BT) collab.rative units for blended environment(CUBE) internet of blended environment(IoBE) security orchestration,automation and response(SOAR)

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Deep Imputation Bi-stochastic Graph Regularized Matrix Factorization for Clustering Single-cell RNA-sequencing Data

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2024年 PP卷 1-11页

作者： Lan, Wei Chen, Jianwei Liu, Mingyang Chen, Qingfeng Liu, Jin Wang, Jianxin Chen, Yi-Ping Phoebe Wei Lan is School of Computer Electronic and Information and Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning Guangxi China School of Computer Electronic and Information Guangxi University Nanning Guangxi China School of Computer Electronic and Information and State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources Guangxi University Nanning Guangxi China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha Hunan China Department of Computer Science and Information Technology La Trobe University Melbourne Victoria Australia

By generating massive gene transcriptome data and analyzing transcriptomic variations at the cell level, single-cell RNA-sequencing (scRNA-seq) technology has provided new way to explore cellular heterogeneity and functionality. Clustering scRNA-seq data could discover the hidden diversity and complexity of cell populations, which can aid to the identification of the disease mechanisms and biomarkers. In this paper, a novel method (DSINMF) is presented for single cell RNA sequencing data by using deep matrix factorization. Our proposed method comprises four steps: first, the feature selection is utilized to remove irrelevant features. Then, the dropout imputation is used to handle missing value problem. Further, the dimension reduction is employed to preserve data characteristics and reduce noise effects. Finally, the deep matrix factorization with bi-stochastic graph regularization is used to obtain cluster results from scRNA-seq data. We compare DSINMF with other state-of-the-art algorithms on nine datasets and the results show our method outperformances than other methods. IEEE

关键词： RNA

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A Caching Strategy Based on Many-to-Many Matching Game in D2D networks

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Tsinghua science and technology 2021年第6期26卷 857-868页

作者： Kaihang Yu Zhonggui Ma Runyu Ni Tao Zhang School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Key Lab of Information Network Security Ministry of Public SecurityShanghai 201204China

Wireless edge caching has been proposed to reduce data traffic congestion in backhaul links, and it is being envisioned as one of the key components of next-generation wireless networks. This paper focuses on the influences of different caching strategies in Device-to-Device(D2D) networks. We model the D2D User Equipments(DUEs) as the Gauss determinantal point process considering the repulsion between DUEs, as well as the caching replacement process as a many-to-many matching game. By analyzing existing caching placement strategies, a new caching strategy is proposed, which represents the preference list of DUEs as the ratio of content popularity to cached probability. There are two distinct features in the proposed caching strategy.(1) It can cache other contents besides high popularity contents.(2) It can improve the cache hit ratio and reduce the latency compared with three caching placement strategies: Least Recently Used(LRU), Equal Probability Random Cache(EPRC), and the Most Popular Content Cache(MPC). Meanwhile, we analyze the effect of caching on the system performance in terms of different content popularity factors and cache capacity. Simulation results show that our proposed caching strategy is superior to the three other comparison strategies and can significantly improve the cache hit ratio and reduce the latency.

关键词： caching strategy Device-to-Device(D2D)networks Gauss determinantal point process many-to-many matching game

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Joint Optimization of Compression, Transmission and Computation for Cooperative Perception Aided Intelligent Vehicular networks

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IEEE Transactions on Vehicular technology 2025年第5期74卷 8201-8214页

作者： Lu, Binbin Huang, Xumin Wu, Yuan Qian, Liping Zhou, Sheng Niyato, Dusit University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science 999078 China Guangdong University of Technology School of Automation Guangzhou510006 China University of Macau State Key Lab of Internet of Things for Smart City 999078 China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer Information Science 999078 China Zhuhai UM Science and Technology AQ2 Research Institute Zhuhai519301 China Zhejiang University of Technology Institute of Cyberspace Security Hangzhou310023 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing100084 China Nanyang Technological University School of Computer Science and Engineering 639815 Singapore

Cooperative perception is a promising paradigm to tackle the perception limitations of a single intelligent vehicle (IV) to enhance the driving safety and efficiency in intelligent vehicular networks. However, the real-time transmission and computation-intensive fusion of raw sensing data raise new challenges for satisfying the stringent delay requirement of delay-sensitive applications. In this paper, we formulate a joint optimization problem of the cooperative IV selection, compression ratio selection, transmit power control, task offloading decision and computation allocation to minimize the end-to-end delay consisting of compression, transmission and computation. In particular, to meet the perception probability requirement, the quality and quantity-aware matching algorithm is proposed to optimize the cooperative IV selection. To guarantee the queue stability of offloading tasks, we develop the Lyapunov optimization algorithm to determine the upper bound of the offloading tasks and the corresponding optimal computation allocation. The Lyapunov aided deep reinforcement learning algorithm is further proposed to dynamically adjust the task offloading decision and compression ratio selection to minimize the end-to-end delay with the transmit power control being optimized by the successive convex approximation algorithm. Simulation results demonstrate that, compared to several benchmark algorithms, our proposed algorithm achieves the lowest end-to-end delay while guaranteeing the requirements on perception probability and queue stability effectively. © 1967-2012 IEEE.

关键词： Deep reinforcement learning

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