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VMCTE:Visualization-BasedMalware Classification Using Transfer and Ensemble Learning

computers, Materials & Continua 2023年第5期75卷 4445-4465页

作者： Zhiguo Chen Jiabing Cao Engineering Research Center of Digital Forensics Ministry of EducationNanjing University of Information Science and TechnologyNanjing210044China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China

The Corona Virus Disease 2019(COVID-19)effect has made telecommuting and remote learning the *** growing number of Internet-connected devices provides cyber attackers with more attack *** development of malware by criminals also incorporates a number of sophisticated obfuscation techniques,making it difficult to classify and detect malware using conventional ***,this paper proposes a novel visualization-based malware classification system using transfer and ensemble learning(VMCTE).VMCTE has a strong anti-interference *** if malware uses obfuscation,fuzzing,encryption,and other techniques to evade detection,it can be accurately classified into its corresponding malware *** traditional dynamic and static analysis techniques,VMCTE does not require either reverse engineering or the aid of domain expert *** proposed classification system combines three strong deep convolutional neural networks(ResNet50,MobilenetV1,and MobilenetV2)as feature extractors,lessens the dimension of the extracted features using principal component analysis,and employs a support vector machine to establish the classification *** semantic representations of malware images can be extracted using various convolutional neural network(CNN)architectures,obtaining higher-quality features than traditional *** fine-tuned and non-fine-tuned classification models based on transfer learning can greatly enhance the capacity to classify various families *** experimental findings on the Malimg dataset demonstrate that VMCTE can attain 99.64%,99.64%,99.66%,and 99.64%accuracy,F1-score,precision,and recall,respectively.

关键词： Malware classification ensemble learning visualization transfer learning

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Analytical Model for Performance Measures over Respiratory Track 27

Analytical Model for Performance Measures over Respiratory T...

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27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Jaiswal, Raghavendra Singh, S. Pratap Gulhane, Monali Rakesh, Nitin Singh, Rajneesh Kumar Galgotias College of Engineering and Technology Department of Electronics and Communication Engineering Greater Noida India Department of Computer Science and Engineering Pune India Sharda University Sharda School of Engineering and Technology Greater Noida India

ISBN: (纸本)9798350392319

Diffusive Molecular Communication (DMC) as a framework for modeling bacterial propagation within biological environments. DMC, a subset of Molecular Communication (MC), allows us to study how bacteria transmit signals inside the human body. The effectiveness of the model depends on the chosen deployment geometry. We focus on a cylindrical biological deployment inspired by the human respiratory tract. This model considers the degradation and flow of information molecules diffusing through the fluid medium. Furthermore, proposed statistical models for bacterial concentration f(y) Further, analyse the key performance metrics within this CBD model and present statistical models for bacterial propagation are binding probability (Pb), non-binding probability (Pnb), evade probability (Pe(y)), and the probability of binding rate (Pb(y)). Major contribution of numerical analysis to find total number of receptor bacteria at y segment N(y), and Bacterial distribution V(y). Further, the numerical results demonstrate excellent agreement with the theoretical foundation. © 2024 IEEE.

关键词： Bacterial distribution binding probability cylindrical biological deployment Diffusive Molecular Communication

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Resource-Efficient Generative AI Model Deployment in Mobile Edge Networks

Resource-Efficient Generative AI Model Deployment in Mobile ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Liang, Yuxin Yang, Peng He, Yuanyuan Lyu, Feng Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan China Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan China Central South University School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350351255

The surging development of Artificial Intelligence-Generated Content (AIGC) marks a transformative era of the content creation and production. Edge servers promise attractive benefits, e.g., reduced service delay and backhaul traffic load, for hosting AIGC services compared to cloud-based solutions. However, the scarcity of available resources on the edge pose significant challenges in deploying generative AI models. In this paper, by characterizing the resource and delay demands of typical generative AI models, we find that the consumption of storage and GPU memory, as well as the model switching delay represented by I/O delay during the preloading phase, are significant and vary across models. These multidimensional coupling factors render it difficult to make efficient edge model deployment decisions. Hence, we present a collaborative edge-cloud framework aiming to properly manage generative AI model deployment on the edge. Specifically, we formulate edge model deployment problem considering heterogeneous features of models as an optimization problem, and propose a model-level decision selection algorithm to solve it. It enables pooled resource sharing and optimizes the trade-off between resource consumption and delay in edge generative AI model deployment. Simulation results validate the efficacy of the proposed algorithm compared with baselines, demonstrating its potential to reduce overall costs by providing feature-aware model deployment decisions. © 2024 IEEE.

关键词： Generative adversarial networks

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CiC-NET: a real-time semantic segmentation network for dam surface crack detection

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Multimedia Tools and Applications 2024年 1-23页

作者： Li, Linjing Zhao, Hao Liu, Ran Nayyar, Anand Ali, Rashid Li, Yonglong Zhang, Hua School of Information Engineering Southwest University of Science and Technology Mianyang China University of Science and Technology of China Heifei China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore School of Computer Science Duy Tan University Da Nang550000 Viet Nam Department of Computer Science University of Turbat Turbat Pakistan Department of Electronic Engineering Tsinghua University Beijing China Tianfu Institute of Research and Innovation Southwest University of Science and Technology Chengdu China

Crack detection is vital for maintaining hydraulic engineering infrastructure. However, achieving a balance between real-time processing and high precision in semantic segmentation models presents a significant challenge, especially given the intricate details of cracks and complex backgrounds. To tackle this issue, this paper proposes a real-time, high-precision crack segmentation model. Initially, a four-branch feature extraction structure is devised to capture the edge details of cracks, with a focus on enhancing segmentation accuracy. Subsequently, an image pyramid is constructed at the input end to feed small-scale samples into high-dimensional feature extraction branches, thereby reducing computational costs and improving segmentation speed. Finally, an effective feature fusion module is designed for the feature extraction structure to capture sufficient crack features and achieve precise crack localization. Extensive experiments validate the superior performance of the proposed method, with Pixel Accuracy, Recall, Intersection over Union, and F1 score reaching 68.45%, 67.04%, 51.22%, and 67.74%, respectively, while maintaining a modest parameter count of 13.12M. This model provides a reliable and efficient solution for the health monitoring and maintenance of hydraulic engineering. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantic Segmentation

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Robust Vehicle Detection Based on Improved You Look Only Once

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computers, Materials & Continua 2023年第2期74卷 3561-3577页

作者： Sunil Kumar Manisha Jailia Sudeep Varshney Nitish Pathak Shabana Urooj Nouf Abd Elmunim Department of Computer Science Banasthali VidyapithRajasthan304022India Department of Computer Science&Engineering School of Engineering&TechnologySharda UniversityGreater Noida201310India Department of Information Technology Bhagwan Parshuram Institute of Technology(BPIT)GGSIPUNew Delhi110078India Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.BOX 84428Riyadh11671Saudi Arabia

Vehicle detection is still challenging for intelligent transportation systems(ITS)to achieve satisfactory *** existing methods based on one stage and two-stage have intrinsic weakness in obtaining high vehicle detection *** to advancements in detection technology,deep learning-based methods for vehicle detection have become more popular because of their higher detection accuracy and speed than the existing *** paper presents a robust vehicle detection technique based on Improved You Look Only Once(RVD-YOLOv5)to enhance vehicle detection *** proposed method works in three phases;in the first phase,the K-means algorithm performs data clustering on datasets to generate the classes of the ***,in the second phase,the YOLOv5 is applied to create the bounding box,and the Non-Maximum Suppression(NMS)technique is used to eliminate the overlapping of the bounding boxes of the ***,the loss function CIoU is employed to obtain the accurate regression bounding box of the vehicle in the third *** simulation results show that the proposed method achieves better results when compared with other state-of-art techniques,namely LightweightDilated Convolutional Neural Network(LD-CNN),Single Shot Detector(SSD),YOLOv3 and YOLOv4 on the performance metric like precision,recall,mAP and *** simulation and analysis are carried out on PASCAL VOC 2007,2012 and MS COCO 2017 datasets to obtain better performance for vehicle ***,the RVD-YOLOv5 obtains the results with an mAP of 98.6%and Precision,Recall,and F1-Score are 98%,96.2%and 97.09%,respectively.

关键词： Image-processing K-means clustering CNN YOLOv5 loss function deep-learning

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Examining Unsupervised Learning Techniques for Economic Forecasting 5th

Examining Unsupervised Learning Techniques for Economic Fore...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Negi, Bichitra Singh Minish, T. N. Sterlin Mishra, Awakash Karthikeyan, M. Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India

ISBN: (纸本)9789819780426

This newsletter examines a ramification of unsupervised mastering strategies for financial forecasting. The number one consciousness has been using strategies inclusive of k-suggest clustering and principal thing evaluation (PCA) to derive meaning from facts and generate forecasts for macroeconomic indicators consisting of gross home product (GDP) and stability of bills. We also discuss using neural networks (NNs) as software for unsupervised learning for monetary forecasting. Via a sequence of empirical experiments, we analyze and examine the forecasts generated through every unsupervised, gaining knowledge of methods for various macroeconomic indicators. The outcomes of the experiments suggest that NNs and PCA are most effective for monetary forecasting and discover that PCA is a promising tool for making forecasts with more excellent accurate effects than okay-manner. Moreover, the results offer insights on a way to use unsupervised learning for financial forecasting and suggest ability ways in which to enhance the accuracy of those forecasts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decentralized finance

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Deep Multi-View Clustering via View-Specific Representation and Global Graph 7

Deep Multi-View Clustering via View-Specific Representation ...

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7th Asian Conference on Artificial Intelligence technology, ACAIT 2023

作者： Pu, Jingyu Cui, Chenhang Ren, Yazhou Wen, Zichen Wu, Tianyi Pu, Xiaorong He, Lifang University of Electronic Science And Technology of China School of Computer Science And Engineering China University of Electronic Science And Technology of China School of Computer Science And Engineering Shenzhen Institute For Advanced Study China Lehigh University Department of Computer Science And Engineering Bethlehem United States

ISBN: (纸本)9798350359145

Multi-view clustering can improve clustering performance by leveraging the complementary information from multiview data and has garnered growing interest. Despite significant advancements has been established in this field, most existing methods primarily concentrate on learning the latent representation, neglecting the latent graph that typically reveals relationships between different instances. To address this limitation, this paper presents a new framework of Deep Multi-View Clustering via View-specific Representation and Global Graph (DVGMVC) to address the aforementioned issue. Our method leverages the latent graph of each view to enhance representation quality and incorporates a global graph that combines discriminative information to enhance the performance of deep embedded clustering. Specifically, we employ autoencoders to learn embedded features and construct latent graphs from each view. Subsequently, a weighted two-layer plain residual network refines the latent feature using topological information derived from the latent graphs. As various views adhere to the same foundational cluster structure across multiple views, DVGMVC merges the embedded features of all views to create a unified feature. By leveraging the Graph Convolutional Network (GCN) module, which exploits information from both the latent graphs and the global graph generated from the global feature, a unified embedded feature is generated to capture comprehensive topological information among samples. This unified embedded feature is then employed to generate pseudo labels in a self-supervised manner, guiding unified embedding learning and clustering. In a self-supervised manner, such pseudo-label information guides unified embedding learning and clustering. In-depth experiments on public datasets has proven the state-of-the-art clustering effectiveness of our proposed method. © 2023 IEEE.

关键词： Embeddings

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Internet of Things: An Aggregated Proof based Secure Healthcare Monitoring System- Comparative Analysis 6

Internet of Things: An Aggregated Proof based Secure Healthc...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Aswathy, R.H. Suresh, P. Jenifa, G. Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Thandalam Chennai India Madanapalle Institute of Technology & Science Andhra Pradesh Madanapalle India

ISBN: (纸本)9798331522667

The healthcare monitoring system plays a crucial role in remote monitoring. A highly secure healthcare system utilizing advanced cryptographic methods to protect sensitive information. The system integrates a multifactor authentication framework combined with aggregated proof mechanisms to enhance security. A detailed security analysis has been conducted to assess its effectiveness against various cyber threats. Compared to existing solutions, the proposed model demonstrates exceptional resilience to numerous known attacks, including Session Key Computation, Password Guessing, User & Gateway Node Impersonation, Replay Attacks, Sensor Node Spoofing, and Privileged Insider Attacks. These features ensure robust protection for healthcare data, addressing critical security concerns in modern applications. By leveraging the encryption and authentication strategies, the system provides a dependable and secure environment for the healthcare industry. This approach not only strengthens data privacy but also ensures operational integrity, making it a comprehensive solution for addressing current and emerging security challenges. © 2025 IEEE.

关键词： Authentication Protocol

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Developing Efficient Game Theory Approaches for Spectrum Sharing in 6G Networks 5th

Developing Efficient Game Theory Approaches for Spectrum Sha...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Ramola, Abhishek C. Francis, Jerrin Joe Savita Murugan, R. Department of Electrical Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

This paper provides the development of green game idea procedures for spectrum sharing in upcoming 6G cellular networks. As dynamic spectrum get entry to keeps to advantage commercial interest, the want to broaden a sustainable framework to make sure powerful and relaxed spectrum sharing arises. To this stop, sport principle methods are seen as a legitimate candidate to facilitate robust spectrum sharing in 6G networks. This paper offers unique attention to the area of spectrum sharing and how the underlying game theory procedures may be leveraged to percentage spectrum efficiently. After an intensive review of existing methods and strategies, an overall performance evaluation of the numerous strategies is provided. Subsequently, the paper gives insights at the potential of game concept strategies in future 6G networks. Moreover, much game theoretic equipment are used for analytical as well as simulation experiments if you want to higher apprehend the dynamics of the spectrum sharing. Basic, this paper examines the effectiveness of each analytical and simulation-based totally game theoretic fashions for spectrum sharing in 6G networks, and gives a new version which may be used to improve the spectrum sharing dynamics in such networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Spread spectrum communication

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A Quantum Q-Learning Model for the Capacitated Vehicle Routing Problem 17

A Quantum Q-Learning Model for the Capacitated Vehicle Routi...

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17th IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip, MCSoC 2024

作者： Xu, Jiahua Xie, Ningyi Lee, Xinwei Saito, Yoshiyuki Asai, Nobuyoshi Cai, Dongsheng University of Tsukuba Graduate School of Science and Technology Japan Singapore Management University School of Computing and Information Systems Singapore University of Aizu Graduate School of Computer Science and Engineering Japan University of Aizu School of Computer Science and Engineering Japan University of Tsukuba Faculty of Engineering Information and Systems Japan

ISBN: (纸本)9798331530471

The research on Variational Quantum Algorithms (VQAs) has gained significant momentum because of their promising practicality in the noisy intermediate-scale quantum (NISQ) era. Recent studies highlight the potential of VQAs in reinforcement learning (RL) by replacing classical components with parameterized quantum circuits (PQCs). Building on this, we propose a quantum Q-learning model for the Capacitated Vehicle Routing Problem (CVRP), combining a PQC with an RL training process. We present a PQC for embedding both static and dynamic states in RL environments. The proposed PQC approximates the Q-value function by using the expectation values of different observables. Experimental results demonstrate that the proposed PQC outperforms the existing PQC for the CVRP with fewer quantum resources. Furthermore, our method shows competitive performance compared to the classical counterpart. © 2024 IEEE.

关键词： Vehicle routing

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