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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

共 6977 条记录，以下是131-140 订阅

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Hydamc: A Hybrid Detection Approach for Misuse of Cryptographic algorithms in Closed-Source Software 22

Hydamc: A Hybrid Detection Approach for Misuse of Cryptograp...

引用

IEEE 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigdataSE conference / CSE conference / EUC conference / ISCI conference

作者： Fan, Haoling Zheng, Fangyu Lin, Jingqiang Meng, Lingjia Wang, Mingyu Wang, Qiang Jia, Shijie Ma, Yuan Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur Beijing Peoples R China Chinese Acad Sci Data Assurance & Commun Secur Res Ctr Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Univ Chinese Acad Sci Sch Cryptog Beijing Peoples R China Univ Sci & Technol China Sch Cyber Secur Hefei Peoples R China

ISBN: (纸本)9798350381993;9798350382006

Cryptographic algorithms are fundamental to secure software development, but security vulnerabilities can arise during implementation, usage, and when calling third-party libraries. As security standards continue to evolve, software updates have become an inevitable trend, and detecting cryptographic algorithm misuse is crucial to ensure compliance with these standards during the update process. However, closed-source software presents challenges in detecting cryptographic algorithm misuse. To enhance the security ecosystem of software, we designed a hybrid detection approach for detecting misuses in closed-source software related to weak cryptographic algorithms, short keys, insecure working modes, and insecure padding modes. Our hybrid detection tool uses both static and dynamic detection methods to collect log information through a logging mechanism in binary executable files. The collected data is cleaned using a data cleaning strategy and analyzed to extract key features, generating test reports to help developers and experts identify cryptographic algorithm security issues. We tested 24 software applications from app stores and found that 62.5% had weak algorithm implementations or usage, 83.3% supported short keys, and 50% supported insecure padding modes. Finally, we provided actionable recommendations to mitigate identified issues.

关键词： Cryptography algorithm Closed source software Misuse detection Instrumentation

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Enhancement of Big data Security in Cloud computing Using RSA Algorithm 10

Enhancement of Big Data Security in Cloud Computing Using RS...

引用

10th international conference on Future Internet of Things and Cloud, FiCloud 2023

作者： Yeboah-Ofori, Abel Darvishi, Iman Opeyemi, Azeez Sakirudeen University of West London School of Computing and Engineering United Kingdom

ISBN: (纸本)9798350316353

The enhancement of big data security in cloud computing has become inevitable dues to factors such as the volume, velocity, veracity, Value, and velocity of the big data. These enhancements of big data and cloud technologies have computing enabled a wide range of vulnerabilities in applications in organizational business environments leading to various attacks such as denial-of-service attacks, injection attacks, and Phishing among others. Deploying big data in cloud computing environments is a rapidly growing technology that significantly impacts organizations and provides benefits such as demand-driven access to computational services, a distorted version of infinite computing capacity, and assistance with demand-driven scaling up, scaling down, and scaling out. To secure cloud computing for big data processing, a variety of encryption techniques such as RSA, and AES can be applied. However, there are several vulnerabilities during processing. The paper aims to explore the enhancement of big data security in cloud computing using the RSA algorithm to improve the deployment and processing of the variety, volume, veracity, velocity and value of the data utilizing RSA encryptions. The novelty contribution of the paper is threefold: First, explore the current challenges and vulnerabilities in securing big data in cloud computing and how the RSA algorithm can be used to address them. Secondly, we implement the RSA algorithm in a cloud computing environment using the AWS cloud platform to secure big data to improve the performance and scalability of the RSA algorithm for big data security in cloud computing. We compare the RSA algorithm to other cryptographic algorithms in terms of its ability to enhance big data security in cloud computing. Finally, we recommend control mechanisms to improve security in the cloud platform. The results show that the RSA algorithm can be used to improve Cloud Security in a network environment. © 2023 IEEE.

关键词： Cryptography

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Apache Spark Big data Analysis, Performance Tuning, and Spark Application Optimization

Apache Spark Big data Analysis, Performance Tuning, and Spar...

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2023 international conference on Evolutionary algorithms and Soft computing Techniques, EASCT 2023

作者： Karthikeya Sahith, Chaganti Sri Muppidi, Satish Merugula, Suneetha Gmr Institute of Technology Dept. of Cse Rajam India Gitam Deemed to Be University Gitam School of Technology Dept. of Cse Visakhapatnam India

ISBN: (纸本)9798350313413

Big data analysis, the processing of vast and diverse data, is crucial for enhancing business intelligence, predictive modeling, and quicker decision-making. It encompasses structured, unstructured, and semi-structured data, ranging from TBs to ZBs. Popular open-source tools like Apache Hadoop and Apache Spark are instrumental for managing this data deluge. Apache Spark is described as a superior environment in this article for handling huge data carefully and effectively. A few of the studies, technical achievements, and popular future development paths of Big data Analysis utilizing Apache Spark are highlighted. Apache Spark can offer a better working environment than Apache Hadoop, however it cannot be argued that it is not widespread in Industry 4.0 such that it could outreach the Hadoop. The main factor is Hadoop's use of the simple, comprehensible, and straightforward MapReduce programming model. However, the users of Spark platform are well-pleased with the versatile processing capabilities. As Spark is mainly based on Resilient Distributed data-sets and RDDs are outmoded now, there is a severe necessity of performance tuning that ensures flawless working from Spark and Spark based APIs. It is a need of the hour scenario where Spark applications should adopt advanced optimization techniques. © 2023 IEEE.

关键词： Apache Hadoop Apache Spark Big data Industry 4.0 MapReduce optimization techniques Performance tuning RDD

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23rd international conference on algorithms and Architectures for Parallel processing, ICA3PP 2023

23rd International Conference on Algorithms and Architecture...

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23rd international conference on algorithms and Architectures for Parallel processing, ICA3PP 2023

ISBN: (纸本)9789819708000

The proceedings contain 165 papers. The special focus in this conference is on algorithms and Architectures for Parallel processing. The topics include: MDCF: Multiple Dynamic Cuckoo Filters for LSM-Tree;a Game Theory Based Task Offloading Scheme for Maximizing Social Welfare in Edge computing;research on Dos Attack Simulation and Detection in Low-Orbit Satellite Network;malware Detection Method Based on Visualization;TOC: Joint Task Offloading and Computation Reuse in Vehicular Edge computing;Distributed Task Offloading for IoAV Using DDP-DQN;a Task Offloading and Resource Allocation Optimization Method in End-Edge-Cloud Orchestrated computing;multi-agent Cooperative Intrusion Detection Based on Generative data Augmentation;an Uncertainty-Aware Auction Mechanism for Federated Learning;Long Short-Term Deterministic Policy Gradient for Joint Optimization of Computational Offloading and Resource Allocation in MEC;query Optimization Mechanism for Blockchain-Based Efficient data Traceability;research on the Evolution Path of Network Hotspot Events Based on the Event Evolutionary Graph;Task Offloading in UAV-Assisted Vehicular Edge computing Networks;path Planning of Coastal Ships Based on Improved Hybrid A-Star;data Augmentation Method Based on Partial Noise Diffusion Strategy for One-Class Defect Detection Task;K Asynchronous Federated Learning with Cosine Similarity Based Aggregation on Non-IID data;MPQUIC Transmission Control Strategy for SDN-Based Satellite Network;an Energy Prediction Method for Energy Harvesting Wireless Sensor with Dynamically Adjusting Weight Factor;Bayesian Optimization for Auto-tuning Convolution Neural Network on GPU;persistent Sketch: A Memory-Efficient and Robust Algorithm for Finding Top-k Persistent Flows;faCa: Fast Aware and Competition-Avoided Balancing for data Center Network;Optimizing GNN Inference processing on Very Long Vector Processor;GDTM: Gaussian Differential Trust Mechanism for Optimal Recommender System;privacy-Enhanced Dyna

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A Survey on Spark Ecosystem: Big data processing Infrastructure, Machine Learning, and Applications (Extended abstract) 39

A Survey on Spark Ecosystem: Big Data Processing Infrastruct...

引用

39th IEEE international conference on data Engineering, ICDE 2023

作者： Tang, Shanjiang He, Bingsheng Yu, Ce Li, Yusen Li, Kun Tianjin University College of Intelligence and Computing Tianjin China National University of Singapore School of Computing Singapore Nankai University School of Computing Tianjin300071 China

ISBN: (纸本)9798350322279

With the explosive increase of big data in industry and academic fields, it is important to apply large-scale data processing systems to analyze Big data. Arguably, Spark is the state-of-the-art in large-scale data computing systems nowadays, due to its good properties including generality, fault tolerance, high performance of in-memory data processing, and scalability. Spark adopts a flexible Resident Distributed dataset (RDD) programming model with a set of provided transformation and action operators whose operating functions can be customized by users according to their applications. It is originally positioned as a fast and general data processing system. A large body of research efforts have been made to make it more efficient (faster) and general by considering various circumstances since its introduction. In this survey, we aim to have a thorough review of various kinds of optimization techniques on the generality and performance improvement of Spark. We introduce various data management and processing systems, machine learning algorithms and applications supported by Spark. Additionally, we make a discussion on the open issues and challenges for large-scale in-memory data processing with Spark. © 2023 IEEE.

关键词： Fault tolerance

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Karasu: A Collaborative Approach to Efficient Cluster Configuration for Big data Analytics

Karasu: A Collaborative Approach to Efficient Cluster Config...

引用

IEEE international Performance, computing, and Communications conference (IPCCC)

作者： Scheinert, Dominik Wiesner, Philipp Wittkopp, Thorsten Thamsen, Lauritz Will, Jonathan Kao, Odej Tech Univ Berlin Berlin Germany Univ Glasgow Glasgow Lanark Scotland

ISBN: (纸本)9798350302936

Selecting the right resources for big data analytics jobs is hard because of the wide variety of configuration options like machine type and cluster size. As poor choices can have a significant impact on resource efficiency, cost, and energy usage, automated approaches are gaining popularity. Most existing methods rely on profiling recurring workloads to find near-optimal solutions over time. Due to the cold-start problem, this often leads to lengthy and costly profiling phases. However, big data analytics jobs across users can share many common properties: they often operate on similar infrastructure, using similar algorithms implemented in similar frameworks. The potential in sharing aggregated profiling runs to collaboratively address the cold start problem is largely unexplored. We present Karasu, an approach to more efficient resource configuration profiling that promotes data sharing among users working with similar infrastructures, frameworks, algorithms, or datasets. Karasu trains lightweight performance models using aggregated runtime information of collaborators and combines them into an ensemble method to exploit inherent knowledge of the configuration search space. Moreover, Karasu allows the optimization of multiple objectives simultaneously. Our evaluation is based on performance data from diverse workload executions in a public cloud environment. We show that Karasu is able to significantly boost existing methods in terms of performance, search time, and cost, even when few comparable profiling runs are available that share only partial common characteristics with the target job.

关键词： Scalable data Analytics Distributed data processing Performance Modeling Resource Management

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23rd international conference on algorithms and Architectures for Parallel processing, ICA3PP 2023