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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Kotha, Aditya Venkanna, U. International Institute of Information Technology Department of Computer Science and Engineering Naya Raipur India National Institute of Technology Department of Computer Science and Engineering Warangal India

ISBN: (纸本)9798350391725

Autonomous vehicles (AVs) increasingly rely on vehicle-to-everything (V2X) networks for communication. However, due to the devices' heterogeneity, they are more susceptible to attacks like distributed denial of service (DDoS), resulting in packet loss and transmission delays. Existing solutions to these issues often create bottlenecks and lack post-attack recovery mechanisms. To solve these problems, this paper proposes RAPID-AV, an ML-based approach that uses programmable P4 switches for real-time detection and mitigation of DDoS attacks in AV networks. The proposed system uses a threefold strategy: collect traffic statistics from AV, use P4-based programmable packet processing for anomaly detection, and use majority voting to mitigate attacks. In our experiments, we found that the decision tree algorithm achieves an accuracy of 94.33% in distinguishing traffic from DDoS traffic. In addition, RAPID-AV demonstrated a nearly 10% reduction in CPU load during attacks and maintained consistent communication between AV and RSU. This approach can help improve AV network security while addressing the limitations of existing solutions. © 2024 IEEE.

关键词： Network security

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Adjustable Model Compression Using Multiple Genetic Algorithm

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IEEE Transactions on Multimedia 2023年 25卷 1125-1132页

作者： Ople, Jose Jaena Mari Huang, Tai-Ming Chiu, Ming-Chih Chen, Yi-Ling Hua, Kai-Lung National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106 Taiwan

Generative Adversarial Networks (GAN) is a popular machine learning method that possesses powerful image generation ability, which is useful for different multimedia applications (e.g., photographic filters, image editing). However, typical GAN models have a large memory footprint that limits their practical applications for resource-constrained devices (e.g., smartphones). To deploy GAN models on devices with various hardware constraints, we propose our method, AdjustableGAN, which can compress a pretrained GAN model to different compression ratios. Our method compresses GAN by performing filter-wise pruning that follows these objectives: (1) deactivate convolutional filters for minimal performance decrease, (2) reactivate convolutional filters for maximal performance increase. We implement multiple Genetic Algorithms (GA) to perform each of these objectives - Downsize GA for best filter deactivations, while Upsize GA searches for best filter reactivations. By selective utilization of Upsize/Downsize GA, we could explicitly control the compression ratio of the model. For finalization, we fine-tune the compressed output model using the training dataset of the original input model. Our experimental results show that our method can reliably compress generative networks with minimal accuracy drop compared to other state-of-the-art compression algorithms. © 1999-2012 IEEE.

关键词： Generative adversarial networks

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A dual hashing-based authentication and secure data transmission scheme for vehicular cloud environment using MECC with optimal resource allocation mechanism

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Soft Computing 2024年第17-18期28卷 10423-10437页

作者： Varalakshmi, J. Dhanasekaran, S. Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil India Department of Information Technology Kalasalingam Academy of Research and Education Krishnankoil India

Vehicle cloud computing (VCC) is a recent area of study that blends vehicular networks with cloud computing, offering networking and sensor capabilities to vehicles for interaction with other vehicles and roadside infrastructure. Integrating unidentified vehicles and infrastructure through a public network has raised concerns about security and privacy in VCC. Several researchers have proposed secure authentication mechanisms to address these issues and ensure secure data transmission in VCC. However, exchanging vehicle data in a vehicular cloud (VC) system presents specific challenges, including maintaining data privacy and implementing lightweight authentication procedures. Therefore, this study proposes an efficient authentication and secure data transfer mechanism for VC systems. The proposed approach utilizes Dual Hashing-based Secure Hashing Algorithm-384 (DHS384) and Modified Elliptical Curve Cryptography (MECC). An optimal resource allocation (RA) scheme is introduced to allocate VC resources optimally to a suitable virtual machine using the Improved Harris Hawks Optimization (IHHO) algorithm. Simulation results demonstrate that the proposed method outperforms existing approaches and enhances security. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Resource allocation

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PULSE: Progressive Utilization of Log-Structured Techniques to Ease SSD Write Amplification in B-epsilon-tree 25

PULSE: Progressive Utilization of Log-Structured Techniques ...

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30th Asia and South Pacific Design Automation Conference, ASP-DAC 2025

作者： Peng, Huai-De Chen, Yi-Shen Chen, Tseng-Yi Chang, Yuan-Hao Department of Computer Science and Information Engineering National Central University Taiwan Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taiwan Institute of Information Science Academia Sinica Taiwan

ISBN: (纸本)9798400706356

During the unprecedented expansion of global data, efficient storage solutions are essential for processing massive datasets stored on modern storage devices. B-epsilon-tree (Bϵ-tree) is one of the most well-known techniques that provides a write-optimized structure for database file systems. With the excellent access performance and high energy efficiency of solid-state drives (SSDs), they are expected to yield promising outcomes for large-scale data computation. However, their integration into storage systems has the challenges of write amplification, which impacts SSD endurance and reliability. This work identifies significant write amplification issues with Bϵ-tree implementations on SSDs due to the complicated management of key-value pairs. To mitigate the impact of write amplification, we propose PULSE, a novel scheme that rethinks Bϵ-tree designs by leveraging log-structured techniques optimized for SSDs. Moreover, PULSE integrates auxiliary indexing and a dual flush selector to minimize write amplification. Experimental results demonstrate that PULSE significantly mitigates write amplification by more than 62.6% on SSDs for the representative benchmarks compared to the Bϵ-tree. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Flash-based SSDs

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QR Code-Based Visual Secret Sharing Scheme: A Review

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第4期9卷 2642-2661页

作者： Chu, Shu-Chuan Liu, Tao Pan, Jeng-Shyang Information Engineering College Guangzhou City Construction College Guangzhou510925 China School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

Visual secret sharing (VSS) scheme is the technology that divides a secret into shares. The decoding operation on the share is performed to restore the secret image. The quick response (QR) code is an image carrying the information. The standard of it is public. When it was obtained, anyone can decode the QR code. The VSS scheme and QR code are combined to improve the security of daily life. They can enhance the security of mobile payments, authentication, privacy data, cheater prevention, and so on. This study mainly aims to summarize previous research achievements. Comparative analysis of relevant results is conducted to illustrate the future research direction. © 2024, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： information hiding

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Predicting and Forecasting Metal Stock Prices Using Machine Learning 6

Predicting and Forecasting Metal Stock Prices Using Machine ...

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6th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024

作者： Bhavana, Katakam Varma, Indukuri Raghu Rohitha, Kalluri Suryanarayana, G. Anvesh, Kachi Kruthika, Sama Department of Information Technology Hyderabad India Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331505790

Machine Learning algorithms analyze past market data to forecast future trends in metal stock prices. Precise estimation of metal stock values is essential for industry stakeholders, policy makers, and investors. Correctly predicting market trends can be challenging as there are large number of underlying factors that affect shifts in the price of stocks. Autoregressive Integrated Moving Average (ARIMA) and Random Forest algorithm are two techniques used to predict prices of different metal genres. Non-linear correlations between features and underlying temporal patterns in the data can both be captured using ARIMA and Random Forest. A preliminary process is taken to guarantee the quality of the historical stock data, which includes transaction volume, opening and closing prices, and technical indicators. Random Forest model use both historical data and supplementary features, ARIMA models are trained on time-series data to extract temporal patterns. The models are able to learn from important aspects influencing metal equity prices in addition to the intrinsic time-series characteristics. To improve forecast accuracy, this includes outside variables such as geopolitical events, economic indicators, and market structures. The above technique offers a thorough grip of the market dynamics affecting metal genres prices by combining technical and fundamental analysis. The initiative intends to give investors with the skills and knowledge required to make good decisions in the ever-changing metal stock market environment by combining cutting-edge machine learning algorithms with domain expertise and outside data sources. These models aim to capture the tangled relationships between market variables and historical price data to detect patterns and trends that direct future price fluctuations. © 2024 IEEE.

关键词： Commerce

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Exploring the Potential of Deep Learning Algorithms in Medical Image Processing: A Comprehensive Analysis 5th

Exploring the Potential of Deep Learning Algorithms in Medic...

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5th International Conference on Communication and Intelligent Systems, ICCIS 2023

作者： Pal, Ganesh Prasad Pal, Raju Department of Computer Science and Engineering Jaypee Institute of Information Technology Noida India Department of Computer Science and Engineering School of Information and Communication Technology Gautam Buddha University Greater Noida India

ISBN: (纸本)9789819720781

In this study, we comprehensively examine the potential of deep learning algorithms in the domain of medical image processing. Through a systematic analysis of existing literature, we explore the applications, methodologies, and outcomes of these algorithms across diverse medical specialties. Our analysis underscores the transformative impact of convolutional neural networks and transfer learning techniques in enhancing diagnostic accuracy, image segmentation, and disease detection. We discuss challenges such as data variability, ethics, and interpretability while emphasizing the importance of interdisciplinary collaboration. By synthesizing findings and identifying future directions, we highlight the promising role of deep learning in revolutionizing healthcare diagnostics and treatment planning. After conducting a thorough analysis of various literature and empirical studies, we have evaluated the capabilities and drawbacks of deep learning models in managing different medical imaging modalities such as X-rays, MRIs, and CT scans. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 377-390页

作者： Honghao Zhu MengChu Zhou Yu Xie Aiiad Albeshri IEEE School of Computer and Information Engineering Bengbu UniversityBengbu 233030China School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China Helen and John C.Hartmann the Department of Electrical and Computer EngineeringNew Jersey Institute of TechnologyNewark NJ 07102 USA College of Information Engineering Shanghai Maritime UniversityShanghai 201306China Department of Computer Science King Abdulaziz UniversityJeddah 21481Saudi Arabia

A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.

关键词： Credit card fraud detection(CCFD) dandelion algorithm(DA) feature selection normal sowing operator

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Anonymous and robust biometric authentication scheme for secure social IoT healthcare applications

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Journal of engineering and Applied science 2024年第1期71卷 8页

作者： Arpitha, T. Chouhan, Dharamendra Shreyas, J. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

In the era of rapid technological advancement, the Internet of Things (IoT) has revolutionised healthcare through systems like the Telecare Medicine information System (TMIS), designed to streamline patient-doctor interactions and enhance medical treatment. However, the transmission of sensitive patient data over inherently insecure Internet channels exposes it to a spectrum of security risks. Protecting patient medical privacy and ensuring system reliability necessitate mutual authentication between both patients and medical servers. TMIS relies on robust authentication mechanisms, and combining passwords and smart cards has been a recognised approach for mutual authentication. This research introduces an innovative three-factor authentication technique with perfect forward secrecy by leveraging the power of Elliptic Curve Cryptography (ECC) in tandem with smart cards. Additionally, we have incorporated biometric authentication with a Fuzzy Extractor technology to enhance the security and reliability of the system, setting a new standard for user authentication within the realm of Social IoT healthcare. The use of ECC in the method is justified due to its compact key size and robust security measures, making the solution both efficient and secure. The proposed method safeguards user privacy by permitting registered users to change their passwords without divulging their identity to the server. The Burrows–Abadi–Needham logic (also known as the BAN logic) serves as a proof-of-concept for the proposed scheme’s security. Our system provides privacy protection along with mutual authentication and session key negotiation at a considerably low computation cost and communication cost of up to 71.03% compared to the other four relevant techniques, making it more useful in real-world scenarios. © 2023, The Author(s).

关键词： Smart cards

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Unified Approach Integrating Machine Learning Algorithms for Detection of e-Mail Phishing Attacks 1

Unified Approach Integrating Machine Learning Algorithms for...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Mathur, Abeer Dubey, Abhinav Sudhakar Mahara, Pranav Singh Gandhi, Usha Devi School of Computer Science & Engineering Vellore Institute of Technology Department of Software Systems Vellore India School of Computer Science Engineering & Information Systems Vellore Institute of Technology Department of Information Technology Vellore India

ISBN: (纸本)9798350353648

With the ever-rising risk of phishing attacks, which capitalize on vulnerable human behavior in the contemporary digital space, requires new cybersecurity methods. This literary work contributes to the solution by novel incorporation of three techniques: Support Vector Machine (SVM), Natural Language Processing (NLP) and Random Forest (RF). Utilizing NLP for deep analysis of text in phishing e-mails, SVM for strong categorization and Random Forest's decision making by group helps in improving detection. This combination of language and pattern analysis along with group learning builds a fortified system. This approach differs from prior work on a variety of factors such as reliability, security, integrity, scalability, interpretability, transparency, and robustness. Thorough evaluation on a reliable dataset aimed to demonstrate the method's effectiveness, which resulted in 98.7 % of accuracy in detection. This majorly aims at showcasing the potential of ensemble techniques to combat against cyber-attacks like phishing for laying a strong foundation for a secure digital environment. © 2024 IEEE.

关键词： Phishing

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