Future Internet最新文献_第7页

DDPG-MPCC: An Experience Driven Multipath Performance Oriented Congestion Control DDPG-MPCC：以经验为导向的多路径性能拥塞控制

Future Internet Pub Date : 2024-01-23 DOI: 10.3390/fi16020037

Shiva Raj Pokhrel, Jonathan Kua, Deol Satish, Sebnem Ozer, Jeff Howe, Anwar Walid

引用次数: 0

A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks 全面评述物联网网络中的机器学习对抗性攻击

Future Internet Pub Date : 2024-01-19 DOI: 10.3390/fi16010032

Hassan Khazane, Mohammed Ridouani, Fatima Salahdine, N. Kaabouch

{"title":"A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks","authors":"Hassan Khazane, Mohammed Ridouani, Fatima Salahdine, N. Kaabouch","doi":"10.3390/fi16010032","DOIUrl":"https://doi.org/10.3390/fi16010032","url":null,"abstract":"With the rapid advancements and notable achievements across various application domains, Machine Learning (ML) has become a vital element within the Internet of Things (IoT) ecosystem. Among these use cases is IoT security, where numerous systems are deployed to identify or thwart attacks, including intrusion detection systems (IDSs), malware detection systems (MDSs), and device identification systems (DISs). Machine Learning-based (ML-based) IoT security systems can fulfill several security objectives, including detecting attacks, authenticating users before they gain access to the system, and categorizing suspicious activities. Nevertheless, ML faces numerous challenges, such as those resulting from the emergence of adversarial attacks crafted to mislead classifiers. This paper provides a comprehensive review of the body of knowledge about adversarial attacks and defense mechanisms, with a particular focus on three prominent IoT security systems: IDSs, MDSs, and DISs. The paper starts by establishing a taxonomy of adversarial attacks within the context of IoT. Then, various methodologies employed in the generation of adversarial attacks are described and classified within a two-dimensional framework. Additionally, we describe existing countermeasures for enhancing IoT security against adversarial attacks. Finally, we explore the most recent literature on the vulnerability of three ML-based IoT security systems to adversarial attacks.","PeriodicalId":509567,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139524890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Investigation of Phishing Susceptibility with Explainable Artificial Intelligence 利用可解释人工智能调查网络钓鱼的易感性

Future Internet Pub Date : 2024-01-17 DOI: 10.3390/fi16010031

Zhengyang Fan, Wanru Li, Kathryn B. Laskey, Kuo-Chu Chang

{"title":"Investigation of Phishing Susceptibility with Explainable Artificial Intelligence","authors":"Zhengyang Fan, Wanru Li, Kathryn B. Laskey, Kuo-Chu Chang","doi":"10.3390/fi16010031","DOIUrl":"https://doi.org/10.3390/fi16010031","url":null,"abstract":"Phishing attacks represent a significant and growing threat in the digital world, affecting individuals and organizations globally. Understanding the various factors that influence susceptibility to phishing is essential for developing more effective strategies to combat this pervasive cybersecurity challenge. Machine learning has become a prevalent method in the study of phishing susceptibility. Most studies in this area have taken one of two approaches: either they explore statistical associations between various factors and susceptibility, or they use complex models such as deep neural networks to predict phishing behavior. However, these approaches have limitations in terms of providing practical insights for individuals to avoid future phishing attacks and delivering personalized explanations regarding their susceptibility to phishing. In this paper, we propose a machine-learning approach that leverages explainable artificial intelligence techniques to examine the influence of human and demographic factors on susceptibility to phishing attacks. The machine learning model yielded an accuracy of 78%, with a recall of 71%, and a precision of 57%. Our analysis reveals that psychological factors such as impulsivity and conscientiousness, as well as appropriate online security habits, significantly affect an individual’s susceptibility to phishing attacks. Furthermore, our individualized case-by-case approach offers personalized recommendations on mitigating the risk of falling prey to phishing exploits, considering the specific circumstances of each individual.","PeriodicalId":509567,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139527826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classification Tendency Difference Index Model for Feature Selection and Extraction in Wireless Intrusion Detection 用于无线入侵检测中特征选择和提取的分类倾向差异指数模型

Future Internet Pub Date : 2024-01-12 DOI: 10.3390/fi16010025

C. Tseng, Woei-Jiunn Tsaur, Yueh-Mao Shen

{"title":"Classification Tendency Difference Index Model for Feature Selection and Extraction in Wireless Intrusion Detection","authors":"C. Tseng, Woei-Jiunn Tsaur, Yueh-Mao Shen","doi":"10.3390/fi16010025","DOIUrl":"https://doi.org/10.3390/fi16010025","url":null,"abstract":"In detecting large-scale attacks, deep neural networks (DNNs) are an effective approach based on high-quality training data samples. Feature selection and feature extraction are the primary approaches for data quality enhancement for high-accuracy intrusion detection. However, their enhancement root causes usually present weak relationships to the differences between normal and attack behaviors in the data samples. Thus, we propose a Classification Tendency Difference Index (CTDI) model for feature selection and extraction in intrusion detection. The CTDI model consists of three indexes: Classification Tendency Frequency Difference (CTFD), Classification Tendency Membership Difference (CTMD), and Classification Tendency Distance Difference (CTDD). In the dataset, each feature has many feature values (FVs). In each FV, the normal and attack samples indicate the FV classification tendency, and CTDI shows the classification tendency differences between the normal and attack samples. CTFD is the frequency difference between the normal and attack samples. By employing fuzzy C means (FCM) to establish the normal and attack clusters, CTMD is the membership difference between the clusters, and CTDD is the distance difference between the cluster centers. CTDI calculates the index score in each FV and summarizes the scores of all FVs in the feature as the feature score for each of the three indexes. CTDI adopts an Auto Encoder for feature extraction to generate new features from the dataset and calculate the three index scores for the new features. CTDI sorts the original and new features for each of the three indexes to select the best features. The selected CTDI features indicate the best classification tendency differences between normal and attack samples. The experiment results demonstrate that the CTDI features achieve better detection accuracy as classified by DNN for the Aegean WiFi Intrusion Dataset than their related works, and the detection enhancements are based on the improved classification tendency differences in the CTDI features.","PeriodicalId":509567,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139533000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Future Sustainable Internet Energy-Defined Networking 未来的可持续互联网能源定义的网络

Future Internet Pub Date : 2024-01-09 DOI: 10.3390/fi16010023

Alex Galis

引用次数: 0

A Novel Semantic IoT Middleware for Secure Data Management: Blockchain and AI-Driven Context Awareness 用于安全数据管理的新型语义物联网中间件：区块链和人工智能驱动的情境感知

Future Internet Pub Date : 2024-01-07 DOI: 10.3390/fi16010022

M. Elkhodr, Samiya Khan, E. Gide

{"title":"A Novel Semantic IoT Middleware for Secure Data Management: Blockchain and AI-Driven Context Awareness","authors":"M. Elkhodr, Samiya Khan, E. Gide","doi":"10.3390/fi16010022","DOIUrl":"https://doi.org/10.3390/fi16010022","url":null,"abstract":"In the modern digital landscape of the Internet of Things (IoT), data interoperability and heterogeneity present critical challenges, particularly with the increasing complexity of IoT systems and networks. Addressing these challenges, while ensuring data security and user trust, is pivotal. This paper proposes a novel Semantic IoT Middleware (SIM) for healthcare. The architecture of this middleware comprises the following main processes: data generation, semantic annotation, security encryption, and semantic operations. The data generation module facilitates seamless data and event sourcing, while the Semantic Annotation Component assigns structured vocabulary for uniformity. SIM adopts blockchain technology to provide enhanced data security, and its layered approach ensures robust interoperability and intuitive user-centric operations for IoT systems. The security encryption module offers data protection, and the semantic operations module underpins data processing and integration. A distinctive feature of this middleware is its proficiency in service integration, leveraging semantic descriptions augmented by user feedback. Additionally, SIM integrates artificial intelligence (AI) feedback mechanisms to continuously refine and optimise the middleware’s operational efficiency.","PeriodicalId":509567,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139448816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint Beam-Forming Optimization for Active-RIS-Assisted Internet-of-Things Networks with SWIPT 利用 SWIPT 实现主动 RIS 辅助物联网网络的联合波束成形优化

Future Internet Pub Date : 2024-01-06 DOI: 10.3390/fi16010020

Lidong Liu, Shidang Li, Mingsheng Wei, Jinsong Xu, Bencheng Yu

{"title":"Joint Beam-Forming Optimization for Active-RIS-Assisted Internet-of-Things Networks with SWIPT","authors":"Lidong Liu, Shidang Li, Mingsheng Wei, Jinsong Xu, Bencheng Yu","doi":"10.3390/fi16010020","DOIUrl":"https://doi.org/10.3390/fi16010020","url":null,"abstract":"Network energy resources are limited in communication systems, which may cause energy shortages in mobile devices at the user end. Active Reconfigurable Intelligent Surfaces (A-RIS) not only have phase modulation properties but also enhance the signal strength; thus, they are expected to solve the energy shortage problem experience at the user end in 6G communications. In this paper, a resource allocation algorithm for maximizing the sum of harvested energy is proposed for an active RIS-assisted Simultaneous Wireless Information and Power Transfer (SWIPT) system to solve the problem of low performance of harvested energy for users due to multiplicative fading. First, in the active RIS-assisted SWIPT system using a power splitting architecture to achieve information and energy co-transmission, the joint resource allocation problem is constructed with the objective function of maximizing the sum of the collected energy of all users, under the constraints of signal-to-noise ratio, active RIS and base station transmit power, and power splitting factors. Second, the considered non-convex problem can be turned into a standard convex problem by using alternating optimization, semi-definite relaxation, successive convex approximation, penalty function, etc., and then an alternating iterative algorithm for harvesting energy is proposed. The proposed algorithm splits the problem into two sub-problems and then performs iterative optimization separately, and then the whole is alternately optimized to obtain the optimal solution. Simulation results show that the proposed algorithm improves the performance by 45.2% and 103.7% compared to the passive RIS algorithm and the traditional without-RIS algorithm, respectively, at the maximum permissible transmitting power of 45 dBm at the base station.","PeriodicalId":509567,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139449443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Comprehensive Study and Analysis of the Third Generation Partnership Project’s 5G New Radio for Vehicle-to-Everything Communication 全面研究和分析第三代合作伙伴计划用于车对车通信的 5G 新无线电

Future Internet Pub Date : 2024-01-06 DOI: 10.3390/fi16010021

G. M. N. Ali, Mohammad Nazmus Sadat, Md. Suruz Miah, Sameer Ahmed Sharief, Yun Wang

{"title":"A Comprehensive Study and Analysis of the Third Generation Partnership Project’s 5G New Radio for Vehicle-to-Everything Communication","authors":"G. M. N. Ali, Mohammad Nazmus Sadat, Md. Suruz Miah, Sameer Ahmed Sharief, Yun Wang","doi":"10.3390/fi16010021","DOIUrl":"https://doi.org/10.3390/fi16010021","url":null,"abstract":"Recently, the Third Generation Partnership Project (3GPP) introduced new radio (NR) technology for vehicle-to-everything (V2X) communication to enable delay-sensitive and bandwidth-hungry applications in vehicular communication. The NR system is strategically crafted to complement the existing long-term evolution (LTE) cellular-vehicle-to-everything (C-V2X) infrastructure, particularly to support advanced services such as the operation of automated vehicles. It is widely anticipated that the fifth-generation (5G) NR system will surpass LTE C-V2X in terms of achieving superior performance in scenarios characterized by high throughput, low latency, and enhanced reliability, especially in the context of congested traffic conditions and a diverse range of vehicular applications. This article will provide a comprehensive literature review on vehicular communications from dedicated short-range communication (DSRC) to NR V2X. Subsequently, it delves into a detailed examination of the challenges and opportunities inherent in NR V2X technology. Finally, we proceed to elucidate the process of creating and analyzing an open-source 5G NR V2X module in network simulation-3 (ns-3) and then demonstrate the NR V2X performance in terms of different key performance indicators implemented through diverse operational scenarios.","PeriodicalId":509567,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139449355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Proximal Policy Optimization for Efficient D2D-Assisted Computation Offloading and Resource Allocation in Multi-Access Edge Computing 针对多接入边缘计算中高效 D2D 辅助计算卸载和资源分配的近端策略优化

Future Internet Pub Date : 2024-01-02 DOI: 10.3390/fi16010019

Chen Zhang, Celimuge Wu, Min Lin, Yangfei Lin, William Liu

{"title":"Proximal Policy Optimization for Efficient D2D-Assisted Computation Offloading and Resource Allocation in Multi-Access Edge Computing","authors":"Chen Zhang, Celimuge Wu, Min Lin, Yangfei Lin, William Liu","doi":"10.3390/fi16010019","DOIUrl":"https://doi.org/10.3390/fi16010019","url":null,"abstract":"In the advanced 5G and beyond networks, multi-access edge computing (MEC) is increasingly recognized as a promising technology, offering the dual advantages of reducing energy utilization in cloud data centers while catering to the demands for reliability and real-time responsiveness in end devices. However, the inherent complexity and variability of MEC networks pose significant challenges in computational offloading decisions. To tackle this problem, we propose a proximal policy optimization (PPO)-based Device-to-Device (D2D)-assisted computation offloading and resource allocation scheme. We construct a realistic MEC network environment and develop a Markov decision process (MDP) model that minimizes time loss and energy consumption. The integration of a D2D communication-based offloading framework allows for collaborative task offloading between end devices and MEC servers, enhancing both resource utilization and computational efficiency. The MDP model is solved using the PPO algorithm in deep reinforcement learning to derive an optimal policy for offloading and resource allocation. Extensive comparative analysis with three benchmarked approaches has confirmed our scheme’s superior performance in latency, energy consumption, and algorithmic convergence, demonstrating its potential to improve MEC network operations in the context of emerging 5G and beyond technologies.","PeriodicalId":509567,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139390866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0