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Applied Sciences
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AI Technology and Security in Cloud/Big Data
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AI Technology and Security in Cloud/Big Data

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 August 2025 | Viewed by 2621

Special Issue Editor

Dr. Jisu Park

E-Mail Website
Guest Editor
Department of Computer Science Engineering, Jeonju University, Jeonju 55069, Republic of Korea
Interests: artificial intelligence; big data analysis; cloud computing; cybersecurity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cloud and big data environments have become core elements of technology operations. The cloud provides cost-effective data storage and processing solutions, and big data plays a critical role in analyzing large amounts of data and deriving useful insights. In this environment, artificial intelligence (AI) is used in various areas such as data analysis, predictive modeling, and automation, and security is essential to ensure the safety of data and systems. In other words, the integration of AI and security technologies is becoming increasingly important in cloud and big data environments.

The important topics are as follows:

Data Protection: Data handled in cloud and big data environments are massive and often contain sensitive information. Security is essential to protecting these data.

Ensuring Reliability: For AI models to provide trustworthy results, data must remain accurate and secure. Data integrity and security directly impact the trustworthiness of AI.

Regulatory Compliance: Data protection regulations such as GDPR and CCPA emphasize the importance of data management and security. Enhancing security in cloud and big data environments helps reduce risks associated with legal compliance.

Cyber ​​Threat Response: The integration of AI and security technologies plays a critical role in detecting and responding to cyber attacks. AI-based security solutions can detect and respond to abnormal behavior in real time.

Business Continuity: You can ensure business continuity by protecting your data from security threats. Data loss or service interruption can have a serious impact on your business.

Based on this background and the important topics, it is important to understand how the convergence of AI and security technologies is taking place in cloud and big data environments, as well as the benefits that can be gained and the challenges that must be overcome

  • AI Technology for Big Data;
  • AI Technology in Cloud/Edge/Fog;
  • AI as a Service;
  • Cloud-based AI platform;
  • AI-based Serverless Computing;
  • Data Lake and Lakehouse;
  • Real-time Large-Scale Data Processing and Analysis;
  • Synthetic Data to solve Data Shortage Problem;
  • Security Technology in Cloud Environment: Data Encryption, IAM, IDS, IPS, and MFA;
  • Security Technology in a Big Data: Data Access Control, SIEM, GDPR, and Data Governance;
  • Application Technology utilizing Cloud/Big Data/AI: Security, Education, Network, etc.

Dr. Jisu Park
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cloud
  • big data
  • AI
  • security
  • application technology

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Published Papers (3 papers)

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Research

14 pages, 1529 KiB  
Article
DimAug-TimesFM: Dimension Augmentation for Long-Term Cloud Demand Forecasting in Few-Shot Scenarios
by Xiaoyi Yang, Qiming Zheng, Xiaoyu Zhu, Mianzhang Luo, Zheng Hou, Jiatai Zhang and Yuqing Lan
Appl. Sci. 2025, 15(7), 3450; https://doi.org/10.3390/app15073450 - 21 Mar 2025
Viewed by 307
Abstract
Accurate long-term cloud demand forecasting is critical for optimizing resource procurement and cost management in cloud computing, yet it remains challenging due to dynamic demand trends, limited historical data, and the poor generalization of existing models in few-shot scenarios. This paper proposes DimAug-TimesFM, [...] Read more.
Accurate long-term cloud demand forecasting is critical for optimizing resource procurement and cost management in cloud computing, yet it remains challenging due to dynamic demand trends, limited historical data, and the poor generalization of existing models in few-shot scenarios. This paper proposes DimAug-TimesFM, a dimension-augmented framework for long-term cloud demand forecasting, which addresses these challenges through two key innovations. First, Delivery Period Extracting identifies critical resource delivery phases by analyzing smoothed utilization trends and differencing thresholds, enabling focused modeling on periods reflecting actual demand. Second, Dimension-Augmented TimesFM enhances the pretrained TimesFM model by integrating cross-pool data via Dynamic Time Warping based similarity matching, enriching training data while mitigating distribution discrepancies. Experiments on real-world cloud resource utilization data demonstrate that DimAug-TimesFM significantly outperforms SOTA baselines (e.g., TimesFM, DLinear, PatchTST) in both short-term (16-day) and long-term (64-day and 128-day) forecasting tasks, achieving average reductions 72.9–81.7% in RMSE. DimAug-TimesFM also exhibits better robustness in scenarios where TimesFM fails, attributed to its synergistic integration of temporal feature enhancement and cross-pool data augmentation. This work provides a practical solution for few-shot cloud demand forecasting, enabling enterprises to align resource allocation with dynamic usage patterns and reduce operational costs. Full article
(This article belongs to the Special Issue AI Technology and Security in Cloud/Big Data)
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15 pages, 668 KiB  
Article
PenQA: A Comprehensive Instructional Dataset for Enhancing Penetration Testing Capabilities in Language Models
by Xiaofeng Zhong, Yunlong Zhang and Jingju Liu
Appl. Sci. 2025, 15(4), 2117; https://doi.org/10.3390/app15042117 - 17 Feb 2025
Viewed by 1045
Abstract
Large language models’ domain-specific capabilities can be enhanced through specialized datasets, yet constructing comprehensive cybersecurity datasets remains challenging due to the field’s multidisciplinary nature. We present PenQA, a novel instructional dataset for penetration testing that integrates theoretical and practical knowledge. Leveraging authoritative sources [...] Read more.
Large language models’ domain-specific capabilities can be enhanced through specialized datasets, yet constructing comprehensive cybersecurity datasets remains challenging due to the field’s multidisciplinary nature. We present PenQA, a novel instructional dataset for penetration testing that integrates theoretical and practical knowledge. Leveraging authoritative sources like MITRE ATT&CK™ and Metasploit, we employ online large language models to generate approximately 50,000 question–answer pairs.We demonstrate PenQA’s efficacy by fine-tuning language models with fewer than 10 billion parameters. Evaluation metrics, including the BLEU, ROUGE, and BERTScore, show significant improvements in the models’ penetration testing capabilities. PenQA is designed to be compatible with various model architectures and updatable as new techniques emerge. This work has implications for automated penetration testing tools, cybersecurity education, and decision support systems. The PenQA dataset is available in our GitHub repository. Full article
(This article belongs to the Special Issue AI Technology and Security in Cloud/Big Data)
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18 pages, 1641 KiB  
Article
User Profile Construction Based on High-Dimensional Features Extracted by Stacking Ensemble Learning
by Zhaoyang Wang, Li Li, Ketai He and Zhenyang Zhu
Appl. Sci. 2025, 15(3), 1224; https://doi.org/10.3390/app15031224 - 25 Jan 2025
Viewed by 731
Abstract
Online social networks, as platforms for personal expression, have evolved into complex networks integrating political and social dimensions. This evolution has shifted the focus of network governance from addressing hacking activities to mitigating unpredictable social behaviors, such as the malicious manipulation of public [...] Read more.
Online social networks, as platforms for personal expression, have evolved into complex networks integrating political and social dimensions. This evolution has shifted the focus of network governance from addressing hacking activities to mitigating unpredictable social behaviors, such as the malicious manipulation of public opinion, the doxing of ordinary users, and cyberbullying. However, the sparsity of data and the concealed nature of user behavior pose significant challenges to existing network reconnaissance technologies. In this study, we focus on constructing user profiles on online social network platforms by extracting features to build deep user profiles based on behavioral patterns. Drawing inspiration from the 5Cs principle of credit evaluation, we refine it into a 3Cs principle tailored for user profiling on social network platforms and associate it with user behavioral patterns. To further analyze user behavior, a high-dimensional feature extraction method is proposed using an improved stacking ensemble learning model. Based on experimental data analysis, the most suitable base algorithms for high-dimensional feature extraction are identified. Experimental results demonstrate that the integration of high-dimensional features improved the behavior prediction accuracy of the profiling model by 9.26% on balanced datasets and enhanced the AUC (area under the curve) metric by 3.69% on imbalanced datasets. The proposed method effectively increases the depth and generalization performance of user profiling. Full article
(This article belongs to the Special Issue AI Technology and Security in Cloud/Big Data)
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