Network Environment Research Articles

Enabling semi-supervised learning in intrusion detection systems

Intrusion Detection systems (IDS) are alerting cybersecurity tools that analyze network traffic in order to identify suspicious activity and known threats. State of the art IDS rely on supervised machine learning models which are trained to categorize the network flow with a historical labeled dataset. Nonetheless, next-generation networks are characterized as heterogeneous and dynamic. The heterogeneity can make every network environment to be significantly different and the dynamicity means that new threats are constantly emerging. These two factors raise the research question if a supervised machine learning based IDS can work efficiently in a network environment different from the one that generated its labeled training data. In this paper, we first give an answer to this research question and next try to propose a semi-supervised learning approach that can be generalized sufficiently in a different network environment using unlabeled data, taking into consideration that unlabeled data are much easier and cheap to be collected compared to labeled ones. In order to have a proof of concept we made experiments with two labeled datasets CIC-IDS2017, CIC-IDS2018 which are publicly available and one unlabeled dataset PS-Azure2023 which we constructed for this work and make it also publicly available. The results confirm our assumption and the applicability of the semi-supervised learning paradigm for the design of IDS.

The status quo and influencing factors of intrinsic capacity among community-dwelling older adults from the perspective of Ecological Systems Theory: A cross-sectional study

BackgroundAs intrinsic capacity (IC) declines, older adults are at a significantly increased risk of frailty, care dependency, and death. Currently, the research on IC among older adults in China was still insufficient. We aimed to identify the status quo and influencing factors among community-dwelling older adults in China and explore the relationship between IC, external environment, and social network.MethodsA convenience sampling method was used to collect 312 older people from May 2023 to February 2024 in five communities in Chengdu, Sichuan Province. Data were collected using the general information questionnaire, Integrated Care of the Elderly (ICOPE) screening tool, World Health Organization Quality of live scale (WHOQOL-100), and Social Network Scale (LSNS-6).ResultsThe IC score among Chinese community-dwelling older adults was 3.39 ± 1.60, and the prevalence of IC decline was 86.9%. Marital status, age, number of chronic diseases, social network, and external environment were influencing factors of IC, which explained 35.7% of the total variance. External environment and social network were positively correlated with IC.ConclusionsChinese community-dwelling older adults had low IC scores and a high prevalence of IC decline. The government should focus on IC for older adults, especially those who are older, not married or widowed, and suffering from multiple chronic diseases. In addition, the richer the external resources available to older adults, the more social support they received, and the better the IC. These findings could provide a theoretical basis for managing and improving IC in older adults.

Enhancing DNS Performance with Efficient Cryptographic Algorithms: A Comparative Study of DoT Frameworks

The Domain Name System (DNS) is a critical component of the Internet, and its disruption can significantly affect service quality. To enhance security and protect user privacy, encrypted protocols, such as DNS over TLS (DoT), DNS over Quick UDP Internet Connections (DoQ), and DNS over HTTPS (DoH), have been introduced. This study evaluates the performance impact of different cryptographic algorithms within the DoT framework, focusing on how encryption influences DNS query performance and resolver efficiency. Performance evaluations were conducted using various cryptographic algorithms under different client load conditions. Metrics such as response rate, timeout rate, and resource utilization were analyzed to assess the impact of encryption on DNS recursive resolvers. The analysis revealed that the choice of encryption algorithm and client load significantly affect performance. Advanced Encryption Standard-Galois/Counter Mode (AES-GCM) 128 and ChaCha20-Poly1305 demonstrated superior performance, exhibiting higher response rates and lower timeout rates compared to AES-GCM 256. Organizations managing DNS infrastructure should monitor client loads and consider adopting efficient encryption algorithms, such as AES-GCM 128 or ChaCha20-Poly1305. These choices can optimize DNS recursive resolver performance while maintaining robust security in dynamic network environments.

Digital Twin-Driven Virtual Network Architecture for Enhanced Extended Reality Capabilities

Extended Reality (XR) technology is proposed as a key enabler for the development of immersive applications, thus transforming the way users interact within digital environments. In communication networks, XR will be able to introduce immersive network functions with the potential to enhance the network experience. This paper proposes a comprehensive architectural framework designed to support the deployment of XR Functions (XRFs) within a Digital Twin Network (DTN) environment in order to provide immersive functions of any type, including control and management functions, that are not available in the original network. The proposal can be deployed in relevant fields where digital twins are promising, such as medical applications, surveillance systems, autonomous driving and space communications. The proposed architecture must consider the need for minimal hardware and software resources, along with a network topological configuration with the objective of providing a robust yet lightweight framework, allowing devices with limited computing power, such as smartphones and laptops, to benefit from the enhanced network functionalities provided in the Digital Twin Network, without affecting the original network (ON).

Adaptive Random Early Detection Using Deep Reinforcement Learning for Enhanced Congestion Control in Dynamic TCP/IP Networks

AQM is used to meet targets for congestion control and low delay, high throughput in TCP/IP networks. But methods developed for AQM like Random Early Detection (RED) work strictly on the control parameters pre-set on them and thus may not respond very well to changing network conditions. Thus, this paper presents a composite AQM model using DRL based on DQN, and the ability to learn independently regarding the AQM weight parameter of the queue. The fact that DRL is adaptive means the effectiveness of the proposed system is also assured. As it is observed in implementing traditional model-based approach, the stability and performance degrade when tested in different network conditions; however, DRL’s ability to learn independently enables ML to solve network congestion as it happens. Consequently, more stability is achieved, the delay is lesser, more bandwidth is used and the overall packet drop rate is low; the proposed DRL-RED model is ideal for dynamic network environments. The proposed DRL-RED model is compared with the regular RED algorithm for both low density and high-density networks. This proposed model has achieved sustained throughput of up to 49.9 Mbps with 0.949 % reduction on delay and very low PLR of 8.38043%. From the comparative analysis, it is clear that the employment of DRL with RED improves the stock of network throughput, the reduction on packet drop frequency, and the general delay in network situations. There are two main disadvantages of this approach: first, it cannot reach the heavy-traffic, the problem partly solved by model-based approach; second, the values of the congestion-control parameters cannot be reset, an issue eradicated by DRL as a result of its inherent adaptiveness. Consequently, new stability and increased performance can be achieved under various network conditions.

A Study of Transmission Point Selection for Multi-Connectivity in Multi-Band Wireless Networks

In 5th generation mobile communication networks, the frequency band of the millimeter band of 30–100 GHz is supported to provide a transmission speed of 20 Gbps or higher. Furthermore, a huge transmission capacity, i.e., up to 1 Tbps, is one of the main requirements for the 6th generation mobile communication networks. In order to meet this requirement, the terahertz band is considered a new service band. Hence, we consider multi-band network environments, serving the sub-6Hz band, mmWave band, and additionally sub-terahertz band. Furthermore, we introduce the transmission point selection criteria with multiple connections for efficient multi-connectivity operation in a multi-band network environment, serving and receiving multiple connections from those bands at the same time. We also propose a point selection algorithm based on the selection criteria, e.g., achievable data rate. The proposed point selection algorithm attains lower computational complexity with a 2-approximation of the optimal solution. Our simulation results, applying the channel environment and beamforming in practical environments defined by 3GPP, show that selecting and serving multiple transmission points regardless of frequency band performs better than services through single-connectivity operation.

Path Algebra-Driven Classification Solution to Realize User-Centric Performance-Oriented Virtual Network Embeddings

The intense diversity of the Next-Generation Networking environments like 6G and the forthcoming deployment of immersive applications with varied user-specific requirements transform the efficient allocation of resources into a real challenge. Traditional solutions like the shortest path algorithm and mono-constraint methodologies are inadequate to handle customized user-defined performance parameters and effectively classify physical resources according to these intricate demands. This research offers a new evaluation mechanism to successfully replace the aforementioned traditional path ranking and path selection techniques. Specifically, the proposed framework is integrated with optimization-oriented metrics, each indicating a unique aspect of performance for evaluating candidate network paths. The deployed metrics are then algebraically synthesized to provide a distinctive multidimensional description of the examined substrate resources. These primary and composite metrics adhere to the fundamental monotonicity and isotonicity properties of a Path Algebra; hence, the validity and optimality of the proposed evaluation mechanism is guaranteed by design. To tackle the complexity created by the variety of human-centric customization, a novel methodology that analyzes and determines the weighted influence of the synthesized metrics depending on the characteristics of the served user-centric application is also introduced. The chosen suitable weights address performance-oriented mission-critical tailored objectives for adaptive optimizations. Its innovative algebraic design allows it to successfully describe and rank candidate paths in a versatile way, whether in legacy or modern architectures. The experimental data of the first scenario show that 62.5% and 50% of highlighted path evaluations proposed by the shortest path and unidimensional constraint strategies, respectively, suffer from moderate performance-oriented values compared to the proposed framework. Likewise, the results of the second examined scenario reveal that the proposed composite metric yields more suitable path rankings by 50% in contrast to its traditional counterparts, rendering the contested evaluation mechanisms obsolete.

MDTA: An efficient, scalable and fast Multiple Disjoint Tree Algorithm for dynamic environments

Emerging applications such as telemedicine, the tactile Internet or live streaming place high demands on low latency to ensure a satisfactory Quality of Experience (QoE). In these scenarios the use of trees can be particularly interesting to efficiently deliver traffic to groups of users because they further enhance network performance by providing redundancy and fault tolerance, ensuring service continuity when network failure or congestion scenarios occur. Furthermore, if trees are isolated from each other (they do not share common communication elements as links and/or nodes), their benefits are further enhanced since events such as failures or congestion in one tree do not affect others. However, the challenge of computing fully disjoint trees (both link- and node-disjoint) introduces significant mathematical complexity, resulting in longer computation times, which negatively impacts latency-sensitive applications.In this article, we propose a novel algorithm designed to rapidly compute multiple fully (either link- or node-) disjoint trees while maintaining efficiency and scalability, specifically focused on targeting the low-latency requirements of emerging services and applications. The proposed algorithm addresses the complexity of ensuring disjointedness between trees without sacrificing performance. Our solution has been tested in a variety of network environments, including both wired and wireless scenarios.The results showcase that our proposed method is approximately 100 times faster than existing techniques, while achieving a comparable success rate in terms of number of obtained disjoint trees. This significant improvement in computational speed makes our approach highly suitable for the low-latency requirements of next-generation networks.

Edge Intelligence-Driven Joint Offloading and Resource Allocation for Future 6G Industrial Internet of Things

The 6G will undergo an unprecedented transformation to revolutionize the wireless system evolution from connected things to connected intelligence. Additionally, data scattered around the industrial environments can be collected for the sake of enabling intelligent operations. In this paper, the promising multi-access edge computing (MEC) service is introduced into the IIoT system to assist the computation offloading and resource allocation for different compelling applications. Moreover, relying on defining a total cost function as a weighted sum of task delay and energy consumption, a novel deep reinforcement learning (DRL) based framework is proposed to jointly optimize task offloading and resource allocation. More specifically, the task offloading is decomposed with the aid of the new isotone action generation technique (IAGT) and adaptive action aggregation update strategy (3AUS) based on the proposed DRL framework, and the initial problem can be transformed into a convex optimization problem to solve the resource allocation for each IIoT device. Additionally, we periodically renovate the offloading policy in the DRL framework so that our proposed DRL-based decision-making algorithm can beneficially adapt to different network environments. Finally, extensive simulation demonstrate that our proposed algorithm for each IIoT device can obtain quasi-optimal system performance compared with some conventional baseline algorithms

Elliptic curve cryptography: Theory, security, and applications in modern network security

Abstract. Due to the swift advancement of Internet and computer technology in the 21st century, the demand for network security is increasing. Classic cryptographic algorithms like Rivest-Shamir-Adleman (RSA) and Digital Signature Algorithm (DSA) are insufficient in the face of modern network environments, while elliptic curve cryptography (ECC) has become a research hotspot due to its high security and high efficiency. The purpose of this paper is to discuss the theoretical basis, security analysis, and practical application cases of elliptic curve cryptography, to provide readers with a comprehensive understanding and trigger further research and thinking. This paper analyzes the theoretical basis of ECC, including group theory, domain theory, and the definition and properties of elliptic curves, and analyzes the application of ECC in combination with practical application cases, such as the SM2 algorithm. The article first introduces the concept of groups, the definition of domains, and the basic properties of elliptic curves. Then, the security of ECC is analyzed, especially the complexity of the Elliptic Curve Discrete Logarithm Problem (ECDLP) and the selection of key length. In addition, the security of ECC in practical applications is discussed, including digital signatures, key exchange protocols, and applications in blockchain technology. The results show that ECC provides comparable security to traditional public key algorithms at a short key length, and shows strong security and efficiency in practical applications. As technology progresses and new threats arise, research in ECC will also evolve.

Delay-aware resource allocation for partial computation offloading in mobile edge cloud computing

Mobile Edge Cloud Computing (MECC), as a promising partial computing offloading solution, has provided new possibilities for compute-intensive and delay-sensitive mobile applications, which can simultaneously leverage edge computing and cloud services. However, designing resource allocation strategies for MECC faces an extremely challenging problem of simultaneously satisfying the end-to-end latency requirements and minimum resource allocation of multiple mobile applications. To address this issue, we comprehensively consider the randomness of computing request arrivals, service time, and dynamic computing resources. We model the MECC network as a two-level tandem queue consisting of two sequential computing processing queues, each with multiple servers. We apply a deep reinforcement learning algorithm called Deep Deterministic Policy Gradient (DDPG) to learn the computing speed adjustment strategy for the tandem queue. This strategy ensures the end-to-end latency requirements of multiple mobile applications while preventing overuse of the total computing resources of edge servers and cloud servers. Numerous simulation experiments demonstrate that our approach is significantly superior to other methods in dynamic network environments.

Cyber Aggression and Suicidal Ideation in Emerging Adults: Examining the Potential Roles of Depressive Symptoms and Nonsuicidal Self-Injury.

Cyber aggression is prevalent among emerging adults and is associated with a range of mental and behavioral issues. One particularly concerning outcome is the widespread presence of suicidal ideation in this group, which poses a significant threat to their life. However, the specific impact of cyber aggression on suicidal ideation and the longitudinal mechanisms remains unclear. Hence, this study sought to address these gaps by applying the general aggression model (GAM). The investigation utilized three-wave longitudinal data with 6-month intervals from a large sample of Chinese emerging adults (N = 1568; 54.7% female; Mage = 19.70 [SD = 1.44], ranging from 17 to 26 years). The random-intercept cross-lagged panel model (RI-CLPM) was employed to explore the roles of depressive symptoms and nonsuicidal self-injury (NSSI) between cyber aggression and suicidal ideation at both the between- and within-person levels. The findings indicated that, at the between-person level, there were trait-like associations between depressive symptoms and cyber aggression, NSSI, and suicidal ideation. At the within-person level, cyber aggression exhibited unidirectional positive cross-lagged effects on depressive symptoms and NSSI, and could subsequently indirectly lead to suicidal ideation via the contemporaneous associations from depressive symptoms and NSSI to suicidal ideation. These results not only align with GAM but also shed light on the underlying mechanisms linking cyber aggression to suicidal ideation. Also, the findings underscore the significance of maintaining a positive network environment for the well-being of all internet users even cyber aggression perpetrators.

Frequency Planning for Optimized Resource Allocation in High-Density Networks Using Machine Learning

Frequency resource allocation in high-density networks generates significant challenges, particularly in environments with changing user demands in the midst of high congestion. Machine learning-based frequency scheduling offers dynamic resolution for optimizing network resources, such as bandwidth and frequency reuse, by using real-time data. The research work investigates the use of ML algorithms to enhance frequency reuse efficiency, focusing on maximizing network capacity while reducing interference. By integrating predictive models, adaptive frequency reuse strategies can be developed based on both historical and real-time user behavior, ensuring optimal performance during peak demand periods. This research work compares the performance of the Frequency Reuse 3 (FR3) and Frequency Reuse 7 (FR7) in high-density networks environment, emphasizing their percentage efficiency. FR3 displays a steady increase in users, peaking at 1538 users in the evening, with FR7 performing at 27.55% of FR3’s capacity during this period. In contrast, FR7 exhibits a significant evening spike to 2288 users, outperforming FR3 in handling peak traffic due to reduced interference. All through the day, FR7's shows higher percentage performance of 22.15% in the morning and a reduced rate of 10% in the afternoon, showing its relative efficiency at different times and thus better suited for peak demand, while FR3 manages steady traffic under moderate congestion.

Deep learning approaches for protecting IoT devices in smart homes from MitM attacks

The primary objective of this paper is to enhance the security of IoT devices in Software-Defined Networking (SDN) environments against Man-in-the-Middle (MitM) attacks in smart homes using Artificial Intelligence (AI) methods as part of an Intrusion Detection and Prevention System (IDPS) framework. This framework aims to authenticate communication parties, ensure overall system and network security within SDN environments, and foster trust among users and stakeholders. The experimental analysis focuses on machine learning (ML) and deep learning (DL) algorithms, particularly those employed in Intrusion Detection Systems (IDS), such as Naive Bayes (NB), k-Nearest Neighbors (kNN), Random Forest (RF), and Convolutional Neural Networks (CNN). The CNN algorithm demonstrates exceptional performance on the training dataset, achieving 99.96% accuracy with minimal training time. It also shows favorable results in terms of detection speed, requiring only 1 s, and maintains a low False Alarm Rate (FAR) of 0.02%. Subsequently, the proposed framework was deployed in a testbed SDN environment to evaluate its detection capabilities across diverse network topologies, showcasing its efficiency compared to existing approaches.

Research on the Role of Family Education in Preschool Education

With the continuous development of education, the importance of preschool education is becoming more and more apparent. Despite the increasing interest in preschool education, the role of family education is still underutilized. This study focuses on this phenomenon, analyzes the role of the family in preschool education, and points out two problems: the neglect of family education and the wrong intervention of family education. Family education generally has active neglect and passive neglect. Mistakes in homeschooling methods can also limit students’ free development. Based on this, this paper puts forward the following suggestions. Parents and other family educators should pay attention to improving their educational concepts and improving teaching methods. Schools should pay attention to improving the children’s education system and strengthen home-school cooperation. The government and society should also take the initiative to create a healthy network and social environment to provide a good environmental foundation for students’ physical and mental health.

The Impact of Posting and Viewing Selfies on Social Media on Self-Identity: A Case Study of xiaohongshu

The emergence of new social media platforms has significantly influenced adolescent mental health through their engagement in selfie-related behaviours. This paper uses xiaohongshu as a research platform to explore the impact of users’ selfie-related behaviour on their body satisfaction and self-identity on social media. This study employed a questionnaire-based survey to examine 60 xiaohongshu users, aged 19-59, utilizing the selfie-posting frequency scale, self-viewing scale, verbal commentary on physical appearance Scale, body Image states scale, and body surveillance subscale. The results show that selfie editing and posting, selfie viewing, and positive appearance-related commentary positively affect body satisfaction, while negative appearance-related commentary negatively affect body satisfaction. There is no statistically significant association identified between body satisfaction and self-objectification. Adolescents’ psychological issues are influenced, in part, by their selfie-related activities within social networking environments. However, a mindful and rational engagement with these platforms can foster self-identity and contribute to the positive development of youth mental health.

Classrooms to Networks: Applying Connectivism Principles in Pedagogy ```

The integration of digital technologies has led to a significant transformation in educational practices, shifting from traditional classroom models to more dynamic, networked learning environments. This paper explores the concept of connectivism, a learning theory developed by Siemens and Downes, which emphasizes the importance of networks, connectivity, and collaboration in acquiring knowledge. Unlike traditional pedagogies that focus on content delivery, connectivism promotes the creation and navigation of networks to facilitate continuous learning. Key principles of connectivism include the diversity of opinions, the distribution of knowledge across networks, and the critical role of digital tools in learning processes. The paper discusses the practical applications of connectivism approaches in contemporary pedagogy, focusing on how educators can transform traditional classrooms into engaging, networked learning spaces. Despite its theoretical strengths, the implementation of connectivism faces challenges, including technological access, information management, and the need for pedagogical adaptation. Through this exploration, the paper aims to provide insights into how connectivism can enhance educational practices and prepare students for the complexities of a rapidly evolving, networked society.

Adaptive Blockchain-Integrated Nonlinear Federated Learning Framework for Real-Time Intrusion Detection in IoT Fog Networks ABFL-RTID

This research presents the Adaptive Blockchain-Integrated Nonlinear Federated Learning (ABFL-RTID) model, designed for real-time intrusion detection in IoT fog networks. The framework leverages nonlinear learning techniques, such as deep neural networks, to enhance detection capabilities in complex and dynamic network environments. Integrating blockchain ensures decentralized security, while federated learning preserves data privacy by enabling local model training on edge devices. The nonlinear models improve adaptability, accurately identifying sophisticated intrusion patterns while securely validating updates through blockchain. Simulations show the framework achieving 96.8% detection accuracy with response times of 150 ms, demonstrating superior scalability and adaptability under changing network conditions. This study provides a practical approach for building resilient and secure intrusion detection systems, enhancing data integrity and privacy without the delays of traditional, centralized models.

Latency‐Sensitive Service Function Chains Intelligent Migration in Satellite Communication Driven by Deep Reinforcement Learning

ABSTRACTSatellite communication technology solves the problem that the traditional wired network infrastructure is difficult to achieve global communication coverage. However, factors such as satellite orbits introduce frequent changes to the network topology, and challenges like satellite failures and communication link interruptions are prevalent. In the face of these issues, service function chain (SFC) migration becomes a crucial method for swiftly adjusting SFCs during faults, maintaining service continuity and availability. This article proposes a latency‐sensitive SFC migration algorithm tailored to satellite networks. The algorithm first models the satellite network as a multi‐domain virtual network, capturing the constraints faced during SFC migration. Subsequently, a deep reinforcement learning algorithm integrated attention mechanism is designed to more accurately capture and understand the complex network environment and dynamic satellite network topology and derive optimal SFC migration strategies for superior performance. Finally, through experimentation and evaluation of the deep reinforcement learning‐driven latency‐sensitive service function chain intelligent migration algorithm (LS‐SFCM) in satellite communication, this study validates the effectiveness and superior performance of the algorithm in latency‐sensitive scenarios. It provides a new avenue for enhancing the service quality and efficiency of satellite communication networks.

Research on the Application of AI Technologies in Computer Network Data Analysis

In recent years, with the development of the Internet of Things and smart devices, the Future Internet project has driven innovation in network architecture by combining different models and resources. To manage complexity, creating abstract models has become a critical step in the field of computer networks. This study explores the application of AI techniques in network data analysis, demonstrating the possibilities of AI for real-time monitoring, anomaly detection, and the future development of AI. Despite the promising applications of AI technology, challenges such as privacy protection and dealing with cyber threats still need to be overcome. Future research should develop safer and more flexible AI algorithms to adapt to complex network environments.