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Research on anti-jamming technology of pseudo-code phase modulation fuse pulse based on CPOKF

Research on anti-jamming technology of pseudo-code phase mod...

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2024 international conference on cloud computing and communication engineering, CCCE 2024

作者： Zhang, Zheng Wei, Daozhi Wang, Jinze Zhu, Bingchen Air Defense and Antimissile School Air Force Engineering University Xi’an710051 China

ISBN: (数字)9781510686717

ISBN: (纸本)9781510686700

At present, it is critical to protect radar and communication systems from interference. As technology continues to advance, ensuring the stability and security of these systems is becoming more and more important to maintain normal operation and communication quality. In this paper, an anti-jamming technique based on cyclic prediction optimal Kalman filter (CPOKF) based on pseudo-code phase modulation fuse pulse is introduced. In order to improve the anti-jamming ability of the pseudo-random code pulse Doppler fuse, on the basis of the analysis of its working and anti-jamming principle, the Kalman filter parameters were optimized by using the Crested Porcupine Optimizer (CPO), and a CPOKF algorithm was proposed to simulate the pseudo-code pulsed Doppler signal under active suppression interference. The results show that the anti-interference performance index of the filtered signal of the proposed algorithm is better than that of the fixed-parameter Kalman filter signal, and the anti-interference performance is better. © 2024 SPIE.

关键词： Kalman filters

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Enhancing the Security of Virtualized Infrastructure through Big Data Analytics in cloud computing 1

Enhancing the Security of Virtualized Infrastructure through...

引用

1st IEEE international conference on Advances in computing, communication and Networking, ICAC2N 2024

作者： Bisen, Anand Singh Kumar, Ravi Hema, B. Jenasamanta, Abhinash Shakya, Lalit Mohan Shanthi, R. Vikrant University Dept. of CSE and IT Gwalior India ITM Skill University Dept of CSE Mumbai India Dhanalakshmi Srinivasan College of Engineering Dept. of Electronics and Communication Engineering Coimbatore India Usha Martin University Dept. of CSE Jharkhand India Science J.S. University Dept. of Computer Shikohabad India Meenakshi A.H.E.R Dept. of Mathematics Chennai India

ISBN: (纸本)9798350356816

This research paper presents a novel approach to enhancing the security of virtualized environments through big data analytics in cloud computing. While virtualization and cloud computing provide flexibility and cost-efficiency, they also pose unique security challenges. Traditional security mechanisms often struggle to detect and mitigate advanced persistent threats (APTs) and sophisticated cyberattacks. To overcome these challenges, the proposed method leverages big data analytics to analyze extensive and complex datasets, identifying patterns, anomalies, and potential threats. The paper includes a comprehensive literature review on big data analytics for cloud and virtualized security, alongside a framework for threat detection and prevention utilizing machine learning algorithms, data mining, and predictive analytics. The study evaluates the benefits and limitations of this method and highlights directions for future research. This approach is designed to strengthen the security of virtualized environments and provide robust protection against emerging and advanced cyber threats. © 2024 IEEE.

关键词： cloud security

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Enhancing the Security and Integrity of Intrusion Detection Systems based on CNN and LSTM Approach for cloud computing 5

Enhancing the Security and Integrity of Intrusion Detection ...

引用

5th international conference on Smart Electronics and communication, ICOSEC 2024

作者： Arunkumar, K. Kumar, S. Sai Vulapula, Sridhar Reddy Inayathulla, Mohammed Venkatesh, N. Babu, G. Charles Malla Reddy Engineering college Department of Cse Telangana Secunderabad India Pvp Siddhartha Institute of Technology Department of It Andhrapradesh Vijayawada India Vignana Bharathi Institute of Technology Department of It Telangana Hyderabad India Gates Institute of Technology Department of Cse Andhra Pradesh Gooty India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India Gokaraju Rangaraju Institute of Engineering and Technology Department of Cse Telangana Hyderabad India

ISBN: (纸本)9798331504403

As cloud computing environments become increasingly prevalent, ensuring the security and integrity of these systems is paramount. Traditional intrusion detection systems (IDS) often fall short in addressing the dynamic and complex nature of cloud infrastructures. This study presents a enhanced security and integrity of Deep Learning (DL) approach for intrusion detection in cloud computing environments, leveraging Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. By incorporating an attention mechanism into the deep learning framework, the suggested system is able to recognize temporal patterns that point to intrusions and efficiently record spatial characteristics. Comprehensive tests are carried out on standard cloud computing datasets, proving that the system performs better than traditional techniques in identifying a broad variety of intrusion types. The findings point to the possibility of using deep learning methods to greatly enhance the responsiveness and resilience of IDS in cloud environments, opening up opportunities for more reliable and secure cloud computing services. © 2024 IEEE.

关键词： Network intrusion

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Application and Optimization Algorithms of Multi-Party Secure computing in Information communication

Application and Optimization Algorithms of Multi-Party Secur...

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2024 international conference on Power, Electrical engineering, Electronics and Control, PEEEC 2024

作者： Ye, Lu Tang, Yi Sun, Jun State Grid Hubei Electric Power Co. Ltd. Information & Communication Branch Hubei Wuhan430077 China

ISBN: (纸本)9798350378917

With the rapid growth of information technology (IT), the amount of data generated by various industries in modern society is exploding. However, while enjoying the convenience brought by data, how to ensure the privacy and security of data while maximizing its value has become a major challenge facing us. In this context, multi-party secure computing (SMPC) technology has emerged, providing strong technical support for data privacy protection. SMPC is a privacy computing technology based on cryptography, which can perform joint computation on multi-party data without leaking the original data, thereby ensuring the security and privacy of data. In order to further improve the efficiency and practicality of SMPC, this paper proposes an optimization algorithm and combines it with cloud computing. By adopting a distributed computing architecture, we can decompose and allocate computing tasks to multiple computing nodes, significantly improving computing efficiency. The experimental results show that our proposed SMPC optimization algorithm, when combined with cloud computing, not only significantly improves computational efficiency, but also demonstrates excellent performance in data privacy protection. © 2024 IEEE.

关键词： Differential privacy

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Feature as a Service (FaaS): Customized Service in cloud computing 1

Feature as a Service (FaaS): Customized Service in Cloud Com...

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1st IEEE international conference on Advances in computing, communication and Networking, ICAC2N 2024

作者： Sharma, Aditi Kaushik, Deepti Maurya, Santosh Kumar Malik, Suraj Kumar, Gaurav Asim, Mohammad IIMT University Computer Science and Engineering Department Uttar Pradesh Meerut India Sharda University Department of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350356816

The world is changing due to technological changes. Like the business approach, there is a need to maintain tremendous pressure for reliable service architecture. Features of trust may include cost-effectiveness, reliability for services, pay per use, etc. cloud services are very much appreciated as it is giving tremendous market to the business audience. Inspired by this, processing and storage capacity is also improving. By proposing a new service FaaS (Feature as a Service), more measuring factors can be obtained to achieve greater growth in technology and will also benefit the business audience. The offered service is directly related to both the things i.e. server and user related, in terms of system optimization and user satisfaction. The measuring function is also proposed in the paper. Ultimately the new customized service model will yield benefits. As the world has now become digital, the system efficiency should be further increased by designing these types of services which can cover the entire cloud system in all aspects. © 2024 IEEE.

关键词： cloud computing architecture

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Federated Learning Strategies for Privacy-Preserving Machine Learning Models in cloud computing Environments

Federated Learning Strategies for Privacy-Preserving Machine...

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2024 international conference on communication, Computer Sciences and engineering, IC3SE 2024

作者： Tiwari, Shashank Shekhar Dhasmana, Gulshan Al-Jawahry, Hassan M. Rana, Aditya Bhardwaj, Garima Srivastava, Arun Pratap Rec Department Of Information Technology U.P Ambedkar Nagar India Dr. Abdul Kalam Technical University Lucknow India Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Amity Business School Amity University Greater Noida India Lloyd Institute Of Engineering & Technology Greater Noida India

ISBN: (纸本)9798350366846

Federated learning presents a promising approach for preparing machine learning models while protecting information protection in cloud computing situations. This investigation examines the adequacy of different unified learning procedures, including Federated Averaging, Secure Multi-Party Computation (SMPC), Differential Security, and Homomorphic Encryption, in keeping up show precision while securing delicate information. Through tests conducted in a reenacted cloud computing environment, exactness, security conservation, and computational overhead were assessed. Results appears that Federated Averaging accomplishes the most noteworthy show exactness (86.5%), followed by SMPC (84.2%), Differential Privacy (83.8%), and Homomorphic Encryption (82.1%). Differential Privacy offers the most elevated level of protection assurance, whereas Unified Averaging shows the least computational overhead. These findings underscore the importance of taking into account such trade-offs between showing performance, security preservation and computational efficiency while choosing unified learning algorithms for privacy sensitive applications. © 2024 IEEE.

关键词： cloud computing

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Optimised Load Balancing for Green cloud computing 13

Optimised Load Balancing for Green Cloud Computing

引用

13th international conference on cloud computing, Data Science and engineering, Confluence 2023

作者： Girsa, Deepanshu Chauhan, Devansh Sharma, Dolly Amity Univeristy Amity School of Engineering Noida India

ISBN: (纸本)9781665462631

Consumption of energy accounts for a significant portion of the costs associated with carrying out any activity. cloud computing has seen spectacular development in network technology because of the advancements in communication technology, the expansion of Internet usage, and the solution of issues on a bigger scale. The NP-hard challenge of scheduling and allocating resources to meet the needs of users in the cloud has been referred to as a scheduling problem. For this reason, balancing the load is critical to overcoming all the issues listed above. Researchers have compared a wide range of load balancing techniques in the cloud to improve different performance metrics and help load balancing. The study also describes relation between green cloud computing and load balancing. © 2023 IEEE.

关键词： cloud computing

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A change detection method for multi-source heterogeneous images using a refined hierarchical clustering approach

A change detection method for multi-source heterogeneous ima...

引用

2024 international conference on cloud computing and communication engineering, CCCE 2024

作者： Xu, Junfeng Yang, Na Lou, Yilan Zhang, Chenhui School of Non-Commissioned Officer Space Engineering University No.7 Fuxue Road Changping District Beijing102249 China

ISBN: (数字)9781510686717

ISBN: (纸本)9781510686700

For change detection in multi-source heterogeneous images, this paper proposes a change detection method using a refined hierarchical clustering approach. For multi-source heterogeneous multi-temporal images, Stacked Denoising Autoencoders (SDAE) is used to extract deep features from multi-source heterogeneous images. On this basis, the comparability of heterogeneous data in deep feature space is guaranteed by iterative transformation of features. Finally, the correlation between deep features of heterogeneous data is described by introducing a variety of distance measures, and the hierarchical clustering method is improved. The classification of change types is gradually realized through multiple clustering, while improving the accuracy of change detection. © 2024 SPIE.

关键词： Change detection

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1st international conference on Innovation in Electrical Power engineering, communication, and computing Technology, IEPCCT 2019

1st International Conference on Innovation in Electrical Pow...

引用

1st international conference on Innovation in Electrical Power engineering, communication, and computing Technology, IEPCCT 2019

ISBN: (纸本)9789811523045

The proceedings contain 58 papers. The special focus in this conference is on Innovation in Electrical Power engineering, communication, and computing Technology. The topics include: Studies on off-grid solar photovoltaic-powered micro-irrigation system in aerobic rice cultivation for sustainable agriculture and mitigating greenhouse gas emission;application of iot in predictive maintenance using long-range communication (LoRa);PID-based electronic load controller for three-phase synchronous generator;survey of sentiment analysis of political content on twitter;Load frequency control incorporating electric vehicles using FOPID controller with HVDC link;automatic generation control in deregulated power market using sunflower optimization algorithm;implementation of monte carlo simulation to the distribution network for its reliability assessment;retinal image segmentation using gabor transform with preprocessing and hysteresis thresholding;Multi-level asymmetrical inverters using two DC ports with less number of switches and conversion stages;Blind feature-based steganalysis with and without cross validation on calibrated JPEG images using support vector machine;fuzzy granular computing-based controller design for inverted pendulum system with delay;electrical faults in photovoltaic modules: Analysis, characterization and detection;detection and classification of voltage sag causes based on S-transform and extreme learning machine;accelerated deep learning in proteomics—A review;DSTATCOM using limit cycle oscillator FLL with optimized gains of voltage error controllers;global horizontal irradiance prediction using deep neural network framework;A hybrid approach for path planning of multiple AUVs;a hybrid framework for fault classification and location in power distribution system using wavelet and support vector machine;effect of soiling on power output of a photovoltaic system.

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Hybrid Machine Learning Approach for Enhanced Vulnerability Detection in cloud Environments Using NIST and MITRE Frameworks 5

Hybrid Machine Learning Approach for Enhanced Vulnerability ...

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5th international conference on Advanced Research in computing, ICARC 2025

作者： Lasantha, N W Chanaka Maduranga, M.W.P. Abeysekara, Ruvan Tilwari, Valmik Chakraborty, Nilotpal Sharma, Debashree Iic University of Technology Faculty of Graduate Studies Phnom Penh Cambodia University of Sri Jayewardenepura Department of Electrical and Electronic Engineering Nugegoda Sri Lanka Bcas Campus Faculty of Computing Colombo Sri Lanka Indian Institute of Information Technology Department of Computer Science Engineering Guwahati India Indian Institute of Information Technology Department of Electronics & Communication Engineering Guwahati India

ISBN: (纸本)9798331530983

cloud security is challenged by constant adaptive cyber threats and traditional detection methods lack real time adaptability. In this paper, we propose a new hybrid ML approach stitching data from National Institute of Standards and Technology (NIST) and MITRE Adversarial Tactics, Techniques and Common Knowledge (ATT&CK) databases together. In contrast to existing methods, this framework combines, in unique and unprecedented fashion, ensemble ML models and Realtime data processing for increased detection accuracy and reduced false positives. This approach addresses specific challenges in the cloud environments and by leveraging standardised frameworks, it offers actionable audiences for efficient and effective vulnerability management process. The framework is validated by the experimental results which prove its robustness to evolution of the cybersecurity demands. © 2025 IEEE.

关键词： Cyber attacks

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