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Proceedings - 2023 ieee 9th international conference on Big Data security on cloud, ieee international conference on High Performance and Smart computing, and ieee international conference on Intelligent Data and security, BigDatasecurity-HPSC-IDS 2023

Proceedings - 2023 IEEE 9th International Conference on Big ...

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9th ieee international conference on Big Data security on cloud, 9th ieee international conference on High Performance and Smart computing and 9th ieee international conference on Intelligent Data and security, BigDatasecurity-HPSC-IDS 2023

ISBN: (纸本)9798350312935

The proceedings contain 35 papers. The topics discussed include: a survey of weakly-supervised semantic segmentation;privacy analysis of federated learning via dishonest servers;a new method of construction of permutation trinomials with coefficients 1;cross-consensus measurement of individual-level decentralization in blockchains;an expert knowledge generation model in smart contract vulnerability fuzzing;cyber-attack detection using secret sharing schemes;exploring downvoting in blockchain-based online social media platforms;inventory big data management for internet of things based on privacy preserving;securitizing microcredits using blockchain;an efficient transformer with distance-aware attention;application of profiled analysis to ADC-based remote side-channel attacks;and an individual-differences-based method for discovering viewpoints on interactive behavior.

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2023 ieee international conference on Dependable, Autonomic and Secure computing, international conference on Pervasive Intelligence and computing, international conference on cloud and Big Data computing, international conference on cyber Science and Technology Congress, DASC/PiCom/CBDCom/cyberSciTech 2023

2023 IEEE International Conference on Dependable, Autonomic ...

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2023 ieee international conference on Dependable, Autonomic and Secure computing, 2023 international conference on Pervasive Intelligence and computing, 2023 international conference on cloud and Big Data computing, 2023 international conference on cyber Science and Technology Congress, DASC/PiCom/CBDCom/cyberSciTech 2023

ISBN: (纸本)9798350304602

The proceedings contain 174 papers. The topics discussed include: analyzing the effects of driving experience on backing maneuvers based on data collected by eye-tracking devices;recommendation algorithm based on social information and global average matrix decomposition;data analytics for dependable transportation systems in a smart city;stabilizing technology for online user dynamics by controlling one-way weights of links;a novel feature extraction technique for ECG arrhythmia classification using ml;fault-tolerant data aggregation with error-checking for smart grids;characterization of execution time variability in FPGA-based ai-accelerators;an improved deep learning-based approach to urban weather radar echo extrapolation;balanced hashgraph based on dynamic MCMC: efficient, practical and compatible;a blockchain of blockchains structure based on asset trading scheme;and research on malicious traffic detection based on word embedding algorithms and neural networks.

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Proceedings of the 2022 ieee international conference on Dependable, Autonomic and Secure computing, international conference on Pervasive Intelligence and computing, international conference on cloud and Big Data computing, international conference on cyber Science and Technology Congress, DASC/PiCom/CBDCom/cyberSciTech 2022

Proceedings of the 2022 IEEE International Conference on Dep...

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20th ieee international conference on Dependable, Autonomic and Secure computing, 20th ieee international conference on Pervasive Intelligence and computing, 7th ieee international conference on cloud and Big Data computing, 2022 ieee international conference on cyber Science and Technology Congress, DASC/PiCom/CBDCom/cyberSciTech 2022

ISBN: (纸本)9781665462976

The proceedings contain 185 papers. The topics discussed include: wearable IMU based gait quality quantitative evaluation method;quantum computing approach for energy optimization in a prosumer community;raising citizens and institutions awareness of environmental problems using smart sensing technologies;event detection in financial markets;design-space exploration of quantized transposed convolutional neural networks for FPGA-based systems-on-chip;seamless sensor data acquisition for the edge-to-cloud continuum;occupancy prediction in buildings: an approach leveraging LSTM and federated learning;lightweight model by iterative momentum pruning scheme for channel reduction;a deep reinforcement learning based approach for intelligent reconfigurable surface elements selection;exploiting mobility data to forecast Covid-19 spread;machine learning methods for microwave imaging in cancer detection;and physical activity recognition using deep transfer learning with convolutional neural networks.

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Proceedings - 2022 ieee 8th international conference on Big Data security on cloud, ieee international conference on High Performance and Smart computing, and ieee international conference on Intelligent Data and security, BigDatasecurity/HPSC/IDS 2022

Proceedings - 2022 IEEE 8th International Conference on Big ...

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8th ieee international conference on Big Data security on cloud, 8th ieee international conference on High Performance and Smart computing, and 8th ieee international conference on Intelligent Data and security, BigDatasecurity/HPSC/IDS 2022

ISBN: (纸本)9781665480697

The proceedings contain 39 papers. The topics discussed include: an insider threat detection method based on heterogeneous graph embedding;predicting relations in sg-cim model based on graph structure and semantic information;a semantic analysis-based method for smart contract vulnerability;overview of blockchain and cloud service integration;a Russian hate speech corpus for cybersecurity applications;modeling learner behavior analysis based on educational big data and dynamic Bayesian network;remote audit scheme of embedded device software based on TPM;practical verifiable computation on encrypted data;high-performance distributed feature storage and consumption services in power grid scenarios;job scheduling on edge and cloud servers;and disk failure prediction based on SW-disk feature engineering.

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A factual study on hybrid multi cloud cyber security threats and proposed methodologies to enable cyber resilience 10

A factual study on hybrid multi cloud cyber security threats...

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10th ieee international conference on Electronics, computing and Communication Technologies (ieee CONECCT)

作者： Mishra, Atul Sarat, Parvathy Afza, Roohi SAP LABS India Private Ltd Discrete Ind Bengaluru India

ISBN: (纸本)9798350385939;9798350385922

A hybrid multi cloud integrates infrastructure components of on-premises, private, and public cloud sources into one centralized, distributed computing environment. Hybrid multi cloud security must operate at all levels of the distributed network and must be resilient to external threats. Since there is a continuous data exchange between different layers, any gap in privileges and insecure communications can expose vital sensitive data to unauthorized access and loss, leading to data breach. Data governance with dispersed logging and monitoring capabilities leads to inefficient audit trails. In this paper we are presenting a factual study and statistical evaluation on the importance of data categorization as a tool for sensitive data in such an environment, data integrity during data life cycle status changes and escalation of privileges highlighting the missed security compliances. The paper also proposes few methodologies like a centralised status management monitor, event based data synchronisation, centralised master data distribution, integration testing automation and the best practices to be considered by the product teams for a secured product.

关键词： cloud security

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Detecting covert channels in cloud access control policies using Large Language Models

Detecting covert channels in cloud access control policies u...

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4th ieee Annual international conference on cyber security and Resilience (ieee CSR)

作者： Karmarkar, Hrishikesh Joshi, Vaibhavi Venkatesh, R. TCS Res Mumbai India TCS Res Pune India

ISBN: (纸本)9798350375367

In the realm of cloud computing, the task of configuring access control policies is a critical aspect of ensuring security of cloud resources. However, policy configuration remains a complex task with a high cognitive load as it requires a simultaneous understanding of the cloud environment and security requirements of the organization. This often creates gaps between intended and actual policy configuration leading to misconfigurations of policies. A misconfigured policy can introduce subtle and unexpected vulnerabilities that can be exploited by malicious entities to gain unauthorized access to cloud resources. In this paper, we model and analyze a particular class of access control vulnerabilities that arise due to the creation of covert channels in role-based access control policies. We present a tool CovertHunter that uses a Large Language Model to recognize intent behind policy configuration described in natural language and check it against actual cloud policies to automatically detect vulnerabilities arising due to the presence of covert channels.

关键词： access control cloud computing security theft role based access control covert channels large language models

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An AI-aware Orchestration Framework for cloud-based LLM Workloads 10

An AI-aware Orchestration Framework for Cloud-based LLM Work...

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ieee 10th international conference on Edge computing and Scalable cloud (ieee EdgeCom) / 11th ieee international conference on cyber security and cloud computing (ieee CScloud)

作者： Ye, Zi Ying, Ruoyu Shanghai Jiao Tong Univ UM SJTU Joint Intitute Shanghai Peoples R China Intel Software & Adv Technol Grp Dept Cloud Software Engn Shanghai Peoples R China

ISBN: (纸本)9798350377149;9798350377132

Large Language Models (LLMs), such as ChatGPT and Bard, have revolutionized the entire industry with their extraordinary power, and gained traction in the cloud community. Deploying LLM workloads in cloud has become increasingly common nowadays, as cloud easily fulfills their requirement on extensive computing resources. There are already tools (such as Kubernetes) that help simplify the management of Artificial Intelligence (AI) workloads, yet LLMs often require the balancing of multi-dimensional demands, which can be hard to handle by basic tools. This paper introduces an AI-aware orchestration framework that optimizes LLM performance from aspects including scheduling, scaling, and advanced data management. It can improve the cost-efficiency of the LLM workloads while ensuring its performance, while ensuring the absence of common security risks, supported by Intel hardware.

关键词： Artificial Intelligence Machine Learning Large Language Model Kubernetes cloud computing

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cloud computing security: Threats and Mitigation Strategies

Cloud Computing Security: Threats and Mitigation Strategies

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2024 ieee international conference on Signal Processing and Advance Research in computing, SPARC 2024

作者： Sharma, Chetna Sharma, Chandani Institute of Computer Technology & Business Management Maharishi Markandeshwar University Haryana Mullana Ambala India

ISBN: (纸本)9798350385199

Information Technology plays a pivotal role in the modern business landscape, facilitating efficient operations and communication. However, the increasing reliance on IT systems also exposes organizations to various security risks. security is a top priority to safeguard sensitive data, maintain operational integrity, and protect against a myriad of cyber threats. The paper classifies different cyber threats, including malware, phishing, man-in-the-middle attacks, denial-of-service attacks, and examines their potential to infiltrate systems, deceive users, disrupt operations, and steal data. It emphasizes the importance of securing internet-connected devices and suggests strategies such as implementing robust password policies and conducting regular software updates. Therefore, cloudGuard Analytics enhances cloud security through AI-driven detection and provides real-time vulnerability monitoring. The paper concludes by outlining future trends, including hybrid techniques with enhanced performance that may be explored for making cloud security robust and highlighting the necessity of adaptive security strategies to address the dynamic nature of cyber threats, incorporating robust defense mechanisms, ongoing monitoring, and threat intelligent tools. Emphasizing cyber security allows organizations to effectively manage the challenges of cloud computing, protecting the integrity, confidentiality, and availability of valuable information in today's digital environment. These insights provide the basis for developing a security framework specifically designed for applications in cloud environments. © 2024 ieee.

关键词： cloud security

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A Cost-effective Automation Method of Massive Vulnerabilities Analysis and Remediation Based on cloud Native 22

A Cost-effective Automation Method of Massive Vulnerabilitie...

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ieee 22nd international conference on Trust, security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Hu, Tian Zhuang, Shangyuan Sun, Jiyan Liu, Yinlong Ma, Wei Wang, Hongchao Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9798350381993;9798350382006

With the rapid development of the cutting edge cloud computing technology, millions of vulnerabilities have been identified, there is a growing concern that organizations should devote plenty of time and lots of resources to secure. The overarching objective of remediation is to prioritize the vulnerabilities. Hence, define the severity and the urgency of the vulnerabilities and remediate them automatically is very important. Although the recognized Common Vulnerability Scoring System (CVSS) 4.0 method addresses this issues partly, they are difficult to be implemented in practices on the cloud because of the complication and lack of risk based factors. To this end, we propose a Cost-effective Massive Automation Method of Vulnerability Analysis and Remediation Based on cloud Native Framework. Specifically, considering that the current CVSS is more like a severity of vulnerabilities, we design a novel formula to define the urgency of vulnerabilities. The formula takes the advantaged of the capabilities of modern cloud-based infrastructure and simplifies the CVSS. Besides, we propose an algorithm of risk reduction by leveraging the cloud native security capabilities, which cut down unnecessary patching time and workload. Particularly, in order to remediation the risk on the cloud, we implement an automatic scheme to harden the vulnerabilities by invoking the cloud native APIs based on the security Orchestration, Automation and Response (SOAR) platform. Finally, we conduct comprehensive experiments to evaluate our system. Experimental results demonstrate the effectiveness of ours approach has a high ratio of urgency risk recognition of 99.24%. Meanwhile, ours approach shows a maximum risk reduction by downgrade the fixable vulnerability with a average of 79% risk reduction rate in application level and 99% of risk reduction rate in operating system level respectively. As a result, our approach lightens the workload of patching greatly in the real cloud computing environ

关键词： cloud computing Massive Vulnerabilities Analysis Vulnerabilities Remediation

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Efficient Privacy-Preserving Spatial Data Query in cloud computing

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ieee TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第1期36卷 122-136页

作者： Miao, Yinbin Yang, Yutao Li, Xinghua Wei, Linfeng Liu, Zhiquan Deng, Robert H. Xidian Univ Sch Cyber Engn Xian 710071 Peoples R China Xidian Univ Sch Cyber Engn State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Minist Educ Engn Res Ctr Big Data Secur Xian 710071 Peoples R China Jinan Univ Sch Cyber Secur Guangzhou 510632 Peoples R China Jinan Univ Coll Cyber Secur Guangzhou 510632 Peoples R China Singapore Management Univ Sch Informat Syst Singapore 178902 Singapore

With the rapid development of geographic location technology and the explosive growth of data, a large amount of spatial data is outsourced to the cloud server for reducing the local high storage and computing burdens, but at the same time causes security issues. Thus, extensive privacy-preserving spatial data query schemes have been proposed. Most of the existing schemes use Asymmetric Scalar-Product-Preserving Encryption (ASPE) to encrypt data, but ASPE has proven to be insecure against known plaintext attack. And the existing schemes require users to provide more information about query range and thus generate a large amount of ciphertexts, which causes high storage and computational burdens. To solve these issues, based on enhanced ASPE designed in our conference version, we first propose a basic Privacy-preserving Spatial Data Query (PSDQ) scheme by using a new unified index structure, which only requires users to provide less information about query range. Then, we propose an enhanced PSDQ scheme (PSDQ$<^>+$+) by using Geohash-based $R$R-tree structure (called $GR$GR-tree) and efficient pruning strategy, which greatly reduces the query time. Formal security analysis proves that our schemes achieve Indistinguishability under Chosen Plaintext Attack (IND-CPA), and extensive experiments demonstrate that our schemes are efficient in practice.

关键词： cloud server privacy-preserving query range security issues spatial data

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