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3rd International conference on Intelligent systems, Advanced Computing, and Communication, ISACC 2025

作者： Lo, Nogaye Mbacke Babou, Cheikh Saliou Niang, Ibrahima Cheikh Anta Diop University Laboratoire Informatique de Dakar Dakar Senegal Tokyo184-8795 Japan

ISBN: (纸本)9798331523893

The rapid development of the internet of Things (IoT) has led to an exponential increase in data volumes offloaded to edge computing for rapid processing and analysis close to IoT devices. One of the major challenges of edge computing is better use of network performance in terms of latency, bandwidth, reliability, and security. Most IoT applications such as autonomous vehicles, connected health systems, augmented reality, industrial monitoring, and smart cities operate in real-time and generally have quality of service (QoS) constraints when transmitting data in the network. In addition, the Software-Defined networking (SDN) paradigm with its centralized control and configuration of network devices can make decision-making dynamic on the transmission policy of data flows in the Edge-IoT system. However, these traditional SDN approaches based on a centralized controller have shown their limitations in terms of scalability, efficiency, and speed. To address these QoS issues, we propose a novel hybrid SDN controller-based architecture for effective QoS management in Edge-IoT network (HAQM) which is a promising solution for real-time IoT applications that are latency-sensitive, providing centralized control while using local resources to reduce latency. This sophisticated architecture marks a major advancement in edge-IoT network management, meeting the performance requirements of modern IoT applications. © 2025 IEEE.

关键词： Quality of service

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TCP CUBIC with HyStart: Congestion control for Satellite Communication in OMNeT++ INET 29

TCP CUBIC with HyStart: Congestion Control for Satellite Com...

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29th IEEE Asia Pacific conference on Communications, APCC 2024

作者： Martino, Luigi Deutschmann, Jorg Hielscher, Kai-Steffen German, Reinhard Friedrich-Alexander-Universität Erlangen-Nürnberg Computer Networks and Communication Systems Germany

ISBN: (纸本)9798331517816

In the context of satellite communication, unique challenges arise, such as long delay links that can impact the network performance also at the Transport Layer. With regard to the Transmission control Protocol (TCP), congestion control plays a crucial role in managing network traffic and ensuring efficient data transmission over the internet. This paper wants to give an overview of how TCP congestion control mechanisms adapt to mitigate the effects of long link delays, highlighting the importance of optimizing protocols for these specific environments. Within this context, where signals travel extended distances, traditional TCP algorithms designed for terrestrial networks with relatively low latency, may struggle to maintain optimal performance. As a result, specialized congestion control algorithms, like TCP CUBIC, can be employed to address these challenges. Through the integration of TCP CUBIC with HyStart within a simulation framework based on OMNeT++ and INET, this study sheds light on the importance of tailoring congestion control strategies to suit specific network environments including satellite communication. © 2024 IEEE.

关键词： Transmission control protocol

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A study on Anomaly-based Intrusion Detection systems Employing Supervised Deep Learning Techniques 8

A study on Anomaly-based Intrusion Detection Systems Employi...

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8th International conference on Inventive systems and control, ICISC 202

作者： Shaffi, Abubucker.S. Chacko, John Velloreuzhathil Eliyan, Greeshma Balaji, S. Gulf College Department of Computing Sciences Oman Al Zahra College for Women Department of Information Technology Ghala Muscat Oman

ISBN: (纸本)9798350386578

The rise of smart cities, driverless automobiles, smart watches, and mobile banking has led to increased reliance on the internet. Although technology has enormous advantages for people and society, it also introduces threats. Cyber attacks are more common in this digital world and the intruders are working hard to enter into the business websites or an organization data server. Hence, integrating an intrusion detection system (IDS) is essential in the security environment because it enables IT infrastructure to resist threats. Conventional IDS are limited to detect only sophisticated attacks and fails to detect the hidden and other anomalies that occur in the network systems. An accurate and strong approach for IDS must be created to solve this difficulty for the successful functioning of businesses. The present study explores the use of supervised Deep Learning (DL) techniques and recommends an effective model for anomaly detection. The performance evaluation of the model is performed using NSL-KDD dataset and KDDcup99 and the explored DL models in this study are compared in terms of accuracy and precision. © 2024 IEEE.

关键词： network intrusion

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Few-Shot Class-Incremental Learning for network Intrusion Detection systems

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2024年 5卷 6736-6757页

作者： Di Monda, Davide Montieri, Antonio Persico, Valerio Voria, Pasquale De Ieso, Matteo Pescape, Antonio Univ Napoli Federico II Dipartimento Ingn Elettr & Tecnol Informaz I-80125 Naples Italy IMT Sch Adv Studies I-55100 Lucca Italy

In today's digital landscape, critical services are increasingly dependent on network connectivity, thus cybersecurity has become paramount. Indeed, the constant escalation of cyberattacks, including zero-day exploits, poses a significant threat. While network Intrusion Detection systems (NIDSs) leveraging machine-learning and deep-learning models have proven effective in recent studies, they encounter limitations such as the need for abundant samples of malicious traffic and full retraining upon encountering new attacks. These limitations hinder their adaptability in real-world scenarios. To address these challenges, we design a novel NIDS capable of promptly adapting to classify new attacks and provide timely predictions. Our proposal for attack-traffic classification adopts Few-Shot Class-Incremental Learning (FSCIL) and is based on the Rethinking Few-Shot (RFS) approach, which we experimentally prove to overcome other FSCIL state-of-the-art alternatives based on either meta-learning or transfer learning. We evaluate the proposed NIDS across a wide array of cyberattacks whose traffic is collected in recent publicly available datasets to demonstrate its robustness across diverse network-attack scenarios, including malicious activities in an internet-of-Things context and cyberattacks targeting servers. We validate various design choices as well, involving the number of traffic samples per attack available, the impact of the features used to represent the traffic objects, and the time to deliver the classification verdict. Experimental results witness that our proposed NIDS effectively retains previously acquired knowledge (with over 94% F1-score) while adapting to new attacks with only few samples available (with over 98% F1-score). Thus, it outperforms non-FSCIL state of the art in terms of classification effectiveness and adaptation time. Moreover, our NIDS exhibits high performance even with traffic collected within short time frames, achieving 95% F1-score whi

关键词： Power capacitors Training Metalearning Computer crime Denial-of-service attack Transfer learning network intrusion detection Telecommunication traffic Vectors Traffic control Attack-traffic classification deep learning few-shot class-incremental learning network intrusion detection system network security

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TANGO: Secure Collaborative Route control across the Public internet 21

TANGO: Secure Collaborative Route Control across the Public ...

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21st USENIX Symposium on networked systems Design and Implementation, NSDI 2024

作者： Birge-Lee, Henry Yoo, Sophia Herber, Benjamin Rexford, Jennifer Apostolaki, Maria Princeton University United States

ISBN: (纸本)9781939133397

As the demands of modern latency-critical applications grow, major service providers are seeking to meet those demands by expanding their infrastructure to the edge and offering global connectivity through private WANs or network-as-a-Service solutions. Unfortunately, these approaches are costly for smaller edge networks and lead to internet consolidation. Worse, since the public internet suffers from limited visibility and control over interdomain routing, smaller edges today are left with poor alternatives outside of joining the hypergiants. As a new alternative, we introduce TANGO, which enables smaller edges to expose paths and exert route control over the public internet without relying on third parties or cooperation from the internet core, to dynamically meet the performance needs of their customers. We show that, using collaboration, TANGO edges can jointly (i) expose more BGP-compliant wide-area paths via coordinated BGP advertisements;(ii) collect fine-grained, trustworthy telemetry using cryptographically-protected custom headers;and (iii) dynamically reroute traffic in the data plane. TANGO innovates in both the control and data planes, and runs on a programmable switch or in eBPF. Our internet-scale experiments uncover rich path diversity, exposing paths that outperform the default BGP path 75-100% of the time for 20 edge pairs across multiple continents, while reducing latency by up to 39% compared to the default. © 2024 Proceedings of the 21st USENIX Symposium on networked systems Design and Implementation, NSDI 2024. All rights reserved.

关键词： systems analysis

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SIoV Mobility Management Using SDVN-Enabled Traffic Light Cooperative Framework

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ACM TRANSACTIONS ON CYBER-PHYSICAL systems 2024年第3期8卷 1-24页

作者： Kumar, Neetesh Singh, Navjot Sachan, Anuj Chaudhry, Rashmi Indian Inst Technol Roorkee Roorkee Uttarakhand India Indian Inst Informat Technol Allahabad Prayagraj Uttar Pradesh India Netaji Subhas Univ Technol Delhi India

Social internet of Vehicles (SIoV) is an emerging connected vehicular networking framework among specialized social vehicles to share and disseminate important information like traffic updates, weather conditions, parking slots, so on. This study aims to form an SIoV network among emergency vehicles for their frequent communication to improve the throughput, average waiting time, queue length, and speed during vehicular movement while crossing the intersection in the city. To address this, we propose a novel Smart Traffic Light controller (STLC)-assisted Software-Defined Vehicular networking (SDNV)-enabled SIoV framework for emergency vehicles. Emergency vehicles form an SIoV network by utilizing SDVN architecture in vehicle-to-vehicle and vehicle-to-infrastructure communication. The SDVN module is used to offer two essential services: (1) SIoV-based road-lane prioritization and (2) congestion prevention signal generation for the STLC. An SDVN-MP algorithm is proposed to generate an effective traffic light control signal with an SDVN controller feedback signal. Furthermore, to improve the SIoV movement in the city, two levels of prioritization are done: (1) SIoV and (2) the road lane with SIoV. The first level of prioritization is to assign higher weightage to the social vehicular entities, and the second level is to prioritize the respective road lane based on SIoV quantity. The proposed framework is validated through a realistic simulation study on the Indian city OpenStreetMap utilizing the Simulation of Urban MObility simulator. The experimental findings demonstrate that the SDVN-MP model enhances (state-of-the-art) comparative performance by 22.5-55.2%, 1.2-82.7%, 1.6-38.4%, and 1.8-12.4% for average waiting time, average speed, average queue length, and average throughput metrics, respectively.

关键词： Social internet of vehicles software-defined vehicular networking traffic light controller scheduling

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Citywide LoRa network Deployment and Operation: Measurements, Analysis, and Implications 21

Citywide LoRa Network Deployment and Operation: Measurements...

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21st ACM conference on Embedded networked Sensor systems (SenSys)

作者： Tong, Shuai Wang, Jiliang Yang, Jing Liu, Yunhao Zhang, Jun Tsinghua Univ Beijing Peoples R China ENNEW Technol Co Ltd Chenzhou Peoples R China

ISBN: (纸本)9798400704147

LoRa, as a representative Low-Power Wide-Area network (LPWAN) technology, holds tremendous potential for various city and industrial applications. However, as there are few real large-scale deployments, it is unclear whether and how well LoRa can eventually meet its prospects. In this paper, we demystify the real performance of LoRa by deploying and measuring a citywide LoRa network, named CityWAN, which consists of 100 gateways and 19,821 LoRa end nodes, covering an area of 130 km2 for 12 applications. Our measurement focuses on the following perspectives: (i) performance of applications running on the citywide LoRa network;(ii) Infrastructure efficiency and deployment optimization;(iii) Physical layer signal features and link performance;(iv) Energy profiling and cost estimation for LoRa applications. The results reveal that LoRa performance in urban settings is bottlenecked by the prevalent blind spots, and there is a gap between the gateway efficiency and network coverage for the infrastructure deployment. Besides, we find that LoRa links at the physical layer are susceptible to environmental variations, and LoRa and other LPWANs show diverse costs for different scenarios. Our measurement provides insights for large-scale LoRa network deployment and also for future academic research to fully unleash the potential of LoRa.

关键词： LoRa network measurement internet of Things

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Federated learning-based intrusion detection system for industrial internet of Things: enhancing security and efficiency 4

Federated learning-based intrusion detection system for indu...

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4th International conference on network Communication and Information Security, ICNCIS 2024

作者： Sun, Yan Liu, Caiyun Weng, Ying Liu, Yitong China Industrial Control Systems Cyber Emergency Reponse Team Beijing100040 China

ISBN: (数字)9781510688261

ISBN: (纸本)9781510688254

As the Industrial internet of Things (iioT) rapidly evolves, cybersecurity issues have become increasingly prominent. Traditional centralized intrusion detection methods face significant challenges, including privacy, security, and computational resource limitations with large-scale heterogeneous data. This paper proposes a federated learning-based intrusion detection method, combining Convolutional Neural networks (CNN) and Gated Recurrent Units (GRU) with Isolation Trees for anomaly detection and removal. This approach addresses the non-independent and identically distributed (non-iiD) data in iioT and provides personalized local model training. Experimental results show that the proposed method significantly improves intrusion detection accuracy and real-time performance. © 2025 SPIE.

关键词： Federated learning

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A Comprehensive Survey on IPv6 Over Low Power Wireless Personal Area networks (6LoWPAN) 1

A Comprehensive Survey on IPv6 Over Low Power Wireless Perso...

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1st International conference on Social and Sustainable Innovations in Technology and Engineering (SASI-ITE)

作者： Veenadhari, C. H. Lakshmi Kandasamy, Selvaradjou Puducherry Technol Univ Dept ofComputer Sci & Engn Pondicherry 605014 India

ISBN: (纸本)9798350360806;9798350360790

The internet of Things (IoT) refers to a network of physical objects embedded with sensors, software, and networking capabilities, enabling them to collect and exchange data over the internet. Within IoT applications, Low Power Wireless Personal Area networks (LP-WPANs) hold significant importance. One of the primary challenges in IoT implementation involves connecting devices that possess low power and limited resources. To overcome this challenge, the IPv6 Over Low Power Wireless Personal Area networks (6LoWPAN) protocol stack was developed. Within 6LoWPAN networks, congestion arising from excessive network traffic leads to a notable decline in network performance and negatively impacts Quality of Service (QoS). This paper presents the 6LoWPAN network, specifically designed to facilitate the transmission of IPv6 packets over IEEE 802.15.4 wireless networks. It outlines the components of the 6LoWPAN protocol stack, including the adaptation layer, network layer, and transport layer. Additionally, the paper explores congestion within 6LoWPAN networks and discusses various congestion control algorithms proposed to mitigate this issue. The paper explores the applications of GLoWPAN, focusing on its potential in the IoT ecosystem. It highlights the benefits of 6LoWPAN for IoT applications, including its ability to support large numbers of low-power devices and compatibility with existing IPv6-based systems. Finally, the paper discusses security issues in 6LoWPAN.

关键词： internet of Things Low power and Personal Area networks congestion control Protocol stack

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Effective Intrusion Detection in Heterogeneous internet-of-Things networks via Ensemble Knowledge Distillation-based Federated Learning 59

Effective Intrusion Detection in Heterogeneous Internet-of-T...

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59th Annual IEEE International conference on Communications (IEEE ICC)

作者： Shen, Jiyuan Yang, Wenzhuo Chu, Zhaowei Fan, Jiani Niyato, Dusit Lam, Kwok-Yan Nanyang Technol Univ Strateg Ctr Res Privacy Preserving Technol & Syst Singapore Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore

ISBN: (纸本)9781728190549

With the rapid development of low-cost consumer electronics and cloud computing, internet-of-Things (IoT) devices are widely adopted for supporting next-generation distributed systems such as smart cities and industrial control systems. IoT devices are often susceptible to cyber attacks due to their open deployment environment and limited computing capabilities for stringent security controls. Hence, Intrusion Detection systems (IDS) have emerged as one of the effective ways of securing IoT networks by monitoring and detecting abnormal activities. However, existing IDS approaches rely on centralized servers to generate behaviour profiles and detect anomalies, causing high response time and large operational costs due to communication overhead. Besides, sharing of behaviour data in an open and distributed IoT network environment may violate on-device privacy requirements. Additionally, various IoT devices tend to capture heterogeneous data, which complicates the training of behaviour models. In this paper, we introduce Federated Learning (FL) to collaboratively train a decentralized shared model of IDS, without exposing training data to others. Furthermore, we propose an effective method called Federated Learning Ensemble Knowledge Distillation (FLEKD) to mitigate the heterogeneity problems across various clients. FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results on the public dataset CICIDS2019 demonstrate that the proposed approach outperforms local training and traditional FL in terms of both speed and performance and significantly improves the system's ability to detect unknown attacks. Finally, we evaluate our proposed framework's performance in three potential real-world scenarios and show FLEKD has a clear advantage in experimental results.

关键词： Intrusion Detection System Federated Learning internet of Things Knowledge Distillation Data Heterogeneity

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