Jaime Ortiz Córdoba, Alejandro Molina Zarca^*, Antonio Skármeta

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2307-2333, 2023, DOI:10.32604/iasc.2023.039607

Abstract 5G/Beyond 5G (B5G) networks provide connectivity to many heterogeneous devices, raising significant security and operational issues and making traditional infrastructure management increasingly complex. In this regard, new frameworks such as Anastacia-H2020 or INSPIRE-5GPlus automate the management of next-generation infrastructures, especially regarding policy-based security, abstraction, flexibility, and extensibility. This paper presents the design, workflow, and implementation of a security solution based on Unmanned Aerial Vehicles (UAVs), able to extend 5G/B5G security framework capabilities with UAV features like dynamic service provisioning in specific geographic areas. The proposed solution allows enforcing UAV security policies in proactive and reactive ways to automate UAV dynamic… More >

Qi Liu^1,2, Jing Li^1,2,*, Xianmin Wang^1,*, Wenpeng Zhao¹

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1753-1771, 2023, DOI:10.32604/iasc.2023.039600

Abstract Recent state-of-the-art semi-supervised learning (SSL) methods usually use data augmentations as core components. Such methods, however, are limited to simple transformations such as the augmentations under the instance’s naive representations or the augmentations under the instance’s semantic representations. To tackle this problem, we offer a unique insight into data augmentations and propose a novel data-augmentation-based semi-supervised learning method, called Attentive Neighborhood Feature Augmentation (ANFA). The motivation of our method lies in the observation that the relationship between the given feature and its neighborhood may contribute to constructing more reliable transformations for the data, and further facilitating the classifier to distinguish… More >

Changshun Liu¹, Yan Liu¹, Jingjing Song^1,*, Taihua Xu^1,2

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2059-2075, 2023, DOI:10.32604/iasc.2023.039592

Abstract Attribute reduction, as one of the essential applications of the rough set, has attracted extensive attention from scholars. Information granulation is a key step of attribute reduction, and its efficiency has a significant impact on the overall efficiency of attribute reduction. The information granulation of the existing neighborhood rough set models is usually a single layer, and the construction of each information granule needs to search all the samples in the universe, which is inefficient. To fill such gap, a new neighborhood rough set model is proposed, which aims to improve the efficiency of attribute reduction by means of two-layer… More >

Songi Gwak, Thien-Phuc Doan, Souhwan Jung^*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1773-1786, 2023, DOI:10.32604/iasc.2023.039565

Abstract Containerization is a fundamental component of modern cloud-native infrastructure, and Kubernetes is a prominent platform of container orchestration systems. However, containerization raises significant security concerns due to the nature of sharing a kernel among multiple containers, which can lead to container breakout or privilege escalation. Kubernetes cannot avoid it as well. While various tools, such as container image scanning and configuration checking, can mitigate container workload vulnerabilities, these are not foolproof and cannot guarantee perfect isolation or prevent every active threat in runtime. As such, a policy enforcement solution is required to tackle the problem, and existing solutions based on… More >

Yanhui Zhai^1,2,*, Rujie Chen¹, Deyu Li^1,2

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1833-1851, 2023, DOI:10.32604/iasc.2023.039553

Abstract Decision implication is a form of decision knowledge representation, which is able to avoid generating attribute implications that occur between condition attributes and between decision attributes. Compared with other forms of decision knowledge representation, decision implication has a stronger knowledge representation capability. Attribute granularization may facilitate the knowledge extraction of different attribute granularity layers and thus is of application significance. Decision implication canonical basis (DICB) is the most compact set of decision implications, which can efficiently represent all knowledge in the decision context. In order to mine all decision information on decision context under attribute granulating, this paper proposes an… More >

Lei Yin¹, Chang Sun², Ming Gao³, Yadong Fang⁴, Ming Li¹, Fengyu Zhou^1,*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1587-1608, 2023, DOI:10.32604/iasc.2023.039380

Abstract The solution strategy of the heuristic algorithm is pre-set and has good performance in the conventional cloud resource scheduling process. However, for complex and dynamic cloud service scheduling tasks, due to the difference in service attributes, the solution efficiency of a single strategy is low for such problems. In this paper, we presents a hyper-heuristic algorithm based on reinforcement learning (HHRL) to optimize the completion time of the task sequence. Firstly, In the reward table setting stage of HHRL, we introduce population diversity and integrate maximum time to comprehensively determine the task scheduling and the selection of low-level heuristic strategies.… More >

Dong-Hoon Shin¹, Seo-El Lee², Byeong-Uk Jeon¹, Kyungyong Chung^3,*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1925-1940, 2023, DOI:10.32604/iasc.2023.039317

Abstract Recently, the importance of data analysis has increased significantly due to the rapid data increase. In particular, vehicle communication data, considered a significant challenge in Intelligent Transportation Systems (ITS), has spatiotemporal characteristics and many missing values. High missing values in data lead to the decreased predictive performance of models. Existing missing value imputation models ignore the topology of transportation networks due to the structural connection of road networks, although physical distances are close in spatiotemporal image data. Additionally, the learning process of missing value imputation models requires complete data, but there are limitations in securing complete vehicle communication data. This… More >

Hang Yang^1,2,*, Xunbo Li¹, Witold Pedrycz²

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1531-1551, 2023, DOI:10.32604/iasc.2023.039256

Abstract Energy supply is one of the most critical challenges of wireless sensor networks (WSNs) and industrial wireless sensor networks (IWSNs). While research on coverage optimization problem (COP) centers on the network’s monitoring coverage, this research focuses on the power banks’ energy supply coverage. The study of 2-D and 3-D spaces is typical in IWSN, with the realistic environment being more complex with obstacles (i.e., machines). A 3-D surface is the field of interest (FOI) in this work with the established hybrid power bank deployment model for the energy supply COP optimization of IWSN. The hybrid power bank deployment model is… More >

Óscar Mogollón-Gutiérrez^*, José Carlos Sancho Núñez, Mar Ávila Vegas, Andrés Caro Lindo

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1691-1709, 2023, DOI:10.32604/iasc.2023.039255

Abstract Nowadays, IT systems rely mainly on artificial intelligence (AI) algorithms to process data. AI is generally used to extract knowledge from stored information and, depending on the nature of data, it may be necessary to apply different AI algorithms. In this article, a novel perspective on the use of AI to ensure the cybersecurity through the study of network traffic is presented. This is done through the construction of a two-stage cyberattack classification ensemble model addressing class imbalance following a one-vs-rest (OvR) approach. With the growing trend of cyberattacks, it is essential to implement techniques that ensure legitimate access to… More >

Wafa F. Alfwzan¹, Dumitru Baleanu^2,3,4, Fazal Dayan^5,*, Sami Ullah⁵, Nauman Ahmed^4,6, Muhammad Rafiq^7,8, Ali Raza^4,9

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1909-1924, 2023, DOI:10.32604/iasc.2023.039249

Abstract A susceptible, exposed, infectious, quarantined and recovered (SEIQR) model with fuzzy parameters is studied in this work. Fuzziness in the model arises due to the different degrees of susceptibility, exposure, infectivity, quarantine and recovery among the computers under consideration due to the different sizes, models, spare parts, the surrounding environments of these PCs and many other factors like the resistance capacity of the individual PC against the virus, etc. Each individual PC has a different degree of infectivity and resistance against infection. In this scenario, the fuzzy model has richer dynamics than its classical counterpart in epidemiology. The reproduction number… More >