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User Mobility Prediction in 5G networks Using Recurrent Neural networks

User Mobility Prediction in 5G Networks Using Recurrent Neur...

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Communication, Computing and Signal Processing (IICCCS), IEEE international conference on

作者： Anubhav Shrestha Vikalp Sharma Layth Hussein M.Aishwarya A N Satyanarayana T. Saimanohar Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India Department of Computer Science & Engineering IES College of Technology Bhopal M.P. India Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Najaf Iraq Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Of Al Diwaniyah Al Diwaniyah Iraq Prince Shri Venkateshwara Padmavathy Engineering College Chennai India ECE Department Sreenidhi Institute of Science and Technology Hyderabad India Department CSE-AI Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

The use of new possibilities introduced by 5G networks also creates new problems and concerns, specifically in the field of user mobility in wireless communication systems. In this paper, the Authors investigate the effectiveness of Recurrent Neural networks (RNNs) in the mobility prediction context in order to exploit the temporal component in the users movement data for improved prediction performance. To drive this process, the input datasets are obtained from the GeoLife GPS Trajectories dataset, then follow the data pre-processing steps, constructing an LSTM-based RNN model, and using Python with TensorFlow and Keras. From the analyses, we detected that the accomplishment of using LSTM and hybrid RNN-CNN is significant and higher than the traditional approaches of EN, RFM, and CL. The test results demonstrate that the LSTM model can reduce the MAE and RMSE values by 19. 49% and 14. 82%, respectively. However, the execution of these models has some issues that include compute time, instantaneous prediction that is needed in many real-world applications, and case. Such problems can be solved with the help of such approaches as edge computing and distributed processing. Besides, it is pivotal to put measures in place to maintain the confidentiality and security of user information. This paper highlights the applicative potential of RNNs in redesigning user mobility prediction in 5G networks, and thus helping toward a better ownership of the network players and assets.

关键词： Recurrent neural networks 5G mobile communication Computational modeling Wireless networks Transforms Predictive models Data models security Long short term memory Edge computing

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E-DDoS: An Evaluation System for DDoS Attack Detection

E-DDoS: An Evaluation System for DDoS Attack Detection

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international conference on Network Protocols

作者： Kaiwen Chi Xiaohui Xie Yannan Hu Dongyang Zhao Yuming Xie Liang Zhang Yong Cui Department of Computer Science and Technology Tsinghua University Beijing China Zhongguancun Laboratory Beijing China AI4NET Lab Huawei Technologies Company Ltd Beijing China

ISBN: (数字)9798350351712

ISBN: (纸本)9798350351729

Research in the area of distributed Denial of Service (DDoS) attack detection is of paramount importance in the field of network security. Many existing studies employ static evaluation methods that fail to account for the reduction in accuracy due to the impact of inference latency on the timeliness of classification results. Furthermore, these studies frequently rely on simulated datasets for experimentation, which often lack the complexity and challenge of real-world attacks. These limitations significantly hinder the applicability of such research in practical scenarios. To overcome these challenges, we propose an evaluation methodology for real-time DDoS attack detection incorporating inference latency considerations. Additionally, we have developed a challenging DDoS dataset named THU-DDoS2024 and conducted experiments across four classification algorithms. This novel evaluation method and the newly generated dataset are integrated into an evaluation framework named E-DDoS. Leveraging E-DDoS, the “Intelligent Classification of High-Speed Network Traffic (ICNT)”, Grand Challenge was initiated. This event aims to motivate both academic and industrial sectors to delve into high-speed traffic classification tasks, thereby enhancing the applicability of research outputs to real-world applications.

关键词： Accuracy Protocols High-speed networks Organizations Network security Denial-of-service attack Real-time systems Classification algorithms computer crime Detection algorithms

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Federated Clustering Defense: Mitigating Data Exposure in Collaborative Learning

Federated Clustering Defense: Mitigating Data Exposure in Co...

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2025 international conference on Emerging Technologies for Intelligent systems, ETIS 2025

作者： Radhakrishnan, Anuja Krishna, Sathya Bama Sathyabhama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331507541

The development of intelligent industrial applications is being driven globally by advancements in the next generation of Internet of Things (IoT) technologies. At the same time, artificial intelligence (AI) technologies like machine learning and deep learning are becoming more widely used. Conventional machine learning models primarily rely on large data sets. However, the vast data sets generated by network-edge devices are expensive, inefficient to acquire and handle, and they pose major privacy hazards. Federated learning (FL) is a novel paradigm for statistical model training on distributed edge networks that allows data to take part in the training of federated models without being localized. Traditional machine learning issues with low data usage, data privacy, and information security brought on by data isolation can be resolved with this method. We propose a novel Federated Clustering with Differential Privacy (FedCDP) algorithm designed to efficiently model user behavior in an unsupervised manner. This algorithm focuses on optimizing computation and storage requirements while minimizing data leakage through robust differential privacy mechanisms. © 2025 IEEE.

关键词： Deep learning

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Advancing Threat Detection in Fog Computing: A Comprehensive Approach to Real-Time Analysis and Model Generation

Advancing Threat Detection in Fog Computing: A Comprehensive...

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international conference on Fog and Mobile Edge Computing (FMEC)

作者： Lucía Cuadrillero Paredes Ángel Luis Perales Gómez Óscar Cánovas Lorenzo Fernández Maimó Félix J. García Clemente Faculty of Computer Science University of Murcia Murcia Spain

ISBN: (数字)9798350366488

ISBN: (纸本)9798350366495

Fog computing, an extension of cloud computing, places computational resources closer to data sources, enhancing network efficiency and security. This paper addresses the challenges posed by cybersecurity threats within fog computing environments, specifically in managing vast volumes of network traffic from IoT devices which are vulnerable to cyberattacks. We propose a novel architecture for real-time anomaly detection that integrates fog and cloud computing layers. This two-tiered system utilizes machine learning and deep learning models for dynamic and adaptive threat detection. By leveraging the computational power of modern GPUs, the system analyzes network traffic at speeds up to 10 Gbps, enabling the detection of anomalies before they can cause significant damage. We introduce a distributed anomaly supervisor that operates at the fog level to provide real-time analysis and immediate response to potential threats, while cloud-level operations facilitate a comprehensive overview and coordination. The effectiveness of this architecture is demonstrated through extensive experimentation, proving its capability to manage high-speed data transfers and improve security measures across fog networks.

关键词： Deep learning Cloud computing Multi-access edge computing Computational modeling Soft sensors Telecommunication traffic computer architecture Real-time systems Threat assessment Internet of Things

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SPIRT: A Fault-Tolerant and Reliable Peer-to-Peer Serverless ML Training Architecture

SPIRT: A Fault-Tolerant and Reliable Peer-to-Peer Serverless...

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IEEE international conference on Software Quality, Reliability and security (QRS)

作者： Amine Barrak Mayssa Jaziri Ranim Trabelsi Fehmi Jaafar Fabio Petrillo Department of Computer Science and Mathematics University of Quebec at Chicoutimi UQAC Saguenay Canada Département de génie logiciel École de Technologie Supérieure ÉTS Montreal QC

The advent of serverless computing has ushered in notable advancements in distributed machine learning, particularly within parameter server-based architectures. Yet, the integration of serverless features within peer-to-peer (P2P) distributed networks remains largely uncharted. In this paper, we introduce SPIRT, a fault-tolerant, reliable, scalable and secure serverless P2P ML training architecture. designed to bridge this existing gap. Capitalizing on the inherent robustness and reliability innate to P2P systems, we emphasized Intra-peer scalability for concurrent gradient to mitigate communication overhead from increased peer interactions. SPIRT, employs RedisAI for in-database operations, achieves an 82% reduction in model update times. This architecture showcases resilience against peer failures and adeptly manages the integration of new peers. Furthermore, SPIRT ensures secure communication between peers, enhancing the reliability of distributed machine learning tasks. Even in the face of Byzantine attacks, the system’s robust aggregation algorithms maintain high levels of accuracy. These findings illuminate the promising potential of serverless architectures in P2P distributed machine learning, offering a significant stride towards the development of more efficient, scalable, and resilient applications.

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K-Means-Based Mobile Adaptive networks for Swarm Robotic systems with Non-Cooperative Behaviors 6

K-Means-Based Mobile Adaptive Networks for Swarm Robotic Sys...

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6th international conference on Computational Intelligence and Applications, ICCIA 2021

作者： You, Jia Li, Zhenhui Yang, Jianhua Zhejiang University College of Information Science and Electronic Engineering Hangzhou China Zhejiang University College of Control Science and Engineering Hangzhou China Zhejiang University College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9781665439336

Mobile adaptive networks enable distributed adaptation and learning over networks while maintaining the cooperative motion, and play an important role in multi-robot control. Non-cooperative behaviors are common in swarm robotic systems and can greatly affect the system performance through information transmission and fusion in mobile adaptive networks. Some adaptations of different aggregation rules have been emerged to improve fault tolerance in mobile adaptive networks. This paper proposes K-means-based mobile adaptive networks which use K-means clustering algorithm to detect the faulty neighbors with non-cooperative behaviors and carry out mobile adaptive networks depending on information of the normal neighbors. Simulations are carried out in four existed mobile adaptive networks with different aggregation rules and our K-means-based mobile adaptive networks with these four aggregation rules. Simulation results prove that our proposed algorithms have better fault tolerance than the original algorithms. © 2021 IEEE.

关键词： Adaptive control systems

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Enhancing Multi-UAV Path Planning Efficiency with security through Integration of Priority-Based Ant Lion Optimization Algorithm

Enhancing Multi-UAV Path Planning Efficiency with Security t...

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Next Generation Data-driven networks (NGDN), international conference on

作者： Yunchong Guan Wei Zhi Sturdy So Su Peng Yihang Zhang Yuanhao Zhang Yang Wang Fahreza Prima Hakim Mumtaz Zain Abdullah School of Computer Science Shenyang Aerospace University Shenyang China School of Cyberspace Security (School of Cryptology) Hainan University Haikou China

ISBN: (数字)9798350388374

ISBN: (纸本)9798350388381

The research endeavors to revolutionize Multi-Robot systems (MRS) through a cutting-edge hybrid strategy, amalgamating Priority-Based Algorithms (PBA) and Ant Lion Optimization (ALO) algorithms. This innovative approach aims to elevate path planning efficacy and coordination within dynamic and intricate environments. Leveraging PBA for collision-free path prioritization and ALO for agile and dynamic decision-making, the methodology promises to significantly enhance MRS's operational efficiency in real-world scenarios. The proposed Hybrid of Asynchronous Centralized and Decentralized (HACD) algorithm introduces a pioneering framework for fostering robot collaboration and resolving conflicts via distributed leader strategies. By employing grid-based decomposition and leader election mechanisms, it ensures seamless navigation in multi-robot setups by segmenting paths and imposing conflict limits. Integrating prioritized planning with trajectory optimization using ALO emulates the efficiency of ant lions' hunting behaviors, yielding adaptive solutions for dynamic operational landscapes. In addition, adding the help of Secret Cryptographic Key as the compartment of SSL for the proposed algorithm. MATLAB simulations validate the algorithm's scalability, efficiency, and effectiveness across diverse robot configurations, underscoring its potential for applications spanning search and rescue operations, military maneuvers, and warehouse logistics, where optimized Multi-Robot Path Planning (MRPP) is paramount.

关键词： Robot kinematics Heuristic algorithms Voting Collaboration Path planning Robustness Multi-robot systems Cryptography Collision avoidance Logistics

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23rd international Symposium on Stabilization, Safety, and security of distributed systems, SSS 2021

23rd International Symposium on Stabilization, Safety, and S...

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23rd international Symposium on Stabilization, Safety, and security of distributed systems, SSS 2021

ISBN: (纸本)9783030910808

The proceedings contain 40 papers. The special focus in this conference is on Stabilization, Safety, and security of distributed systems. The topics include: Round-Oblivious Stabilizing Consensus in Dynamic networks;towards a Robust distributed Framework for Election-Day Voter Check-In;asynchronous Proof-of-Stake;lack of Quorum Sensing Leads to Failure of Consensus in Temnothorax Ant Emigration;location Functions for Self-stabilizing Byzantine Tolerant Swarms;applications and Implications of a General Framework for Self-Stabilizing Overlay networks;network Scaffolding for Efficient Stabilization of the Chord Overlay Network;the Agreement Power of Disagreement;the Max-Line-Formation Problem: And New Insights for Gathering and Chain-Formation;byzantine-Tolerant Reliable Broadcast in the Presence of Silent Churn;message Delivery in the Plane by Robots with Different Speeds;exploring a Dynamic Ring Without Landmark;loosely-Stabilizing Maximal Independent Set Algorithms with Unreliable Communications;on Regenerating Codes and Proactive Secret Sharing: Relationships and Implications;extending Lattice Linearity for Self-stabilizing Algorithms;information Exchange in the Russian Cards Problem;compact distributed Interactive Proofs for the Recognition of Cographs and Distance-Hereditary Graphs;asynchronous Gathering Algorithms for Autonomous Mobile Robots with Lights;synchronization Modulo k in Dynamic networks;partial Gathering of Mobile Agents in Dynamic Rings;building systems of systems with Escher;optimal Protocols for 2-Party Contention Resolution;computer Aided Formal Design of Swarm Robotics Algorithms;Delta-State JSON CRDT: Putting Collaboration on Solid Ground;self-stabilization and Byzantine Tolerance for Maximal Independent Set;coordinating Amoebots via Reconfigurable Circuits;on Optimal Doorway Egress by Autonomous Robots;byz-GentleRain: An Efficient Byzantine-Tolerant Causal Consistency Protocol;mitigating Internal, Stealthy DoS Attacks in Microservice networks;

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ZF-DDOS: An Enhanced Statistical-Based DDoS Detection Approach using Integrated Z-Score and Fast-Entropy Measures

ZF-DDOS: An Enhanced Statistical-Based DDoS Detection Approa...

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Computing and Informatics (ICCI), international conference on

作者： Mahmoud Hassan Khaked Metwally Mohamed A. Elshafey Departemnt of Computer Engineering and Artifitial Intelligence Military Technical Colleague Cairo Egypt

The fast emerging of Internet technologies and 5G connectivity played an imperative role in our daily lives. This allowed to incredible increase in the Internet usage, so, a large amount of information is exchanged between various connected devices daily. However, this development introduced a great cyber security challenge for all individuals and enterprises. Evolution of new and different types of network attacks grows quickly to compromise one or more of the Confidentiality, Integrity, and Availability (CIA) Triad principles that guarantee the security of information systems. distributed Denial of Service (DDoS) attack is one of the most destructive attacks that targets the availability in the CIA Triad. Many research works have been proposed targeting DDoS attacks detection in recent networks. However, the detection accuracy still a challenging task. This paper presents an enhanced statistical-based DDoS detection approach that mainly relies on the integration of Z-Score and Fast-Entropy statistical measurements. Moreover, the proposed approach integrates additional statistical features that contribute significantly on higher DDoS detection accuracy over statistical based benchmarks. In experiments, the proposed approach has been tested on CIC-DDOS-2019 dataset and resulted markable improvements in DDOS detection accuracy of 25% and 10% over Fast-Entropy- and Z-Score statistical-based benchmarks, respectively.

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RPA-Code for Secure Binary Sequence Generation from Graph-Based Scanning 23

RPA-Code for Secure Binary Sequence Generation from Graph-Ba...

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Proceedings of the 7th international conference on Future networks and distributed systems

作者： Ahcene Bounceur Mohammad Hammoudeh Jameleddine Hassine Madani Bezoui Adel Fadhl Ahmed Muhamad Felemban Computer Science Department King Fahd University of Petroleum and Minerals Saudi Arabia Information and Computer Science Department King Fahd University of Petroleum and Minerals Saudi Arabia CESI LINEACT CESI LINEACT France

ISBN: (纸本)9798400709036

This article introduces a novel method for generating random binary sequences from Random Polar Angles (RPA). These sequences can be derived from an image, akin to QR-Codes, making them suitable for cryptographic applications and information coding systems. The proposed method allows the generation of multiple codes using the same image. It is based on the LPCN (Lowest Polar Angle Connectivity Node) algorithm, which, in each iteration, selects a node from a graph that forms the minimal polar angle with its neighbor found in the previous iteration. The decision on the type of bit to generate (0 or 1) is based on the nature of the angles found (acute or obtuse). The primary objective of this research is to facilitate the transformation of a graph’s image representation into a concealed binary code, thereby enhancing data security. We have assessed the randomness of the generated codes through entropy analysis, revealing greater unpredictability.

关键词： LPCN algorithm

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