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Preventing 51% Attack by Using Consecutive Block Limits in Bitcoin

IEEE Access 2024年 12卷 77852-77869页

作者： Mahmood Babur, Sohail Ur Rehman Khan, Shafiq Yang, Jing Chen, Yen-Lin Soon Ku, Chin Yee Por, Lip Government Murray College Department of Information Technology Sialkot51010 Pakistan Capital University of Science and Technology Department of Computer Science Islamabad44730 Pakistan Universiti Malaya Department of Computer System and Technology Kuala Lumpur50603 Malaysia National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

In permissionless blockchain systems, Proof of Work (PoW) is utilized to address the issues of double-spending and transaction starvation. When an attacker acquires more than 50% of the hash power of the entire network, they gain the ability to engage in double-spending activities, posing a significant threat to the PoW consensus algorithm. This research focuses on the consensus algorithm employed in the Bitcoin system, explaining how it operates and the security challenges it faces. The proposed modification to the PoW algorithm imposes a restriction on miners: they are not allowed to accept consecutive blocks from the same miner into the final local blockchain to prevent the 51% attack problem. This modification supports transactions that require six confirmations. In the event an attacker attempts a 51% attack with a private chain that consists of fewer than 6 blocks, it becomes easier to detect a double-spending attack before accepting the attacker's private chain. The modified algorithm introduces a 'Safe Mode Detection Algorithm' that scrutinizes incoming blocks for adjustments at the top of the local blockchain. If inconsistencies are identified, the consensus algorithm proceeds cautiously by comparing the UTXO dictionaries from the attacker's chain with those from the miner's own blockchain. This meticulous comparison aims to detect instances of double-spending. If such instances are detected, the miner rejects the attacker's chain, establishing a double-spend-free environment and thwarting 51% attacks. © 2013 IEEE.

关键词： Blockchain

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The use of bigdata in the management of library resources: Nigeria university expriences

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Business information Review 2024年第3期41卷 134-140页

作者： Akanbi, M.L. Sulaiman, Kabir Alabi Omolola, Maliq Hammed Nallah, Bilqees Jumia Department Library and Information Science Faculty of Communication and Information Science University of Ilorin Department Library and Information Science Faculty of Information and Communication Technology Kwara State University Library Kwara State University Malete

Services of libraries have to be accessible to all members of the community;this requires well situated library infrastructure, facilities and information resources as well as relevant technologies. Therefore, this study explored the use of big data in the management of library resources in university libraries. Four objectives were proposed for the study to describe the use of bigdata in managing resources of university library. In addition, ethical concern related to the use of bigdata was also identified in the paper. Descriptive literature review was used as methodology. The study conclude that Bigdata as a tool in university libraries is an important mechanism that is meant to protect library information resources for use. The nature of the library resources call for more protection especially resources are ICT in nature. However, the important of Bigdata in any organization including library become an imperative to resources management. The nature of services delivery in this present age in university library calls for the use of Bigdata so that right information resources can be provided to right people at tight time and place. Based on the concluding part, the study recommends that awareness should be provided by university libraries and management on the opportunities of Bigdata to library clienteles and training, conference and workshop should be orgnise by library and university management in order to equip library personnel with the needed skills and knowledge on better use of Bigdata in service provision. © The Author(s) 2024.

关键词： Academic library bigdata library library resources

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An enhanced AMBTC for color image compression using color palette

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Multimedia Tools and Applications 2024年第11期83卷 31783-31803页

作者： Xiong, Lizhi Zhang, Mengtao Yang, Ching-Nung Kim, Cheonshik School of Computer Science Nanjing University of Information Science and Technology Nanjing China Department of Computer Science and Information Engineering National Dong Hwa University Hualien Taiwan Department of Computer Engineering Sejong University Seoul Korea Republic of

Digital image has been used in various fields as an essential carrier. Many color images have been constantly produced since their more realistic description, which takes up much storage space and network bandwidth. Thus, color image compression has become an essential key technology. Absolute Moment Block Truncation Coding (AMBTC) has been widely studied as one of the classical image compression methods. However, in the existing methods, the visual quality of the reconstructed images and the compression rate are all relatively low. Therefore, this paper proposes an enhanced AMBTC for color image compression using a color palette. In the proposed method, the K-means clustering algorithm is utilized for training the image's palette pattern. The color palette obtained by K-mean will be more suitable for reconstructing this image than the standard color palette, and the visual quality will be higher. The six clustered central pixels are matched with the palette through a color difference formula, and the obtained index values are used as the quantization levels. Huffman coding is used to build a bitmap to achieve a higher compression rate, that is, a lower bit rate. At last, a block of a color image can be represented by six index values and a bitmap. Experimental results and theoretical analysis demonstrate that the proposed method has better visual quality and bit rate than similar schemes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Color

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Enhancing aviation safety: Machine learning for real-time ADS-B injection detection through advanced data analysis

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Alexandria Engineering Journal 2025年 126卷 262-276页

作者： Rahman, Md. Atiqur Bhuiyan, Touhid Ali, M. Ameer Department of Computer Science and Engineering East West university Dhaka Bangladesh School of Information Technology Washington University of Science and Technology Alexandria United States Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh

Airplanes play a critical role in global transportation, ensuring the efficient movement of people and goods. Although generally safe, aviation systems occasionally encounter incidents and accidents that underscore the need for proactive risk management. This study employs machine learning to detect abnormalities in commercial aircraft operations using Automatic Dependent Surveillance–Broadcast (ADS-B) data. Given the growing reliance on ADS-B technology, concerns regarding its susceptibility to security breaches, such as injection attacks, have intensified. To address these vulnerabilities, we propose a robust ADS-B injection detection system. Employing GridSearchCV for model optimization, it effectively identifies and categorizes injection risks. The system's performance, evaluated using the ADS-B Message Injection Attacks Dataset, achieves outstanding results, including a value of 0.9970 for the accuracy, precision, recall, and F1 score. The proposed classifier also demonstrates a higher area under the curve (0.9999), specificity (0.9956), and Cohen's kappa (0.9954) than existing approaches, while achieving a lower log loss (0.0107). This research significantly enhances aviation security by introducing a highly accurate, computationally efficient, and reliable real-time detection model for ADS-B injection attacks, ensuring the integrity and resilience of modern flight control systems. © 2025 The Authors

关键词： Risk management

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PSO-CHS Routing Protocol for Energy Consumption Enhancement in Wireless Sensor Networks 4

PSO-CHS Routing Protocol for Energy Consumption Enhancement ...

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4th IEEE International Conference on Mobile Networks and Wireless communications, ICMNWC 2024

作者： Fakhri, Israa Sabri Marhoon, Haydar Abdulameer Hussein, Mohsin Hasan College of Computer Science and Information Technology University of Kerbala Department of Computer Science Karbala Iraq Scientific Research Center Al-Ayen University Department of Computer Science Information and Communication Technology Research Group Thi-Qar Iraq

ISBN: (纸本)9798350352931

A region's Wireless sensor networks consist of sensors distributed over a region that collect and send environmental data to a central station. It measures humidity and temperature. The formation is based on many key ideas, including the routing system that optimizes data flow to the base station. Routing introduces limits and obstacles to better results. One challenge is choosing the cluster head, who aggregates data from nearby sensors and sends it to the base station. Many methods are used to choose the cluster head. Instead of GSTEB, DCBRP, and ETSRP, which are laborious and time-consuming, modern cluster head identification methods use artificial intelligence. This study selects the cluster head quickly and accurately using PSO-CHS (particle swarm optimization-Cluster Head Selection Routing Protocol). It improves execution time by 84.1%, delay matrix by 44.3%, and energy matrix by 80.4% over previous protocols. PSO-CHS design promotes energy efficiency, network durability, and packet loss reduction. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Colored Edge Detection Using Thresholding Techniques

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Recent Advances in computer science and communications 2023年第4期16卷 33-41页

作者： Fenyi, Adolf Fenyi, Isaac Asante, Michael Department of Computer Science and Information Technology Kwame Nkrumah University of Science and Technology Kumasi Ghana

Background: In this research, a novel algorithm is formulated through the combination of gradient and adaptive thresholding. A set of 5 X 5 convolution kernels were generated to determine the gradients in the four main directions of the image. Objectives: The researcher converted the gaussian equation into a normalized kernel, which was convolved with the gradients to suppress the impact of noise. Methods: The edges derived were partitioned into a set of 5 x 5 matrices. A weighted variance was calculated for each local window in the image. The pixel that generated the minimum variance was used for the segmentation process in each local window. The researcher then trimmed multiple pixel width edges into singles by developing a set of 5 X 5 Structuring Elements (SE). These elements were placed over the image to remove boundary pixels. In order to produce colored edges, the algorithm was executed over all the channels and the results were concatenated to produce the skeletal colored edges. Results: From the evaluations conducted, the proposed algorithm exhibited better performance than most of the recent algorithms with respect to Human Perception Clarity and time complexity in both noisy and non-uniform illuminated images. Conclusion: The reason for this performance is that it is able to extract edges moving in the various directions of images. It also ensures that identified edges are single pixel width instead of multiple. © 2023 Bentham science Publishers.

关键词： Pixels

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Visual quality-enhanced reversible data hiding scheme based on interpolation and variant Sudoku matrics

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Multimedia Tools and Applications 2024年第18期84卷 1-29页

作者： Yuan, Longfei Lin, Chia-Chen Chang, Chin-Chen Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung Taiwan Department of Information Engineering and Computer Science Feng Chia University Taichung Taiwan

Utilizing interpolation techniques (IT) within reversible data hiding (RDH) algorithms presents the advantage of a substantial embedding capacity. Nevertheless, prevalent algorithms often straightforwardly embed confidential data into non-reference pixels without accounting for the relationships between neighboring pixels. This practice results in suboptimal visual quality in the stego images. To rectify this issue, we have introduced the augmentation and modification interpolation (AMI). Additionally, we have devised two variant Sudoku matrices to facilitate distinct data-hiding strategies tailored for different types of reference pixels: Single type and Paired type. By incorporating these innovative designs, the interpolation operation now preserves the pattern-level information of the block in the original image, rather than focusing solely on the pixel level. In assessing the effectiveness of our proposed scheme, we performed experiments utilizing two representative image datasets. The outcomes of these experiments validate that our scheme attains an average PSNR of 44.11 dB and an average hiding capacity of 1.63 bpp. Consequently, it is affirmed that the advantage offered by our proposed scheme is well-suited for scenarios demanding high-quality stego images. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Interpolation

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Multiclass Classification for Cyber Threats Detection on Twitter

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computers, Materials & Continua 2023年第12期77卷 3853-3866页

作者： Adnan Hussein Abdulwahab Ali Almazroi College of Computer Science and Engineering Department of Computer ScienceAL-Ahgaff UniversityMukallaYemen College of Computing and Information Technology at Khulais Department of Information TechnologyUniversity of JeddahJeddahSaudi Arabia

The advances in technology increase the number of internet systems *** a result,cybersecurity issues have become more *** threats are one of the main problems in the area of ***,detecting cybersecurity threats is not a trivial task and thus is the center of focus for many researchers due to its *** study aims to analyze Twitter data to detect cyber threats using a multiclass classification *** data is passed through different tasks to prepare it for the *** Frequency and Inverse Document Frequency(TFIDF)features are extracted to vectorize the cleaned data and several machine learning algorithms are used to classify the Twitter posts into multiple classes of cyber *** results are evaluated using different metrics including precision,recall,F-score,and *** work contributes to the cyber security research *** experiments revealed the promised results of the analysis using the Random Forest(RF)algorithm with(F-score=81%).This result outperformed the existing studies in the field of cyber threat detection and showed the importance of detecting cyber threats in social media *** is a need for more investigation in the field of multiclass classification to achieve more accurate *** the future,this study suggests applying different data representations for the feature extraction other than TF-IDF such as Word2Vec,and adding a new phase for feature selection to select the optimum features subset to achieve higher accuracy of the detection process.

关键词： Cybersecurity cyber threat detection artificial intelligence machine learning Twitter

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A Support Vector Machine(SVM)Model for Privacy Recommending Data Processing Model(PRDPM)in Internet of Vehicles

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computers, Materials & Continua 2025年第1期82卷 389-406页

作者： Ali Alqarni Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy *** key requirement for such systems is the preservation of user privacy,ensuring a seamless experience in driving,navigation,and *** privacy needs are influenced by various factors,such as data collected at different intervals,trip durations,and user *** address this,the paper proposes a Support Vector Machine(SVM)model designed to process large amounts of aggregated data and recommend privacy preserving *** model analyzes data based on user demands and interactions with service providers or neighboring *** aims to minimize privacy risks while ensuring service continuity and *** SVMmodel helps validate the system’s reliability by creating a hyperplane that distinguishes between maximum and minimum privacy *** results demonstrate the effectiveness of the proposed SVM model in enhancing both privacy and service performance.

关键词： Support vector machine big data IoV privacy-preserving

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Enhancing Hybrid Classification for Plant Diseases with Deep Feature Selection Based on Analytical Entropy and Statistical Method

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IEEE Access 2025年 13卷 86781-86798页

作者： Phiphatkamtorn, Phanthawat Jitanan, Sutasinee Faculty of Science Naresuan University Department of Computer Science and Information Technology Thailand

The purpose of much research in the hybrid classification area is to reduce the number of deep features. However, many approaches overlook the relation between deep features and specific classes or diseases. This study aims to use information entropy to distinguish between valuable and less valuable features in the extracted deep feature set. The architecture of state-of-the-art models influences the characteristics of deep features, which are of three types: negative, zero, and positive values. Accurate predictions in typical machine learning models entail identifying valuable features, especially those with the most positive values. We perform one-way analysis of variance (ANOVA) and mutual information (MI) analysis to verify the correlation between the remaining deep features and specific classes or diseases. We discard features with weak relations based on predefined thresholds established by the involved f-statistic and MI mean values. This enhanced hybrid classification approach uses the entropy mean threshold, skewness visualization, the standard deviation, and the mean probability of valuable feature threshold to select valuable features and remove less valuable features, as well as one-way ANOVA and MI to evaluate relations and eliminate weakly related deep features. The results of all experiments show that the proposed approach exhibits higher accuracy than traditional hybrid classification and remains effective across all plant species. For tomatoes, ResNet-34 is the best model, achieving an excellent accuracy of 99.94% with 98 features. For sugarcane, ResNet-34 achieves a remarkably accuracy of 99.41% with 121 features. For corn, EfficientNet-B4 attains a significant accuracy of 99.77% with 123 features. © 2013 IEEE.

关键词： Analysis of variance (ANOVA)

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