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Synergistic Solution: IDS and Scheduler Harmony for Secure 5G Radio Resources

Synergistic Solution: IDS and Scheduler Harmony for Secure 5...

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international Wireless Communications and Mobile Computing conference, IWCMC

作者： Saloua Hendaoui Fatma Hendaoui Department of Computer Science College of Computer and Information Sciences Jouf University Sakaka KSA Department of Computer Science Applied College University of Ha’il HA’il KSA

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

The diverse range of applications in 5G networks introduces novel security challenges, particularly due to the high volume of transmitted packets. The proliferation of devices also increases their vulnerability to various security threats. Among these, distributed Denial of Service (DDoS) attacks stand out as the most common in 5 G environments. Such attacks create a single point of failure by overwhelming the central base station with malicious traffic, leading to degradation in the quality of service for critical applications, especially those reliant on realtime responsiveness. The inundation of suspicious traffic hampers legitimate devices from fulfilling their tasks effectively. While traditional security measures, including Intrusion Detection systems (IDS), are deployed to mitigate these threats, they often detect suspicious traffic only after it has reached the scheduler and resource allocation process. To address this limitation, we propose in this paper a novel architecture which integrates a filtering block and a Radio Resource Allocation Block (RRAB) to safeguard 5G radio resources. By employing unsupervised learning, the proposed architecture exhibits promising results in distinguishing between legitimate and illegitimate devices. Enhanced filtering mechanisms and prioritization tools contribute to a comprehensive mitigation strategy, bolstering the security and resilience of 5 G networks against evolving cyber *** implementation of clustering models, such as K-means and DBSCAN, effectively identifies patterns of unauthorized usage, with the DBSCAN model achieving high homogeneity (0.3161) and a V-measure of 0.2245. This comprehensive approach improves the quality of service for delay-sensitive applications and bolsters the security and resilience of 5 G networks against evolving cyber threats.

关键词： Filtering 5G mobile communication Prevention and mitigation computer architecture Quality of service Denial-of-service attack security

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DDoS Attacks Detection in IoT networks using Naive Bayes and Random Forest

DDoS Attacks Detection in IoT Networks using Naive Bayes and...

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Applied Artificial Intelligence and Computing (ICAAIC), international conference on

作者： Animesh Srivastava Shweta Tiwari Bhupender Singh Rawat Shiv Ashish Dhondiyal Department of Computer Science & Engineering Graphic Era Hill University Dehradun Department of Computer Science & Engineering Department / University Institute of Engineering Chandigarh University Gharuan Mohali Punjab College of Smart Computing COER University Roorkee Department of Computer Science & Engineering Graphic Era (Deemed to be University) Dehradun

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

The proliferation of Internet of Things (IoT) devices has resulted in numerous benefits, including streamlined tasks and improved connectivity but has also raised concerns regarding security vulnerabilities. distributed Denial of Service (DDoS) attacks targeting IoT systems pose a significant threat, potentially disrupting operational activities. This study conducts a comparative analysis of existing methodologies to detect DDoS attacks in IoT environments, focusing on the efficacy of machine learning algorithms. Specifically, this study evaluates the performance of Random Forest and Naive Bayes classifiers in detecting DDoS attacks using an IoT dataset. The evaluation involves training and testing these classifiers and assessing their accuracy as a key performance metric. Empirical findings demonstrate enhanced precision in identifying DDoS attacks in IoT environment, indicating the effectiveness of the proposed approach. By leveraging machine learning algorithms, particularly Random Forest and Naive Bayes, traditional techniques for detecting and mitigating DDoS attacks in IoT environments are accelerated, thus enhancing the security of IoT systems.

关键词： Training Machine learning algorithms Accuracy Telecommunication traffic Denial-of-service attack Bayes methods Internet of Things

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Multifactor Authentication Using Blockchain in 6G

Multifactor Authentication Using Blockchain in 6G

引用

international conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Aishwarya R Anusha Khot Sumana Sreenivas Nagamani K Electronics and Telecommunication Engineering R V College of Engineering Bangalore Karnataka

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

With the rise of 6G technology, cyberattacks have become a significant concern, necessitating robust data security measures. Multi-factor authentication (MFA) systems, integrating advanced biometric methods, offer a promising solution to enhance security and protect sensitive information. This work explores the integration of iris and voice recognition with blockchain technology to enhance security measures for 6G networks. The increasing complexity of cyberattacks, privacy concerns from massive data collection, and the vulnerabilities associated with edge computing and quantum computing highlight the necessity for robust security solutions. Gaps in current 6G network security research, including limited studies on integrating multiple biometric modalities within Multi-Factor Authentication (MFA) systems and the lack of practical integration of AI/ML, distributed Ledger Technology, and quantum computing have been identified. Addressing these gaps can lead to scalable and effective security solutions for 6G networks. The combination of unique iris and voice patterns with the tamper-proof and decentralized nature of blockchain ensures a robust authentication process. This framework was implemented and its efficacy and feasibility were demonstrated through simulated authentication procedures. The results show a significant improvement in security metrics over conventional single-factor authentication methods, with high accuracy rates for both voice and iris recognition. These outcomes, along with the blockchain's tamper-proof features, provide a strong defense against unauthorized access.

关键词： 6G mobile communication Measurement Quantum computing Multi-factor authentication Speech recognition Network security Blockchains computer crime Information technology Iris recognition

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Federated Patient Similarity Network for Data-Driven Diagnosis of COVID-19 Patients 18

Federated Patient Similarity Network for Data-Driven Diagnos...

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18th ACS/IEEE international conference on computer systems and Applications (AICCSA)

作者： El Kassabi, Hadeel T. Serhani, Mohamed Adel Navaz, Alramzana Nujum Ouhbi, Sofia UAEU Dept Comp Sci & Software Engn Coll Informat Technol Al Ain U Arab Emirates UAEU Dept Informat Syst & Secur Coll Informat Technol Al Ain U Arab Emirates

ISBN: (纸本)9781665409698

Sensitive patient data is generated from a variety of sources and then transferred to a cloud for processing. Therefore, it is exposed to security and privacy and may lead to an increase in communication costs. Edge computing will ease computing pressure through distributed computational capabilities while improving security and privacy. In this paper, we propose a Federated PSN (FPSN) model where the model is moved directly to the edge to minimize computation and communication costs. PSN has been applied as a successful approach in categorizing and diagnosing patients based on similarities against some clinical and non-clinical features. Our proposed model distributes processing at each edge node, then fuses the constructed PSN matrices at the cloud premises, which significantly reduce the model's training and inference time and ensures quick model updates with the local client/nodes. In this paper, we propose: (i) an algorithm to evaluate patient's data similarity at the edge;and (ii) an algorithm to implement the federated similarity network fusion at the Cloud. We conducted a set of experiments to evaluate our FPSN model against other machine learning algorithms using a COVID-19 dataset. The results obtained prove that the FPSN model accuracy is higher than the distributed PSNs at various edges and higher than the accuracies of other classification models.

关键词： Patient Similarity Network Precision Medicine Neural networks Deep Learning Edge Computing COVID-19 Federated PSN Federated Learning

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Investigation on the Integrated Cloud and BlockChain (ICBC)Technologies to Secure Healthcare Data Management systems

Investigation on the Integrated Cloud and BlockChain (ICBC)T...

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international conference on Developments in esystems Engineering (DESE)

作者： Aymen Mudheher Badr Lamia Chaari Fourati Samiha Ayed Digital Research Center of Sfax (CRNS) Laboratory of Signals Systems Artificial Intelligence and Networks (SM@RTS) Sfax University IRAQ Digital Research Center of Sfax (CRNS) Laboratory of Signals Systems Artificial Intelligence and Networks (SM@RTS) Sfax University TUNISIA Institute Charles Delaunay-ERA University of Technology of Troyes France

Blockchain is emerging as one of the most promising and resourceful security technologies for cloud infrastructures. In a distributed database system, blockchain is used to store, read, and validate transactions. It can improve security, trustworthiness, and privacy by using an unchallengeable, shared distributed ledger on cloud nodes. Cloud-based healthcare systems (CHS) are vulnerable to various threats and attacks such as identity theft, medical fraud, insurance fraud, and alteration of critical patient data. Secure retrieval, access, and storage of data on CHS are necessary to protect critical medical data. Accordingly, the integrated cloud and BlockChain (ICBC) architecture emerge as a potential solution for shaping the next era of a healthcare system while providing efficient, secure, and effective patient care. In this context, this paper presents an in-depth exploration of advanced approaches to securing cloud-based healthcare data management systems using blockchain technologies. It provides a taxonomy and highlights the benefits and limitations of the approaches examined.

关键词： Cloud computing Technological innovation Privacy Taxonomy Medical services computer architecture Blockchains

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Traffic Pattern Sharing for Federated Traffic Flow Prediction with Personalization 24

Traffic Pattern Sharing for Federated Traffic Flow Predictio...

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24th IEEE international conference on Data Mining, ICDM 2024

作者： Zhou, Hang Yu, Wentao Wan, Sheng Tong, Yongxin Gu, Tianlong Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China SKLSDE Beihang University China Jinan University Engineering Research Center of Trustworthy AI Ministry of Education China

ISBN: (纸本)9798331506681

Accurate Traffic Flow Prediction (TFP) is crucial for enhancing the efficiency and safety of transportation systems, so it has attracted intensive researches by exploiting spatial-temporal dependencies within road networks. However, existing works only consider the case of centralized data collection with all traffic data observed, which may raise privacy concerns as each region of a city may have its own traffic administration department and the traffic data is not allowed to distribute. Therefore, this paper proposes to use Federated Learning (FL) to address this issue by allowing all clients (i.e., traffic administration departments in all regions in our problem) to collaboratively train TFP models without exchanging raw data, thereby offering a solution in maintaining data privacy. Nevertheless, most existing FL methods aim to learn a global model that performs well universally, so they cannot well handle the non-Independent and Identically distributed (non-IID) traffic data naturally over different regions. To cope with this problem, this paper develops a new FL framework termed 'personalized Federated learning with Traffic Pattern Sharing' (FedTPS) to solve federated TFP problem. Our FedTPS critically exploits the underlying common traffic patterns (e.g., morning and evening rush hours) shared across different city regions and meanwhile maintaining the region-specific data characteristics in a personalized FL manner. Specifically, to extract the common traffic patterns, we decompose the traffic data in each client via using discrete wavelet transform, where the low-frequency components uncover the stable traffic dynamics of different regions and thus can be considered as the common traffic patterns. These common patterns are then shared among different clients through traffic pattern repositories on the server side to aid the global collaborative traffic flow modeling. Moreover, the model components capturing spatial-temporal dependencies in traffic data are r

关键词： Highway administration

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Integrated PON-VLC System for IoT Applications

Integrated PON-VLC System for IoT Applications

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distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE international conference on

作者： Meet Kumari Neha Sharma Rahul Chauhan Kireet Joshi Amit Kumar Dept. of ECE UIE Chandigarh University Gharuan Mohali India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India Computer Science & Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

Healthcare is one of the vital and well-known application fields of internet of things (IoT) technology, which is being extensively accepted worldwide. In modern healthcare systems, the IoT -eHealth is playing a prominent role in healthcare fields by spreading the devices and proficient people interaction. This system can minimise the span of hospital accommodation as well as healthcare costs, upgrade results and precludes re-admission of patients to the hospitals. The performance of these systems, especially transmission distance, security and information rate of medical data, could be affected highly by high power consumption and cost with the increase in number of patients. This motivates towards passive optical network and visible light communication (PON-VLC) system using sensors for IoT -eHealth services. The results show that the proposed system utilizing multi-diagonal code offers faithful 50km fiber and 10m VLC distance at 130Gbps per channel transmission rate for simultaneously 53 patients.

关键词： Performance evaluation Optical fiber sensors Costs Power demand Hospitals Sensor systems Passive optical networks

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Affinity and Class Probability based Fuzzy Support Vector Machine for Path Loss Prediction in 5G Communication System

Affinity and Class Probability based Fuzzy Support Vector Ma...

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distributed systems, computer networks and Cybersecurity (ICDSCNC), international conference on

作者： Zhiru Cong Shuqing Wang Department of Electronics and Automation Inner Mongolia Electronic Information Vocational and Technical College Hohhot China Department of Office/Security Supervision China United Network Communications Co. Ltd Hohhot China

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Wireless propagation channel is a primary factor for optimizing and planning 5G communication system. Predicting path loss in 5G plays a vital role in optimizing performance of network and ensure appropriate communication by determining signals over distance. However, path loss remains a challenge in 5G system because of signal propagation in different environment which results in minimized and unreliable network performance. This research proposes Affinity and Class Probability based Fuzzy Support Vector Machine (ACPFSVM) to predict the path loss in 5G communication system. In the traditional SVM, the ACPF is involved for prediction to handle uncertainties efficiently in data which results in more robust and accurate performance. The Gaussian filter is applied to remove a noise by smoothening the images which minimize high-frequency noise while conserving significant structural features. Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) are applied to extract the features by capturing local texture patterns effectively. The proposed ACPFSVM obtains a less Mean Average Error (MAE) of 0.083 compared to existing methods like Random Forest (RF), Linear Regression (LR), and SVM respectively.

关键词： Support vector machines Radio frequency Wireless communication Uncertainty Accuracy 5G mobile communication Communication systems Noise Feature extraction Robustness

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Securing Smart Homes with Blockchain-Enabled systems

Securing Smart Homes with Blockchain-Enabled Systems

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international conference on ICT in Business Industry & Government (ICTBIG)

作者： Aravendra Kumar Sharma Ratnesh Kumar Dubey Pragya Goswami Rakesh Prasad Sarang CSA Department ITM University Gwalior

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Growing Internet Of Things (IoT) boosts rapid development Of smart home technologies.. Yet it brings new security challenges.. Centralized architectures frequently used in smart home networks are particularly at risk through gateways. .Such networks are exposed to numerous security threats. These include unauthorized access, data tampering And single points of failure. In this study we suggest decentralized blockchain-based system to reinforce security in smart home environments .Through integration of blockchain technology we seek to handle problems like data integrity. Also authentication and availability. Adopting Our proposed architecture plays upon use Of Ethereum blockchain.. It utilizes smart contracts to manage and secure interactions Between devices in the household.. Furthermore employing SHA-256 hashing guarantees privacy to sensitive data exchanged between devices. .It also makes the transmitted data tamper-proof. We perform real-time validation of data over distributed ledgers. This considerably diminishes chance of cyber-attacks .Our strategy is Scalable and Safe for management of Smart homes.

关键词： Data privacy Multi-access edge computing System performance Smart contracts Smart homes computer architecture Logic gates Blockchains security Internet of Things

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Developing a Face Recognition System Using Vision Transformer and Federated Learning

Developing a Face Recognition System Using Vision Transforme...

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international conference on Advanced Technologies for Communications, ATC

作者： Kiet Truong-Tuan Luan Van-Thien Thuat Nguyen-Khanh Dung Tran-Thi Quan Le-Trung Faculty of Computer Networks and Communications University of Information Technology Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Vietnam

ISBN: (数字)9798350353983

ISBN: (纸本)9798350353990

Traditional centralized approaches to training AI systems for face recognition raise significant privacy concerns by consolidating massive datasets in a single location. This method exposes personal data to potential breaches and misuse. This paper proposes a Federated Learning framework utilizing a Vision Transformer model for face recognition with minimal local data. This framework ensures data security by facilitating collaborative training across multiple devices, thereby avoiding the risks associated with centralized data storage. Experimental results validate the effectiveness and reliability of our approach: the Vision Transformer model achieves stable performance when trained on limited local data using Federated Learning. This method supports efficient and secure data distribution, offering a promising solution to the privacy-performance trade-off in AI system development for face recognition while maintaining high performance and security standards.

关键词： Training computer vision Federated learning Face recognition Data security Memory distributed databases Transformers Data models Artificial intelligence

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