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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Tilwari, Valmik Sharma, Debashree Solanki, Sourabh Chakraborty, Nilotpal Maduranga, M.W.P. Indian Institute of Information Technology Department of Electronics & Communication Engineering Guwahati India National Institute of Technology Department of Electronics & Communication Engineering Warangal India Indian Institute of Information Technology Department of Computer Science Engineering Guwahati India General Sir Jhon Kotelawala Defence University Department of Computer Engineering Colombo Sri Lanka

ISBN: (纸本)9798350305463

The optimal solution of creating a communications network in isolated places is still a challenging task. Establishing end-to-end connectivity via mobile ad-hoc networks (MANETs) is a better solution for the above issue. However, the limitation of the mobility of nodes and battery resources between the overlay and mobile ad-hoc network protocols is still a challenge for trustable wireless connections. Therefore, this paper presented a routing scheme named Mobility and Energy consumption aware multipath routing (MEMR) in mobile ad-hoc networks. The proposed routing scheme provides reliable communication with stable route and high lifetime routes to support efficient data transmission in the network. For this purpose, it uses a multicriteria decision-making approach to select the intermediate nodes with the highest energy and lowest mobility for selecting the optimal route. The obtained results indicate that the proposed routing method can significantly improve the overall network performance and deliver packets with acceptable throughput. At the same time, it reduces the energy consumption of the nodes compared with the conventional MP-OLSRv2 routing protocol. © 2024 IEEE.

关键词： Energy utilization

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Designing a Model for Predicting Asthma in Adolescent Using Map Reduce and Federated Learning

Designing a Model for Predicting Asthma in Adolescent Using ...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Ekpo, Raphael Henshaw Chukwudi Osamor, Victor Azeta, Ambrose A. Akindeji, Kayode Orelaja, Adeyinka Elizabeth Covenant University Department of Computer and Information Sciences Km 10 Idiroko Road Ogun State Ota Nigeria Namibia University of Science and Technology Department of Software Engineering Windhoek Namibia Durban University of Technology Faculty of Engineering and the Built Environment Department of Electrical Power Engineering South Africa Department of Computer Science and Quantitative Methods ClarksvilleTN United States

ISBN: (纸本)9798350358155

This study aims at designing Map Reduce and federated learning-based asthma prediction model for adolescent to provide answers to the problems associated with the existing asthma prediction model for adolescent. This study leveraged on two diverse datasets: the Nigerian Hospital Asthma dataset and the National Survey of Children Health dataset for benchmarking purposes. Symmetrical uncertainty and normalization interaction were employed for feature selection. The model was trained using Federated Artificial Intelligence (AI) Technology Enabler (FATE) and complemented with XGBoost model and one central server for federated algorithm averaging. The study was implemented with python programming language on Google Collaboratory environment. The results of the analysis showed considerable high accuracy of 0.98, precision (0.98%), recall (0.98%) and F1-socre (0.99%) for the asthmatic class and precision of 0.98%, recall of 0.98%, as well as F1-score of 0.99% for the non-asthmatic class. The implemented model was benchmark with the existing asthma prediction models in the literature. Simulated attack was also performed using implemented model with and without Map Reduce. This study concludes that the researchers' model has the potentials to outperform some of the existing machine learning models on asthma prediction in adolescent, thereby facilitating collaborative sharing of the model with other hospitals, protect the patient information from leakage, meeting the criteria of healthcare data protection regulation as well as beneficial for enhancing the security of the dataset. © 2024 IEEE.

关键词： Federated learning

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Towards Robust Cyber Attack Taxonomies: A Survey with Requirements, Structures, and Assessment

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ACM Computing Surveys 2025年第8期57卷 1-36页

作者： Santos, Paulo Roberto Da Paz Ferraz Resende, Paulo Angelo Alves Gondim, João José Costa Drummond, André Costa Department of Computer Science University of Brasilia Brasilia Brazil C4I and Cyber Center George Mason University Fairfax United States GigaCandanga Association Brasilia Brazil Department of Electrical Engineering University of Brasilia Brasilia Brazil Institute of Computer and Network Engineering Technische Universität Braunschweig Braunschweig Germany

Cyber attacks have become a growing threat in today's interconnected society, and taxonomies play a crucial role in understanding and preventing these attacks. However, the lack of comprehensive assessment methods for evaluating attack taxonomies represents a significant gap in the literature, hindering their development and applicability. This article aims to address this gap by conducting a survey of 20 attack taxonomies published between 2011 and 2022 and evaluating them with a novel set of qualitative and quantitative assessment criteria, grounded in fundamental taxonomy requirements and key structural attributes. In pursuit of clear and objective assessment criteria, the authors investigated the main taxonomy properties in the literature, identifying dependencies and relationships. This investigation extracted the fundamental requirements for a relevant and widely accepted attack taxonomy in the cybersecurity community. Noteworthy structural aspects, such as organization, scheme, labeling, and approach, are also addressed, considering their impact on taxonomy effectiveness and applicability constraints. Finally, the article poses some open questions and challenges, along with suggestions for future research directions. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Cyber attacks

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Global Positioning of Underwater Nodes Using Airbome-formed Visual Light Beams and Acoustic Ranging 59

Global Positioning of Underwater Nodes Using Airbome-formed ...

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59th Annual IEEE International Conference on Communications, ICC 2024

作者： Saif, Jaeed Bin Younis, Mohamed Choa, Fow-Sen Ahmed, Akram University of Maryland Baltimore County Department of Computer Science and Electrical Engineering BaltimoreMD United States King Fahd University of Petroleum and Minerals Computer Engineering Department Dhahran Saudi Arabia

ISBN: (纸本)9781728190549

In many applications of underwater networks, surface nodes cannot be employed for security and logistical reasons. For these scenarios an airborne unit ought to reach the underwater nodes directly from the air in order to provide commands to and configure the network. In particular, the airborne unit should facilitate localizing the underwater nodes and enable the establishment of a global coordinate system. Visible Light Communication (VLC) stands out as a prime choice for communication across the air-water interface. Using VLC, an underwater node uses the light intensity to infer proximity of the beam incident point. When receiving a sufficient number of VLC transmissions, prior work could localize the underwater nodes. However, such an approach requires very fine-grained area coverage, especially in shallow water environments, which is not practical in many application scenarios. This paper tackles such a limitation by introducing a novel Hybrid Airborne-Enabled Underwater Localization (HAUL) method. HAUL leverages knowledge of the underwater network topology in the localization process. Specifically, both acoustic ranging and light intensity measurements are used to estimate the node's global coordinates. HAUL is validated through extensive MATLAB simulations, demonstrating its effectiveness in calculating accurate positions. © 2024 IEEE.

关键词： MATLAB

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A New Technique of Cellular-Automata-Based PUF Design in Nanoscale Crossbar Circuits 6th

A New Technique of Cellular-Automata-Based PUF Design in Nan...

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6th International Conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Das, Rohit Basu, Subhashree Kule, Malay Department of Information Technology St. Thomas’ College of Engineering and Technology Kolkata India Department of Computer Technology Indian Institute of Engineering Science and Technology Howrah Shibpur India

ISBN: (纸本)9789819780891

The increasing demand in the VLSI technology is also proportionately increasing the threats to these ICs. Hardware Trojan is one such threat that once inserted into the chip can cause malfunctioning of the chip or degradation in performance of the chip. A physical unclonable function (PUF) is one feature that can be a preventive shield against the Trojans. The PUF provides each device with a distinct identity, and in higher-level security architecture, this identity serves as a trust anchor. This chapter uses cellular automata (CA) paradigm implemented in nano-crossbar circuits to impart these unique features. Attacks are designed on the implemented architecture, and the obtained results immediately reflect the presence of some attacks. The results prove that CA-based design can be a competitive and encouraging contender for PUF primitives. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Automata theory

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Unleashing the Power of STAR-RIS for Enhanced Connectivity in Counteracting Random Blockages

Unleashing the Power of STAR-RIS for Enhanced Connectivity i...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhu, Zengjie Huang, Xiaoxia Li, Pan Lin, Phone Sun Yat-sen University School of Electronics and Communication Engineering China Hangzhou Dianzi University School of Computer Science China National Taiwan University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350351255

Reconfigurable Intelligent Surfaces (RISs) have emerged as a promising technology for network performance enhancement by intelligently manipulating the propagation environment. In contrast to conventional reflecting-only RIS, the simultaneous transmitting and reflecting RIS (STAR-RIS) extends coverage to 360 degrees through transmission and reflection, offering more flexibility in channel reconfiguration. The application of STAR-RIS in wireless networks holds tremendous potential for significantly improving network connectivity by fostering cascaded paths. However, it remains open on the deployment strategy for STAR-RIS to guarantee network connectivity cost-effectively, especially when considering random blockages. In this paper, we focus on investigating the impact of STAR-RIS on network connectivity in blockage-prone scenarios. Leveraging percolation theory, we derive upper and lower bounds for the critical density of nodes in both no-STAR-RIS and STAR-RIS-aided networks. Our results quantify the impact of STAR-RIS on network connectivity, demonstrating its significant enhancement on network connectivity and providing valuable insights into cost-effective deployment schemes for STAR-RIS. © 2024 IEEE.

关键词： Cost effectiveness

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Fire detection method based on improved DETR 4

Fire detection method based on improved DETR

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4th International Symposium on AI, ISAI 2024

作者： Ge, Ruize Hu, Xiaoyin Xiao, Duo Zhejiang University College of Contral Science and Engineering China Hangzhou City University School of Information and Electrical Engineering Hangzhou China Hangzhou City University Hangzhou School of Computer and Computing Science China

ISBN: (纸本)9798350342482

Fire, as a type of disaster, poses a significant threat to both life and property safety. Therefore, timely and accurate detection of fire occurrences is of utmost importance. However, current fire detection methods that rely on traditional sensors suffer from limitations such as a high false alarm rate and extended response time. In this manuscript, we propose a novel fire detection method based on an enhanced version of DETR that leverages flame detection. Our approach entails several key modifications. Firstly, we replace the bounding box loss function of the original DETR with the alpha-CIoU loss function. This substitution enhances the accuracy and efficiency of our fire detection method. Secondly, we introduce relative position encoding to replace the sine-cosine position encoding in the transformer module. This adjustment aims to improve the model's ability to capture spatial relationships between flame instances, resulting in more precise detection outcomes. Furthermore, we incorporate multiscale features by modifying the backbone of DETR. This addition enables our algorithm to effectively handle variations in flame sizes and appearances, contributing to improved detection performance across different fire scenarios. Finally, we apply gradient clipping to the DETR optimizer to stabilize the training process and mitigate the potential issue of exploding gradients. To evaluate our proposed method, we train and test the model using a self-generated dataset specifically tailored for flame detection. Experimental results indicate that our improved DETR algorithm achieves an average accuracy of 92.27%, which represents a substantial 3.81% enhancement over the original DETR. Moreover, through ablation experiments, we demonstrate the effectiveness of each individual improvement in our approach. © 2024 IEEE.

关键词： Premixed flames

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An Optimal Deep Learning for Cooperative Intelligent Transportation System

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computers, Materials & Continua 2022年第7期72卷 19-35页

作者： K.Lakshmi Srinivas Nagineni E.Laxmi Lydia A.Francis Saviour Devaraj Sachi Nandan Mohanty Irina V.Pustokhina Denis A.Pustokhin Department of Computer Science and Engineering Periyar Maniammai Institute of Science&TechnologyThanjavur613403India Department of Computer Science and Engineering KG Reddy College of Engineering and TechnologyTelangana500075India Department of Computer Science and Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Computer Science and Engineering Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Engineering College of Engineering PunePuneMaharashtra411005India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia

Cooperative Intelligent Transport System(C-ITS)plays a vital role in the future road traffic management system.A vital element of C-ITS comprises vehicles,road side units,and traffic command centers,which produce a massive quantity of data comprising both mobility and service-related *** the extraction of meaningful and related details out of the generated data,data science acts as an essential part of the upcoming C-ITS *** the same time,prediction of short-term traffic flow is highly essential to manage the traffic *** to the rapid increase in the amount of traffic data,deep learning(DL)models are widely employed,which uses a non-parametric approach for dealing with traffic flow *** paper focuses on the design of intelligent deep learning based short-termtraffic flow prediction(IDL-STFLP)model for C-ITS that assists the people in various ways,namely optimization of signal timing by traffic signal controllers,travelers being able to adapt and alter their routes,and so *** presented IDLSTFLP model operates on two main stages namely vehicle counting and traffic flow *** IDL-STFLP model employs the Fully Convolutional Redundant Counting(FCRC)based vehicle count *** addition,deep belief network(DBN)model is applied for the prediction of short-term traffic *** further improve the performance of the DBN in traffic flow prediction,it will be optimized by Quantum-behaved bat algorithm(QBA)which optimizes the tunable parameters of *** results based on benchmark dataset show that the presented method can count vehicles and predict traffic flowin real-time with amaximumperformance under dissimilar environmental situations.

关键词： Cooperative intelligent transportation systems traffic flow prediction deep belief network deep learning vehicle counting

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Analysis of Web Application Vulnerabilities using Dynamic Application Security Testing 9

Analysis of Web Application Vulnerabilities using Dynamic Ap...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Singh, Ravinder Kumar Gupta, Mukesh Patil, Dipak Raghunath Maruti Patil, Sarang Suresh Gyan Vihar University Department of Computer Engineering Jaipur India Suresh Gyan Vihar University Department of Electrical Engineering Jaipur India Avcoe Department of Computer Enginerring Sangamner India Skn Sinhgad Institute of Technology & Science Department of Computer Engineering Lonavala India

ISBN: (纸本)9798350394474

In the era of ubiquitous digital connectivity, securing software applications against cyber threats is paramount. Dynamic Application Security Testing (DAST) has emerged as a crucial methodology in the realm of cybersecurity, providing a dynamic, real-time approach to identify and mitigate vulnerabilities within software applications. This research paper delves into the significance and application of DAST in fortifying cybersecurity measures. DAST represents a departure from traditional static testing methods, which analyze source code or binaries, by assessing applications during runtime. The methodology involves the active, controlled probing of live applications through simulated cyber-attacks, including injection of malicious payloads and emulation of common exploit techniques. By dynamically assessing how applications respond to these simulated threats, DAST offers a real-world evaluation of security posture, facilitating the identification of potential *** research paper analyzed the Dynamic Application Security Testing (DAST) reports for different web applications over the past two years and provide a valuable resource for cybersecurity practitioners, researchers, and organizations striving to adopt effective strategies in safeguarding their digital assets against evolving cyber threats in the current environment. © 2024 IEEE.

关键词： Cybersecurity

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Characterization of Traffic in the Internet of Vehicles Using Microscopic Modelling

Characterization of Traffic in the Internet of Vehicles Usin...

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2024 IEEE International Conference on Advanced Telecommunication and Networking Technologies, ATNT 2024

作者： Obeng, Kobby Asare Ariffin, Sharifah H.S. Muhammad, Nor Aishah Baharudin, Muhammad Ariff Universiti Teknologi Malaysia Faculty of Electrical Engineering Malaysia Koforidua Technical University Department of Computer Science Ghana Universiti Teknologi Malaysia Faculty of Electrical Engineering Johor Malaysia

ISBN: (纸本)9798350353501

The emergence of the Internet of Vehicles (IoV) as a driver for the new age of Intelligent Transportation Systems (ITS) provides an opportunity for a wide range of services and applications driven by the various interactions that exist between a vehicle and its environment. However, the nature of road networks affects real-time vehicular movements with respect to the departure, delay, speed and arrival of vehicles. In this paper, we present a data-driven, traffic theory and microscopic traffic simulation approach that can be used in modelling the underlying vehicular network of the IoV. Using the traffic status from the A4 highway dataset in Luxembourg, we obtain vehicular volumes from the vehicle flow rate, define thresholds for traffic status using data scaling and employ microscopic modeling to create traffic scenarios. The analysis from the results of the traffic scenarios shows unique vehicular patterns due to the nature of the road network providing insights that ultimately affect the design and implementation of IoV-based services and applications. © 2024 IEEE.

关键词： Internet of things

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