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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Saeed, Ifrah Cullen, Andrew C. Zaidi, Zainab R. Erfani, Sarah Alpcan, Tansu The University of Melbourne Department of Electrical and Electronic Engineering Australia The University of Melbourne School of Computing and Information Systems Australia

ISBN: (纸本)9798350316339

Device-to-Device (D2D) communication is critical in many public safety scenarios where access to network infrastructure is not guaranteed. In such out-of-coverage situations, random resource allocation is a straightforward way for direct communication by each D2D pair. However, such a resource allocation plan often results in severe interference and low throughput, which can degrade crucial communications performance. In contrast, the problem of centralised joint resource block selection and power control optimisation to maximise the total data rate of all D2D pairs, known as the sum-rate, is non-convex and NP-hard but can effectively reduce interference. To address the inability to solve this non-convex centralised problem setting due to limited information in resource-constrained out-of-coverage scenarios, we propose two distributed reinforcement learning schemes. Our methods allow D2D pairs to autonomously make decisions on their joint resource block and power allocation to minimise their mutual interference and maximise their sum-rate, while maintaining the quality of service constraints. To evaluate these methods in out-of-coverage scenarios, we conduct extensive performance evaluations using the ns-3 network simulator designed for public safety LTE-D2D. We demonstrate that our algorithms, in the absence of any centralised coordination and neighbouring information, autonomously reach time-averaged sum-rates that are within 98.2 % and 98.6 % of the rates achieved by the centralised optimisation solution. © 2024 IEEE.

关键词： Reinforcement learning

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Decision making framework for heterogeneous QoS information: an application to cloud service selection

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Journal of Ambient Intelligence and Humanized computing 2023年第3期14卷 2915-2934页

作者： Tiwari, Rohit Kumar Kumar, Rakesh Baranwal, Gaurav Buyya, Rajkumar Department of Computer Science and Engineering Madan Mohan Malaviya University of Technology Gorakhpur273010 India Department of Computer Science BHU Varanasi India Cloud Computing and Distributed Systems Laboratory School of Computing and Information Systems University of Melbourne Melbourne Australia

In recent times, appropriate decision-making in challenging and critical situations has been very well supported by multicriteria decision-making (MCDM) methods. The technique for order of preference by similarity to ideal solution (TOPSIS) is the most widely used MCDM method for solving decision problems. However, it restricts decision-makers to use only one type of Quality of Service (QoS) information, and it suffers from the rank reversal problem. Restriction to only one type of QoS makes the decision problems more challenging, as it restricts the decision-makers freedom. Further, the rank reversal problem makes the decision result unreliable. To address these issues of TOPSIS, we have proposed a reliable rank reversal robust modular TOPSIS (RMo-TOPSIS). RMo-TOPSIS allows crisp, interval, fuzzy, intuitionistic and neutrosophic fuzzy QoS metrics. It does not suffer from the rank reversal problem. Cloud computing provides computing services on-demand basis without involving maintenance by its users. The availability of many cloud service providers and their services makes cloud service selection a challenging problem. To validate RMo-TOPSIS, we select the cloud service selection consisting of different types of QoS metrics as an application. Experiments on cloud service selection show consistency and accuracy in results obtained by RMo-TOPSIS and its robustness against rank reversal. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Decision making

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A Literature Review of Maturity Models for Cyber-Physical Production systems 18

A Literature Review of Maturity Models for Cyber-Physical Pr...

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18th IFAC Symposium on Information Control Problems in Manufacturing, INCOM 2024

作者： Abadía, Sofía Avila, Oscar Goepp, Virginie Department of Systems and Computing Engineering School of Engineering Universidad de Los Andes Bogota Colombia ICube INSA Strasbourg Strasbourg France

The transition towards Industry 4.0 is taking place nowadays, with fundamental technologies such as CPSs (Cyber-Physical System) and Digital Twins enabling it. For this, a variety of maturity models have been presented in the context of Industry 4.0 with the aim of helping organization transition from one state to the next one. Nevertheless, the number of academic articles and white papers that focus on CPS/CPPS (Cyber-Physical Production System) is quite low, making difficult the correct transition of the system and even more so, generating a lack of support for the transition from legacy systems to CPPS based manufacturing systems. This paper aims at providing a review of academic articles and white papers that propose maturity models in the fields of Industry 4.0, manufacturing systems, CPS/CPPS and Digital Twins, analyzing them together to obtain a whole picture of how they work and determining the main gaps that need to be closed in order to correctly support the transition of legacy systems to CPPS based manufacturing systems. This paper proposes that a new maturity model needs to be developed for this purpose. Copyright © 2024 The Authors.

关键词： Smart manufacturing

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Cooperative Jamming for Physical Layer Security Enhancement Using Deep Reinforcement Learning

Cooperative Jamming for Physical Layer Security Enhancement ...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Hoseini, Sayed Amir Bouhafs, Faycal Aboutorab, Neda Sadeghi, Parastoo Hartog, Frank Den School of Systems and Computing Australia School of Engineering and Technology University of New South Wales Canberra Australia

ISBN: (纸本)9798350370218

Wireless data communications are always facing the risk of eavesdropping and interception. Conventional protection solutions which are based on encryption may not always be practical as is the case for wireless IoT networks or may soon become ineffective against quantum computers. In this regard, Physical Layer Security (PLS) presents a promising approach to secure wireless communications through the exploitation of the physical properties of the wireless channel. Cooperative Friendly Jamming (CFJ) is among the PLS techniques that have received attention in recent years. However, finding an optimal transmit power allocation that results in the highest secrecy is a complex problem that becomes more difficult to address as the size of the wireless network increases. In this paper, we propose an optimization approach to achieve CFJ in large Wi-Fi networks by using a Reinforcement Learning Algorithm. Obtained results show that our optimization approach offers better secrecy results and becomes more effective as the network size and the density of Wi-Fi access points increase. © 2023 IEEE.

关键词： Artificial Noise Friendly Jamming Machine Learning Physical-Layer Security Programmable Networks Reinforcement Learning SDN Secrecy

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Generating Adversarial Patterns in Facial Recognition with Visual Camouflage

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Journal of Shanghai Jiaotong University (Science) 2024年 1-12页

作者： Bao, Qirui Mei, Haiyang Wei, Huilin Lü, Zheng Wang, Yuxin Yang, Xin Key Laboratory of Social Computing and Cognitive Intelligence of Ministry of Education School of Control Science and EngineeringSchool of Computer Science and Technology Dalian University of Technology Liaoning Dalian116024 China Systems Engineering Institute Academy of Military Sciences Beijing100141 China

Deep neural networks, especially face recognition models, have been shown to be vulnerable to adversarial examples. However, existing attack methods for face recognition systems either cannot attack black-box models, are not universal, have cumbersome deployment processes, or lack camouflage and are easily detected by the human eye. In this paper, we propose an adversarial pattern generation method for face recognition and achieve universal black-box attacks by pasting the pattern on the frame of goggles. To achieve visual camouflage, we use a generative adversarial network (GAN). The scale of the generative network of GAN is increased to balance the performance conflict between concealment and adversarial behavior, the perceptual loss function based on VGG19 is used to constrain the color style and enhance GAN’s learning ability, and the fine-grained meta-learning adversarial attack strategy is used to carry out black-box attacks. Sufficient visualization results demonstrate that compared with existing methods, the proposed method can generate samples with camouflage and adversarial characteristics. Meanwhile, extensive quantitative experiments show that the generated samples have a high attack success rate against black-box models. © 2023, Shanghai Jiao Tong University.

关键词： Face recognition

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Secure location-aware geocast routing in Internet of Vehicles

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Multimedia Tools and Applications 2025年第18期84卷 19669-19692页

作者： Kumar, Sunil Dohare, Upasana Kumar, Sushil Department of Computer Science and Information Technology Central University of Haryana Mahendragarh India School of Computing Science & Engineering Galgotias University Greater Noida India School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi India

Efficient message dissemination in Vehicular Ad Hoc Networks (VANETs) relies on robust connectivity between neighboring vehicular nodes, yet it is often compromised by malicious intruders. While recent literature proposes various approaches to enhance geocasting performance, security considerations for connectivity are often overlooked. To address this gap, we propose the Secure Location-Aware Cache Agent based Geocast Routing (SL-CAGR) protocol, emphasizing an Intrusion Prevention Technique (IPT) for message integrity preservation and a Connectivity Reliant Packet Forwarding (CRPF) algorithm for improved VANET connectivity. Our protocol introduces a network model for cache agent-based geocast routing on the Internet of Vehicle (IoV) environment, implements IPT using Diffie-Hellman digital signatures, and presents a secure geocast routing strategy focusing on re-caching and cache agent selection. Empirical simulations demonstrate SL-CAGR's superiority in packet delivery ratio, packet loss, duplication, hop count, end-to-end delay, and network load compared to these protocols. Simulation results demonstrate that SL-CAGR outperforms state-of-the-art techniques like CAG, CTRC, and D-Flooding. SL-CAGR stands out by merging intrusion prevention, secure geocast routing, and cache agent selection, ensuring robust security and efficiency in VANETs. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Vehicular ad hoc networks

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Cybersecurity framework for SMEs in Peru based on ISO/IEC 27001 and CSF NIST controls 18

Cybersecurity framework for SMEs in Peru based on ISO/IEC 27...

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18th Iberian Conference on Information systems and Technologies, CISTI 2023

作者： Angelo Edu, Munoz Luyo Alexis, Garibay Palomino Lenis, Wong Portillo Universidad Peruana de Ciencias Aplicadas School of Systems Engineering and Computing Lima Peru

ISBN: (纸本)9789893347928

Due to the global pandemic that was experienced in 2020, the Small and Medium Enterprises (SMEs) sector in Peru chose to store all their information in cloud services. However, a 2021 Kaspersky study indicates that SMBs have few resources to implement security solutions to protect their information. For this reason, this article proposes a cybersecurity framework composed of controls from ISO/IEC 27001 and the Cybersecurity Framework (CSF) of the National Institute of Standards and Technology (NIST) to mitigate cyber-threats against SMEs in Peru. The framework consists of 7 steps having as reference the Deming cycle (PDCA). For the implementation of the composite framework, we worked with 12 domains and 40 controls for a Peruvian SME in the technology sector. The results showed an increase in cybersecurity of 40 %, after applying the 40 controls, improving its level of maturity from the 'insufficient' state to a 'mature' state, according to the assessment given. © 2023 ITMA.

关键词： Cybersecurity

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Generation of Routes Using Genetic Algorithms to Optimize the Domestic Solid Waste Collection 8th

Generation of Routes Using Genetic Algorithms to Optimize th...

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8th International Conference on Intelligent Technologies, ICIT 2023

作者： Vega-Huerta, Hugo Marcos, Jordy Mayhuay Acosta, Marx Campos Monrroy, Xavier Villanueva Maquen-Niño, Gisella Luisa Elena Puelles-Bulnes, María Gil-Calvo, Rubén School of Systems Engineering Universidad Nacional Mayor de San Marcos Lima Peru School of Computing and Informatics Engineering Universidad Nacional Pedro Ruiz Gallo Lambayeque Peru

ISBN: (纸本)9789819738588

The district of San Juan de Lurigancho (SJL) in Lima—Peru, is one of the districts crowed in the capital and has one of the highest amounts solid domestic wastes. To fix this problem, a model was developed to map the distance and calculate the best route to collect the domes-tic solid waste in the whole San Carlos neighborhood in SJL district. Our model was developed using genetic algorithms in JavaScript with Node JS to calculate the shortest path. For the data visualization was used the React JS for the frontend and Google Maps API for the display of an interactive map. The results obtained allowed were able to find an optimal route that joins two opposite points within the map of the San Carlos neighborhood, which is made up of 5 nodes and measures 514 m away. The model developed could generate populations of 10 to 100 individuals with the map nodes through selection, crossing and mutation processes, obtaining an optimal route that facilitates the domestic waste collection processes with the shortest distance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Collection Genetic algorithm Minimum route Pathfinding Solid waste

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A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges (Extended Abstract) 39

A Survey on Modern Deep Neural Network for Traffic Predictio...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Tedjopurnomo, David Alexander Bao, Zhifeng Zheng, Baihua Choudhury, Farhana Murtaza Qin, A.K. Rmit University School of Computing Technologies Australia Singapore Management University School of Computing and Information Systems Singapore The University of Melbourne School of Computing and Information Systems Australia Swinburne University of Technology Department of Computer Science and Software Engineering Australia

ISBN: (纸本)9798350322279

In this modern era, traffic congestion has become a major source of negative economic and environmental impact for urban areas worldwide. One of the most efficient ways to mitigate this issue is through traffic prediction. This research field has evolved greatly ever since its inception in the late 70s. Recently, deep neural network models have gained popularity thanks to its predictive power, but despite this, literature surveys of such methods are rare;making it difficult to ascertain the progress of this research field. In this work, we address this issue by presenting an up-to-date survey of deep neural network for traffic prediction. We provide detailed explanations of popular deep neural network architectures used in the traffic flow prediction literatures, categorize and describe the literatures themselves, present an overview of the commonalities and differences among different works, and finally provide a discussion regarding the challenges and future directions for this field. © 2023 IEEE.

关键词： Deep neural networks

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Dual Distillation: A Lightweight and Effective Intrusion Detection Model for IoT Security 15

Dual Distillation: A Lightweight and Effective Intrusion Det...

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15th Global Information Infrastructure and Networking Symposium, GIIS 2025

作者： Pei, Chonghao Mhamdi, Lotfi University of Leeds School of Electronic and Electrical Engineering United Kingdom Abdullah Al Salem University College of Computing & Systems Kuwait

ISBN: (纸本)9798331520113

With the exponential growth of IoT devices, ensuring efficient and accurate network anomaly detection in resource-constrained environments has become a pressing challenge. A lightweight dual distillation approach is proposed, combining data distillation and model distillation. Data distillation is achieved through KMeans clustering and refinement, while model distillation leverages a random forest (RF) teacher model to guide a simplified multi-layer perceptron (MLP) student model. The approach is validated on CICIDS2017 and TON-IoT datasets, where dataset sizes are reduced to 20% and 40%, respectively. Experimental results demonstrate that the proposed method enhances MLP accuracy on CICIDS2017 from 93.17% to 99.66% and on TON-IoT from 89.00% to 93.14%. Moreover, the method significantly reduces model parameters compared to the RF teacher model while maintaining competitive performance, making it highly suitable for resource-constrained IoT environments. © 2025 IEEE.

关键词： Network intrusion

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