QP-ChainSZKP: A Quantum-Proof Blockchain Framework for Scalable and Secure Cloud Applications

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 5, 2025

In the rapidly evolving landscape of cloud computing, burgeoning growth and centralization data exacerbate security vulnerabilities, necessitating robust scalable cryptographic solutions. This paper introduces QP-ChainSZKP framework, a novel architecture that amalgamates Quantum-Secure Cryptographic Algorithms with Zero-Knowledge Proof Management to shield environments against both classical emerging quantum threats. The proposed framework effectively integrates advanced techniques, enhancing protocols compliance measures required for operations. ensures not only adherence high-security standards but also provides strong protection breaches unauthorized access, crucial maintaining integrity confidentiality in environments. We employ dual approach our methodology by simulating rigorously testing evaluate its security, scalability, performance metrics. experimental results demonstrate significant enhancement transaction throughput reduction latency, corroborating framework’s capability manage high applications effectively. Specifically, achieves improvement 20% latency 30% under peak load scenarios, establishing efficacy handling dynamic Notably, addresses future computational threats modifying existing practices used public clouds, setting pioneering standard using technologies security. Our study contributes scalable, quantum-resistant solution tailored extensive applications, marking substantial advancement computing frameworks can meet imminent global requirements.

Language: Английский

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Enhanced Intrusion Detection with LSTM-Based Model, Feature Selection, and SMOTE for Imbalanced Data

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(2), P. 479 - 479

Published: Jan. 5, 2024

This study introduces a sophisticated intrusion detection system (IDS) that has been specifically developed for internet of things (IoT) networks. By utilizing the capabilities long short-term memory (LSTM), deep learning model renowned its proficiency in modeling sequential data, our effectively discerns between regular network traffic and potential malicious attacks. In order to tackle issue imbalanced which is prevalent concern development systems (IDSs), we have integrated synthetic minority over-sampling technique (SMOTE) into approach. incorporation allows accurately identify infrequent incursion patterns. The rebalancing dataset accomplished by SMOTE through generation samples belonging class. Various strategies, such as utilization generative adversarial networks (GANs), put forth data imbalance. However, (synthetic technique) presents some distinct advantages when applied detection. characterized simplicity proven efficacy across diverse areas, including implementation this approach straightforward does not necessitate intricate training techniques (GANs). interpretability lies ability generate are aligned with properties original rendering it well suited security applications prioritize transparency. widely embraced field research, demonstrating effectiveness augmenting capacities (IDSs) reducing consequences class conducted thorough assessment three commonly utilized public datasets, namely, CICIDS2017, NSL-KDD, UNSW-NB15. findings indicate LSTM-based (IDS), conjunction address imbalance, outperforms existing methodologies detecting intrusions. provide significant contributions domain security, presenting proactive adaptable safeguarding against advanced cyberattacks. Through mitigation imbalance using SMOTE, AI-driven enhances networks, hence facilitating wider IoT technologies many industries.

Language: Английский

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A hybrid approach for optimizing software defect prediction using a grey wolf optimization and multilayer perceptron

International Journal of Intelligent Computing and Cybernetics, Journal Year: 2024, Volume and Issue: 17(2), P. 436 - 464

Published: March 21, 2024

Purpose Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in systems. In this paper, we have proposed novel hybrid approach that combines Grey Wolf Optimization with Feature Selection (GWOFS) multilayer perceptron (MLP) for SDP. The GWOFS-MLP model designed optimize feature selection, ultimately enhancing the accuracy efficiency Optimization, inspired by social hierarchy hunting behavior grey wolves, employed select subset relevant features from an extensive pool predictors. This study investigates key challenges traditional SDP approaches encounter proposes promising solutions overcome time complexity curse dimensionality reduction problem. Design/methodology/approach integration GWOFS MLP results robust can adapt diverse datasets. selection process harnesses cooperative allowing exploration combinations. selected are then fed into MLP, powerful artificial neural network (ANN) known its capability learn intricate patterns within metrics. serves as predictive engine, utilizing curated set classify accurately. Findings performance evaluation on real-world dataset demonstrates effectiveness. achieves remarkable training 97.69% testing 97.99%. Additionally, receiver operating characteristic area under curve (ROC-AUC) score 0.89 highlights model’s ability discriminate between defective defect-free components. Originality/value Experimental implementations using machine learning-based techniques conducted validate solutions. goal enhance SDP’s accuracy, relevance efficiency, improving assurance processes. confusion matrix further illustrates performance, only small number false positives negatives.

Language: Английский

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Secure IoT framework for authentication and confidentiality using hybrid cryptographic schemes

International Journal of Information Technology, Journal Year: 2024, Volume and Issue: 16(4), P. 2053 - 2067

Published: Feb. 29, 2024

Language: Английский

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Irrigation with Artificial Intelligence: Problems, Premises, Promises

Human-Centric Intelligent Systems, Journal Year: 2024, Volume and Issue: 4(2), P. 187 - 205

Published: May 13, 2024

Abstract Protagonists allege that artificial intelligence (AI) is revolutionising contemporaneous mindscapes. Here, we authoritatively review the status quo of AI and machine learning application in irrigated agriculture, evaluating potential of, challenges associated with, a wide range existential approaches. We contend aspiring developers irrigation systems may benefit from human-centred AI, nascent algorithm captures diverse end-user views, behaviours actions, potentially facilitating refinement proposed through iterative stakeholder feedback. AI-guided human–machine collaboration can streamline integration user needs, allowing customisation towards situational farm management adaptation. Presentation big data intuitive, legible actionable forms for specialists laypeople also urgently requires attention: here, AI-explainable interpretability help harness human expertise, enabling end-users to contribute their experience within an pipeline bespoke outputs. Transfer holds promise contextualising place-based agroecological regions, production or enterprise mixes, even with limited inputs. find rate scientific software development recent times has outpaced evolution adequate legal institutional regulations, often social, moral ethical license operate, revealing consumer issues ownership, legitimacy trust. opine great elicit sustainable outcomes food security, social innovation environmental stewardship, albeit such more likely be realised concurrent appropriate ethical, dimensions.

Language: Английский

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Security challenges for workflow allocation model in cloud computing environment: a comprehensive survey, framework, taxonomy, open issues, and future directions

The Journal of Supercomputing, Journal Year: 2024, Volume and Issue: 80(8), P. 11491 - 11555

Published: Jan. 27, 2024

Language: Английский

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A bibliometric review of Internet of Things (IoT) on cybersecurity issues

Journal of Science and Technology Policy Management, Journal Year: 2024, Volume and Issue: unknown

Published: Feb. 15, 2024

Purpose In response to the growing interest in Internet of Things (IoT) technology and its profound implications for businesses individuals, this bibliometric study focuses on a critical yet understudied aspect, i.e. cybersecurity. As IoT adoption grows, so do concerns regarding user privacy data security. This aims provide comprehensive understanding current research vital area, shedding light trends, gaps emerging themes. Design/methodology/approach The conducted analysis systematic review literature spanning over two decades (2013–2023). Bibliometric is using Biblioshiny which R-software-based advanced analytical tool. Further, VOSviewer used conduct network analysis. highlights evolving landscape cybersecurity, emphasizing interdisciplinary intersections ethical dimensions technologies. Findings uncovers crucial related adoption, urgent need cybersecurity protocols. It identifies themes such as artificial intelligence blockchain integration, indicating shift toward solutions. Furthermore, discussions, importance responsible innovation. Research limitations/implications Businesses can bolster their strategies, policymakers craft informed regulations researchers are encouraged explore IoT’s dimensions. Originality/value pioneers nuanced filling gap existing business management literature. By synthesizing decade scholarly work, it provides foundational insights researchers, policymakers. not only informs academic discourse but also offers practical guidance enhancing security measures fostering

Language: Английский

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Trustworthy and efficient project scheduling in IIoT based on smart contracts and edge computing

Journal of Cloud Computing Advances Systems and Applications, Journal Year: 2025, Volume and Issue: 14(1)

Published: Jan. 11, 2025

To facilitate flexible manufacturing, modern industries have incorporated numerous modular operations such as multi-robot services which can be expediently arranged or offloaded to other production resources. However, complex manufacturing projects often consist of multiple tasks with fixed sequences, posing a significant challenge for smart factories in efficiently scheduling limited robot resources complete specific tasks. Additionally, when span across factories, ensuring faithful execution contracts becomes another challenge. In this paper, we propose modified combinatorial auction method combined blockchain and edge computing technologies organize project scheduling. Firstly, transform efficient resource into resource-constrained multi-project problem (RCPSP). Subsequently, the solution integrates random sampling (CA-RS) contracts. Alongside security analysis, simulations are conducted using real data sets. The results indicate that suggested CA-RS approach significantly enhances efficiency arrangement within industrial Internet Things compared baseline algorithms.

Language: Английский

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Exploration of Secured Data Transmission in Internet of Things: A Survey

Published: Feb. 9, 2024

The Internet of Things (IoT) is a pivotal technology with far-reaching potential to enhance various facets human life, spanning industries such as healthcare, automotive, agriculture, and education. However, its heterogeneous data landscape necessitates systematic examination IoT security challenges. To ensure end-to-end secure transmission, paradigm shift in application architecture imperative. This study meticulously investigates the intricacies transmission security, encompassing limitations, requirements, encryption methods, key management, protocols. Access control authentication mechanisms are also scrutinized fortify integrity. primary aim this paper comprehensively explore address critical securing IoT, showcasing real-world applications, underlining paramount importance transformative technological landscape.

Language: Английский

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Cloud Computing: Architecture, Vision, Challenges, Opportunities, and Emerging Trends

2022 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Journal Year: 2023, Volume and Issue: unknown, P. 829 - 834

Published: Nov. 3, 2023

Cloud computing stands at the forefront of a technological revolution, fundamentally altering provisioning, utilization, and administration resources. This paper conducts comprehensive examination visionary aspects, obstacles, possibilities inherent in cloud computing. It delves deep into foundational principles distinguishing features this technology, showcasing its capacity to redefine realm information technology. Additionally, it scrutinizes hurdles encountered when embracing while spotlighting potential benefits offers both organizations individuals. By grasping effectively confronting challenges, capitalizing on opportunities hand, businesses can fully exploit computing's drive innovation, enhance operational efficiency, achieve unprecedented scalability. Our study aims offer insights emerging trends developments field

Language: Английский

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