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A comprehensive survey on 6G-security: physical connection and service layers

Discover Internet of Things 2025年第1期5卷 1-30页

作者： Saeed, Mamoon M. Saeed, Rashid A. Hasan, Mohammad Kamrul Ali, Elmustafa Sayed Mazha, Tehseen Shahzad, Tariq Khan, Sunawar Hamam, Habib Sana00967 Yemen Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Electronics Engineering College of Electronics Engineering Sudan University of Science and Technology Omdurman Sudan Bangi43600 Malaysia Department of Electrical and Electronic Engineering Faculty of Engineering Red Sea University Port Sudan33312 Sudan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Computer Science and Software Engineering International Islamic University Islamabad44000 Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa BP 1989 Av. Grandes Ecoles Libreville Gabon Bridges for Academic Excellence-Spectrum Tunis Tunisia Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India

After adopting 5G technology, businesses and academia have started working on sixth-generation wireless networking (6G) technologies. Mobile communications options are expected to expand in areas where previous generations could not do so. 6G networks are anticipated to be constructed using various diverse technologies. These encompass diverse cutting-edge advancements, such as distributed ledger systems like blockchain, visible light communications (VLC), post-quantum cryptography, edge computing, molecular communication, THz, and other advances. These advances necessitate a reassessment of previous security strategies from a security perspective. In the future, networks must adhere to stricter criteria for authentication, encryption, access control, connectivity, and detection of harmful activities. Ensuring privacy and dependability necessitates the implementation of supplementary security protocols. The essay explores the primary concerns and challenges related to the security of the 6G network. This paper describes the improvements in security in communications from 1G through 6G. This paper divides security in the sixth generation into three layers: physical, connection, and service. Each layer-by-layer discusses the standard technologies and security issues for each technology proposed in each sixth-generation security layer. All proposed solutions for each of the three layers are discussed in Sixth Generation Security. It also reviews all proposed solutions for each layer, indicating the proposed solution and its limitations. © The Author(s) 2025.

关键词： 5G mobile communication systems

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Cybersecurity and Privacy Attacks Detection in IoT Networks with Improved Data engineering and Machine Learning Methods 9

Cybersecurity and Privacy Attacks Detection in IoT Networks ...

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9th IEEE International Conference on Big Data Computing Service and Applications, BigDataService 2023

作者： Elmazi, Donald Karras, Dimitrios A. Alkholidi, Abdulsalam Capari, Klea Faculty of Engineering Dept. of Electronics and Telecommunications Tirana Albania Dept. S/W Engineering Tirana Albania Dept. General School of Science Athens Greece Tirana Albania Faculty of Engineering Sana'a University Yemen University of Rijeka Faculty of Engineering Dept. of Computer Engineering Croatia Faculty of Engineering Cit Dept. of Software Engineering Tirana Albania

ISBN: (纸本)9798350333794

Presently, the global proliferation of Internet of Things (IoT) devices and applications has accelerated owing to their advantages in enhancing the business and industrial environment, as well as the daily lives of individuals. Nevertheless, IoT devices are vulnerable to malevolent network traffic, which can lead to undesirable outcomes and disrupt the operation of IoT devices. Consequently, it is imperative to devise a screening method for network traffic to identify and categorize malicious activity and reduce its harmful effects. In this regard, the present study proposes a framework and thoroughly compares single and ensemble machine learning models that are using predictive analytics to detect and classify network activity in an IoT network. Specifically, our framework and models distinguish between normal and anomalous network activity. Furthermore, network traffic is classified into eight categories, normal, denial of service (DoS) attack, Scan attack, Data Type Probing, Malicious Operation, Malicious Control, Spying attack and Wrong Setup based attack. The special focus of this paper is in identifying privacy related attacks in comparison with main cybersecurity related ones. Several supervised machine learning models have been herein implemented to characterize their performance in detecting and classifying network activities for IoT Networks based on the ensemble learning framework as well as on the single learning machines framework. Moreover, classifiers based on the statistical learning theory framework are herein involved too, including hybrid Bayesian theory models, decision trees (DT) and the ordinal Learning Modelling approach. The statistical pattern recognition based models as well as all supervised learning machines models have been evaluated on a broad benchmark dataset for IoT attacks. Besides, an improved data engineering feature processing model has been applied to the dataset under consideration to improve the accuracy of all models under co

关键词： Internet of things

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Data Transmission with Priority Service and Limited Capacity Storage in Unstationary Flow and High Load

Data Transmission with Priority Service and Limited Capacity...

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2023 Intelligent Technologies and Electronic Devices in Vehicle and Road Transport Complex, TIRVED 2023

作者： Lygdenov, S.Z. Lygdenova, I.Z. Itmo University Faculty of Software Engineering and Computer Engineering Saint-Petersburg Russia First St. Petersburg University Named after Academician Pavlov Medical Faculty Saint-Petersburg Russia

ISBN: (纸本)9798350317626

Subject of research. The influence of priority service in a multichannel data transmission system with drives of limited capacity and high load with a non-stationary nature of the intensity of packets entering the system and service time in channels. Method. Calculation and analysis of the functional characteristics of a multichannel system is realized using simulation methods and mathematical statistics. Main results. A simulation model of a multichannel system with priority service is proposed to calculate the functional characteristics of a multichannel system with a high load. A number of experiments were carried out on the influence of priority maintenance on the efficiency and reliability of the system. Dependencies between stationary and non-stationary functional quantities have been identified. Practical significance. The presented research results can be used in the design of real multithreaded data transmission systems with a highly heterogeneous load. © 2023 IEEE.

关键词： Data communication systems

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Comparison of Standard and Modified Ant Colony Algorithm with Fuzzy system for Path Planning of Robot in Complex Environment 14

Comparison of Standard and Modified Ant Colony Algorithm wit...

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14th International Conference on Intelligent Systems: Theories and Applications, SITA 2023

作者： Sharan, Sudeep Minh, Anh Tong Ngoc Nauth, Peter Dominguez-Jimenez, Juan Jose Frankfurt University of Applied Sciences Faculty of Computer Science and Engineering Frankfurt am Main Germany University of Cádiz Ucase Software Engineering Group School of Engineering Cádiz Spain

ISBN: (纸本)9798350308211

The requirements of optimizing path planning for the autonomous robot are in high demand in such industrial communities, especially in manufacturing and caring social support. The application of meta-heuristic methods in autonomous problems is considered because of their adaptivity and robustness. Ant Colony Optimization (ACO) is one of the researchers' approaches because of its effectiveness in the ant community. However, some constraints are making this method less productive. Therefore, this paper is to generate a modified ACO combined with Fuzzy logic (ACOFL) to minimize its drawbacks and maximize the robustness of path planning. This study presents the role of ACO algorithms in Path Planning, especially in the complex aspect, through the theoretical background of ACO and the other combination with other algorithms. The improved mathematics model evaluation demonstrates the upgrade steps in the path-finding process. Simulation results shows that the modified ACO algorithm is effective for complex environment compare to standard ACO algorithm. Moreover, the comparison results are presented in this paper between the standard ACO and modified ACO algorithm. © 2023 IEEE.

关键词： Motion planning

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Fault Detection and Mitigation in Vehicle Platooning

Fault Detection and Mitigation in Vehicle Platooning

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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Ahmed, Najeebuddin Naser, Hassan Ameli, Amir Lakehead University Faculty of Engineering Thunder Bay Canada Lakehead University Department of Software Engineering Thunder Bay Canada Lakehead University Department of Electrical and Computer Engineering Thunder Bay Canada

ISBN: (纸本)9798350304602

This paper addresses the challenges posed by faults in the complex systems of autonomous vehicles within vehicle platoons. It presents a state-space model tailored for vehicle platoons, incorporating an Unknown Input Observer (UIO) to estimate internal states for each vehicle. By monitoring discrepancies between measured and estimated states, the framework effectively detects faults affecting a vehicle's position, velocity, and acceleration, often stemming from malfunctions in its control and navigation components. The paper also introduces fault detection and identification UIOs to pinpoint faulty parameters and estimate associated fault inputs. To validate its effectiveness, the proposed method undergoes MATLAB simulations across diverse scenarios, confirming its capability to mitigate faults within the vehicle platoon. © 2023 IEEE.

关键词： Autonomous vehicles

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Submarine Hunter: Efficient and Secure Multi-Type Unmanned Vehicles

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computers, Materials & Continua 2023年第7期76卷 573-589页

作者： Halah Hasan Mahmoud Marwan Kadhim Mohammed Al-Shammari Gehad Abdullah Amran Elsayed Tag eldin Ala R.Alareqi Nivin A.Ghamry Ehaa A.Lnajjar Esmail Almosharea Computer Center University of BaghdadBaghdad6751Iraq Department of Management Science and Engineering Dalian University of TechnologyLiaoningDalian116024China Department of Information Technology Faculty of Computer Sciences and Information Technology AL Razi UniversitySana’aYemen Faculty of Engineering and Technology Future University in EgyptNew Cairo11835Egypt Faculty of Engineering Mechanics Dalian University of TechnologyLiaoningDalian116024China Faculty of Computers and Artificial Intelligence Cairo UniversityGizaEgypt Department of Business Administration Dalian University of TechnologyLiaoningDalian116024China College of Software Engineering Dalian University of TechnologyChina

Utilizing artificial intelligence(AI)to protect smart coastal cities has become a novel vision for scientific and industrial *** of these AI technologies is using efficient and secure multi-environment Unmanned Vehicles(UVs)for anti-submarine *** study’s contribution is the early detection of a submarine assault employing hybrid environment UVs that are controlled using swarm optimization and secure the information in between UVs using a decentralized cybersecurity *** Dragonfly Algorithm is used for the orientation and clustering of the UVs in the optimization approach,and the Re-fragmentation strategy is used in the Network layer of the TCP/IP protocol as a cybersecurity *** research’s noteworthy findings demonstrate UVs’logistical capability to promptly detect the target and address the problem while securely keeping the drone’s geographical *** results suggest that detecting the submarine early increases the likelihood of averting a *** dragonfly strategy of sensing the position of the submersible and aggregating around it demonstrates the reliability of swarm intelligence in increasing access *** communication between Unmanned Aerial Vehicles(UAVs)improves the level of secrecy necessary for the *** swarm navigation is based on a peer-to-peer system,which allows each UAV to access information from its ***,in turn,helps the UAVs to determine the best route to take and to avoid collisions with other *** dragonfly strategy also increases the speed of the mission by minimizing the time spent finding the target.

关键词： Unmanned vehicles unmanned aerial vehicles unmanned underwater vehicles high altitude unmanned aerial vehicles anti-submarine warfare re-fragmentation dragonfly algorithm

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Robust In-Vehicle Diagnostic-Based Intrusion Detection System

Robust In-Vehicle Diagnostic-Based Intrusion Detection Syste...

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Shafee, Ahmed Awaad, Tasneem A. Moro, Ahmed Adams State University Department of Engineering and Computer Science AlamosaCO United States Ain Shams University Faculty of Engineering Department of Computer and Systems Engineering Cairo Egypt Siemens Eda Cairo Egypt Siemens Digital Industries Software Integrated Electrical Systems Segment Cairo Egypt

ISBN: (纸本)9798350364910

Cybersecurity has become a significant concern for automotive manufacturers as modern cars increasingly incorporate electronic components. Electronic Control Units (ECUs) have evolved to become the central control units for critical car functions such as engines and brakes, experiencing rapid technological advancements. However, this swift progression in ECU technology has also made them prime targets for cyber attacks. This vulnerability has spurred researchers to focus on securing ECUs. Numerous studies have proposed intrusion detection systems (IDS) to protect against attacks on ECUs in vehicles. Yet, these IDSs are not impenetrable;attackers can exploit them by launching evasion attacks, which can trigger numerous false positive alarms. Such false alarms can be disruptive and potentially hazardous for drivers. Additionally, attackers can evade IDSs from detecting malicious data that can cause harm to the vehicle. Accordingly, in this paper, we propose a novel training framework to train a robust in-vehicle IDS that can encounter evasion attacks. Our methodology is based on implementing two rounds of mimic learning technique for training Random Forest (RF) based IDS. RF has been chosen to incorporate the randomness of the RF architecture to enhance the robustness of the model. Additionally, in each round of the two rounds of the mimic learning technique, an RF model with a different architecture is chosen to improve the resilience of the model against evasion attacks without affecting its accuracy. Our Experimental results have shown the effectiveness of our framework against evasion attacks. © 2024 IEEE.

关键词： Robustness (control systems)

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An Explainable Predictive Model for Diabetes Detection Using Shapley Additive Explanations Approach

An Explainable Predictive Model for Diabetes Detection Using...

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International Conference on Advanced engineering, Technology and Applications, ICAETA 2024

作者： Khan, Andleeb Yadav, Saundarya Nand, Parma Khanday, Akib Mohi Ud Din Bhushan, Bharat Jamil, Akhtar Hameedkhan, Alaa Ali Department of Computer Science and Engineering Sharda University Greater Noida India Department of Computer Science and Software Engineering United Arab Emirates University Al Ain United Arab Emirates Department of Computer Science National University of Computer and Emerging Sciences Islamabad44000 Pakistan Department of Computer Engineering Faculty of Engineeringand Natural Sciences Istinye University Istanbul34396 Turkey

ISBN: (纸本)9783031709234

The chronic and incurable disease of diabetes that is not only marked with increasing prevalence but is also linked to other life-threatening diseases like cardiovascular diseases, tissue injuries and retinopathy. With this the early detection and diagnosis of the disease becomes essential. This paper, proposes a novel explainable predictive model for diabetes detection using Shapley additive explanations approach which will not just predict the disease but also highlight the most contributing features contributing to our result. Through the utilization of PIDD dataset, diabetes has been predicted by using three machine learning classifiers i.e. Random Forest, XGBoost and Support Vector Machine. The proposed explainable model outperforms the existing studies by giving an accuracy of 82%. The results were then interpreted using Shapley Additive Explanations (SHAP) which further explained that the most contributing features throughout the study in each model were ‘Glucose’, ‘BMI’ and ‘Age’ among others. This explanation is meant to aid the medical community in their diagnosis and effective recommendations to the patients. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Enhancing Diversity in Multi-Objective Feature Selection 13

Enhancing Diversity in Multi-Objective Feature Selection

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Miyandoab, Sevil Zanjani Rahnamayan, Shahryar Bidgoli, Azam Asilian Ebrahimi, Sevda Makrehchi, Masoud Lab Department of Electrical Computer and Software Engineering Ontario Tech University OshawaON Canada Brock University Department of Engineering St. CatharinesON Canada Wilfrid Laurier University Faculty of Science WaterlooON Canada

ISBN: (纸本)9798350308365

Feature selection plays a pivotal role in the data preprocessing and model-building pipeline, significantly en-hancing model performance, interpretability, and resource efficiency across diverse domains. In population-based optimization methods, the generation of diverse individuals holds utmost importance for adequately exploring the problem landscape, particularly in highly multi-modal multi-objective optimization problems. Our study reveals that, in line with findings from sev-eral prior research papers, commonly employed crossover and mutation operations lack the capability to generate high-quality diverse individuals and tend to become confined to limited areas around various local optima. This paper introduces an augmen-tation to the diversity of the population in the well-established multi-objective scheme of the genetic algorithm, NSGA-II. This enhancement is achieved through two key components: the genuine initialization method and the substitution of the worst individuals with new randomly generated individuals as a re-initialization approach in each generation. The proposed multi-objective feature selection method undergoes testing on twelve real-world classification problems, with the number of features ranging from 2,400 to nearly 50,000. The results demonstrate that replacing the last front of the population with an equivalent number of new random individuals generated using the genuine initialization method and featuring a limited number of features substantially improves the population's quality and, consequently, enhances the performance of the multi-objective algorithm. © 2024 IEEE.

关键词： Genetic algorithms

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Image-based Criticality-Aware Fire Detection

Image-based Criticality-Aware Fire Detection

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2024 International Conference on Frontiers of Information Technology, FIT 2024

作者： Arshad, Alina Shamsi, Jawwad Ahmed Khan, Muhammad Burhan Bawany, Narmeen Zakaria Department of Software Engineering National University of Computer and Emerging Sciences Karachi Pakistan Department of Computer Science National University of Computer and Emerging Sciences Karachi Pakistan Department of Electrical Engineering National University of Computer and Emerging Sciences Karachi Pakistan Faculty of Science Jinnah University for Women Karachi Pakistan

ISBN: (纸本)9798331510503

Context-aware fire detection is a significant task in the era of new urban monitoring. Severe damage might result from fire events. To minimize the occurrence of these events, timely detection of fire accidents is necessary. Various machine-learning and deep learning methods are used for fire detection. Most of them do not focus on the extent of the fire or the damage it causes. All these methods need the detection of the criticality of the fire event. This work proposes an identification system based on Large Vision Models (LVMs) for fire detection. We proposed an intelligent criticality-aware fire detection system that can detect fire and its varying scales from small to large and generate alarming alerts accordingly. The model integrates advanced computer vision techniques with LVMs to explain the hidden context in a textual format. The system is tested on real-time data collected from surveillance cameras and achieves an accuracy of 86.67% in correctly identifying the context-based criticality of the fire events depicted in the input data. This proposed framework can revolutionize surveillance applications by enhancing security and responding to crucial fire incidents in real-time. The system provides an efficient method for allocating resources and facilitates rapid response to incidents to prioritize activities intelligently. Using this approach, we identified and prioritized fire incidents in the urban environment. © 2024 IEEE.

关键词： Fire detectors

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