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26th International Conference on Advanced Communications technology, ICACT 2024

作者： Mateen, Abdul Mahmood, Khawaja Tahir Nam, Seung Yeob Federal Urdu University of Arts Science and Technology Department of Computer Science Islamabad45570 Pakistan Yeungnam University Department of Information and Communication Engineering Gyeongsan Korea Republic of

ISBN: (纸本)9791188428120

It is difficult to handle the database (DB) workload due to the huge increase in data., the functionality demand from the user., and the rapid changes in data. It is not easy to manage the DB workload., which therefore leads to malnourishment. To get efficient results., there must be complete knowledge about the type and changes in workload. The versatility and complexity of DBMSs led the DB researchers towards new philosophy and thoughts. A novel approach is introduced for DB workload management through characterization., scheduling., and database idleness detection. In workload characterization., workload is observed., and effective workload characterization parameters are selected. After that., scheduling is performed in order to arrange the DB workload to reduce the waiting time for each workload. Lastly., database idleness is identified at run-time and exploited for system as well as user-initiated workloads to improve efficiency. The proposed approach for workload management is validated through experiments using benchmark workloads. © 2024 Global IT Research Institute - GIRI.

关键词： Database systems

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Analyzing the Effect of Observer Node Addition Strategy on Bitcoin Double-Spending Attack Detection Using Graph Neural Network 24

Analyzing the Effect of Observer Node Addition Strategy on B...

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24th Asia-Pacific Network Operations and Management Symposium, APNOMS 2023

作者： Kang, Changhoon Woo, Jongsoo Hong, James Won-Ki Department of Computer Science and Engineering POSTECH Korea Republic of Graduate School of Information Technology POSTECH Korea Republic of

ISBN: (纸本)9788995004395

Bitcoin transactions are created through the concept called Unspent Transaction Output (UTXO). Users put their own UTXOs as inputs into a transaction for Bitcoin transfer and create multiple outputs, each specifying the recipient’s wallet address and the amount to be sent. UTXO refers to an output that has not been used as an input for any transaction yet and each UTXO can only be used as an input once. However, attempting to use a UTXO more than once is called a double-spending attack. Although double-spending in Bitcoin is ultimately impossible due to the system structure, it can occur when a transaction is deemed confirmed and off-chain goods or services are provided before sufficient transaction finality is guaranteed. We consider an attempt of double-spending attack when a UTXO used as an input in a transaction for payment exists together with another transaction on the Bitcoin network that uses the same UTXO as an input. In previous research, we randomly deployed observer nodes on the Bitcoin network and proposed a method to detect double-spending attacks using transaction data in the memory pool and a graph neural network model. In this paper, we analyze the impact of adding observer nodes to the Bitcoin network on the performance of graph neural network-based Bitcoin double-spending attack detection. We conducted experiments to examine the performance differences among three strategies for adding observer nodes. However, it was difficult to compare clear differences due to the performance degradation of the model caused by the differences in graph structure between datasets. Therefore, we provide an analysis of the causes and suggestions for improvement. Copyright 2023 KICS.

关键词： Graph neural networks

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Methods for Detecting Android Malware: Employing Mobile Devices to Improve Procedures for Inquiry-Based Learning

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第6期19卷 103-114页

作者： Yelisetti, Sandeep Suresh, P. Jha, Suresh Kumar Arora, Neha Anand, Ketan Sailaja, Vedala Naga Information Technology Velagapudi Ramakrishna Siddhartha Engineering College Deemed to be University Andhra Pradesh Vijayawada India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamil Nadu Thandalam Chennai India Manipal University Rajasthan Jaipur India School of Information Science Presidency University Karnataka Bengaluru India Telangana Hyderabad India Dept of MBA KLEF Deemed to be University Green Fields Andhra Pradesh Vaddeswaram India

The operating system (OS) of a computer controls both its hardware and software. It handles necessary functions including input and output processing, file and memory management, and peripheral device management, including disc drives and printers. Programs created for particular purposes are referred to as application software. These programs, which are frequently open source and freely accessible, contribute to the growing number of downloads. This paper discusses the basics of Android malware, its evolution, and malware analysis tools and techniques. Providing the research gaps and giving a review of the literature on Android malware detection using machine learning and deep learning are its main objectives. It offers the knowledge gathered from the literature as well as potential avenues for future research, which may aid in the development of reliable and precise methods for classifying Android malware. This paper conducts a systematic and comprehensive assessment of the methods and tools utilized for the analysis, classification, and detection of malicious Android apps. Several research gaps are indicated based on the thorough literature evaluation. Additionally, the report offers insights on future research paths that may aid academics in developing novel and reliable methods for identifying and categorizing Android malware. © 2025 by the authors of this article.

关键词： Android malware

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DBNode: A Decentralized Storage System for Big Data Storage in Consortium Blockchains 9

DBNode: A Decentralized Storage System for Big Data Storage ...

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9th International Conference on Big Data Analytics, ICBDA 2024

作者： Dadkhah, Narges Ma, Xuyang Wolter, Katinka Wunder, Gerhard Freie Universität Berlin Department of Mathematics and Computer Science Berlin Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350352962

Storing big data directly on a blockchain poses a substantial burden due to the need to maintain a consistent ledger across all nodes. Numerous studies in decentralized storage systems have been conducted to tackle this particular challenge. Most state-of-the-art research concentrates on developing a general storage system that can accommodate diverse blockchain categories. However, it is essential to recognize the unique attributes of a consortium blockchain, such as data privacy and access control. Beyond ensuring high performance, these specific needs are often overlooked by general storage systems. This paper proposes a decentralized storage system for Hyperledger Fabric, which is a well-known consortium blockchain. First, we employ erasure coding to partition files, subsequently organizing these chunks into a hierarchical structure that fosters efficient and dependable data storage. Second, we design a two-layer hash-slots mechanism and a mirror strategy, enabling high data availability. Third, we design an access control mechanism based on a smart contract to regulate file access. © 2024 IEEE.

关键词： Decentralized control

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Detection and Classification of Novel Attacks and Anomaly in IoT Network using Rule based Deep Learning Model

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SN computer science 2024年第8期5卷 1-15页

作者： Chakraborty, Sanjay Pandey, Saroj Kumar Maity, Saikat Dey, Lopamudra Department of Computer and Information Science (IDA) REAL AIICS at Linköping University Linköping Sweden Department of Computer Science and Engineering Techno International New Town Kolkata India Department of Computer Engineering and Applications GLA University Mathura India Department of Computer Science and Engineering Sister Nivedita University Kolkata India Department of Computer Science and Engineering Meghnad Saha Institute of Technology Kolkata India Department of Biomedical and Clinical Sciences Linköping University Linköping Sweden

Attackers are now using sophisticated techniques, like polymorphism, to change the attack pattern for each new attack. Thus, the detection of novel attacks has become the biggest challenge for cyber experts and researchers. Recently, anomaly and hybrid approaches are used for the detection of network attacks. Detecting novel attacks, on the other hand, is a key enabler for a wide range of IoT applications. Novel attacks can easily evade existing signature-based detection methods and are extremely difficult to detect, even going undetected for years. Existing machine learning models have also failed to detect the attack and have a high rate of false positives. In this paper, a rule-based deep neural network technique has been proposed as a framework for addressing the problem of detecting novel attacks. The designed framework significantly improves respective benchmark results, including the CICIDS 2017 dataset. The experimental results show that the proposed model keeps a good balance between attack detection, untruthful positive rates, and untruthful negative rates. For novel attacks, the model has an accuracy of more than 99%. During the automatic interaction between network devices (IoT), security and privacy are the primary obstacles. Our proposed method can handle these obstacles efficiently and finally identify, and classify the different levels of threats. A brief set of comparisons on detection accuracy and time measurements with some state-of-the-art methods are discussed in this paper. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Anomaly detection Classification Deep Learning Machine Learning Network-attack

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Book Genre Classification System Using Machine Learning Approach: A Survey 5th

Book Genre Classification System Using Machine Learning Appr...

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5th International Conference on Soft Computing and Signal Processing, ICSCSP 2022

作者： Sethy, Abhisek Rout, Ajit Kumar Gunda, Chandra Routhu, Sai Kiran Kallepalli, Srinu Garbhapu, Geetha Department of Information Technology GMR Institute of Technology Srikakulam Rajam India Department of Computer Science & Engineering Silicon Institute of Technology Bhubaneswar India

ISBN: (纸本)9789811986680

The amount of complicated texts and documents that require a deeper understanding of machine learning techniques has expanded rapidly in recent decades. Several machine learning approaches have shown exceptional results in NLP. Complex models and non-linear data correlations help these learning systems function well. It is difficult for academics to discover appropriate text classification structures, designs, and procedures. Manually reading and classifying a book's category would be tedious. It takes a long time for a language beginner to absorb the complete text in order to discern its genre. Natural Language Processing (NLP), which is commonly used nowadays, can help overcome this issue. It includes text classification and summarization. The book synopsis is input to the Machine Learning algorithms, which output the genre. This review includes text feature extraction, dimensionality reduction, algorithms, and evaluation. Finally, the limitations of each strategy are assessed, as well as their practicality. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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Agent Selection Framework for Federated Learning in Resource-Constrained Wireless Networks

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 1265-1282页

作者： Raftopoulou, Maria Da Silva, Jose Mairton B. Litjens, Remco Vincent Poor, H. Van Mieghem, Piet Uppsala University Department of Information Technology Uppsala751 05 Sweden Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft2628 CD Netherlands Department of Networks The Hague2595 DA Netherlands Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States

Federated learning is an effective method to train a machine learning model without requiring to aggregate the potentially sensitive data of agents in a central server. However, the limited communication bandwidth, the hardware of the agents and a potential application-specific latency requirement impact how many and which agents can participate in the learning process at each communication round. In this paper, we propose a selection metric characterizing each agent's importance with respect to both the learning process and the resource efficiency of its wireless communication channel. Leveraging this importance metric, we formulate a general agent selection optimization problem, which can be adapted to different environments with latency or resource-oriented constraints. Considering an example wireless environment with latency constraints, the agent selection problem reduces to the 0/1 Knapsack problem, which we solve with a fully polynomial approximation. We then evaluate the agent selection policy in different scenarios, using extensive simulations for an example task of object classification of European traffic signs. The results indicate that agent selection policies which consider both learning and channel aspects provide benefits in terms of the attainable global model accuracy and/or the time needed to achieve a targeted accuracy level. However, in scenarios where agents have a limited number of data samples or where the latency requirement is very stringent, a pure learning-based agent selection policy is shown to be more beneficial during the early or late stages of the learning process. © 2023 CCBY.

关键词： Federated learning

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Research on Amplification Algorithm of Small Sample Soil Composite Data Based on Probability Distribution 7

Research on Amplification Algorithm of Small Sample Soil Com...

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7th International Conference on computer Network, Electronic and Automation, ICCNEA 2024

作者： Zhang, Shuyue Liang, Xiangyang Song, Yang School of Computer Science and Engineering Xi’an Technological University Xi’an China Shaanxi Kaizhou Information Technology Co. Ltd Xi’an China

Analyzing and studying the data of soil compounds from 10 cities, two important features were found in this project. First, the data for all compounds in soil follow a normal distribution with different parameters. Therefore, this paper argues that small-sample soil compound amplification data should also have these two characteristics. Based on this, this paper proposes a new algorithm called PDWCC (probability distribution with correlation coefficient) for amplification of small-sample soil compound data. The algorithm combines a probability distribution with a Pearson correlation coefficient. In addition, an optimized genetic algorithm incorporating a mutation operator was introduced to improve the efficiency of data amplification. Finally, experimental comparisons were made with conventional Bootstrap and SMOTE amplification algorithms using SVM classifiers. The results show that the PDWCC amplification algorithm outperforms the other algorithms in terms of classification accuracy, recall and F1 value with mean values of 81.77%, 79.34% and 78.65%, respectively. © 2024 Institute of Physics Publishing. All rights reserved.

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Open-Vocabulary Calibration for Fine-tuned CLIP 41

Open-Vocabulary Calibration for Fine-tuned CLIP

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41st International Conference on Machine Learning, ICML 2024

作者： Wang, Shuoyuan Wang, Jindong Wang, Guoqing Zhang, Bob Zhou, Kaiyang Wei, Hongxin Department of Statistics and Data Science Southern University of Science and Technology Shenzhen China Department of Computer and Information Science University of Macau Taipa China William & Mary WilliamsburgVA United States School of Computer Science and Engineering University of Electronic Science and Technology of China China Department of Computer Science Hong Kong Baptist University Hong Kong

Vision-language models (VLMs) have emerged as formidable tools, showing their strong capability in handling various open-vocabulary tasks in image recognition, text-driven visual content generation, and visual chatbots, to name a few. In recent years, considerable efforts and resources have been devoted to adaptation methods for improving the downstream performance of VLMs, particularly on parameter-efficient fine-tuning methods like prompt learning. However, a crucial aspect that has been largely overlooked is the confidence calibration problem in fine-tuned VLMs, which could greatly reduce reliability when deploying such models in the real world. This paper bridges the gap by systematically investigating the confidence calibration problem in the context of prompt learning and reveals that existing calibration methods are insufficient to address the problem, especially in the open-vocabulary setting. To solve the problem, we present a simple and effective approach called Distance-Aware Calibration (DAC), which is based on scaling the temperature using as guidance the distance between predicted text labels and base classes. The experiments with 7 distinct prompt learning methods applied across 11 diverse downstream datasets demonstrate the effectiveness of DAC, which achieves high efficacy without sacrificing the inference speed. Our code is available at https://***/mlstat-Sustech/CLIP Calibration. Copyright 2024 by the author(s)

关键词： Calibration

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Design and Implementation of an Astute Infant Monitoring System Based on YOLO v8 Algorithm 15

Design and Implementation of an Astute Infant Monitoring Sys...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Arunkumar, T. Maheswaran, S. Dineshkumar, P. Geetha, K. Sureshkumar, A. Praveenkumar, S. Kongu Engineering College Department of Computer Science and Engineering Tamilnadu Perundurai India Kongu Engineering College Department of Electronics and Communication Engineering Tamilnadu Perundurai India K.S.Rangasamy college of Technology Department of Information Technology Tamilnadu Tiruchengode India Excel Engineering College Department of Computer Science and Engineering Tamilnadu Namakkal India

ISBN: (纸本)9798350370249

Parents today have a greater workload because they are working hard at their careers. As a result, there is less opportunity of giving babies better care. Children's health may suffer greatly as a result of this. Even if the infants are appropriately and consistently monitored, there is a risk of losing life. For this, we use cameras to monitor the external conditions. The system is designed to monitor and alert the baby emotions to the parents. The monitoring of baby's motion, emotions and surrounding is done by the camera that is fixed in an angle that it can cover the baby to the maximum extend. The camera that are fixed turn according to the baby's movement, so that it continuously monitors the baby. The system will alert the user if there in some negative emotion given by the baby. The system is trained in a way that it monitors the surroundings around the baby carefully. (eg: fire). This is done using YOLO v8. YOLO v8 is an anchor-free model. © 2024 IEEE.

关键词： Neonatal monitoring

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