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15th IEEE International Conference on Security of Information and Networks, SIN 2022

作者： Yildiz, Ali Kerem Atmaca, Aykut Solak, Ali Ozgur Tursun, Yekta Can Bahtiyar, Serif Istanbul Technical University Ayazaga Campus Cyber Security and Privacy Research Lab SPFLab Department of Computer Engineering Maslak Istanbul Turkey

ISBN: (数字)9781665454650

ISBN: (纸本)9781665454650

The blockchain network is often considered a reliable and secure network. However, some security attacks, such as eclipse attacks, have a significant impact on blockchain networks. In order to perform an eclipse attack, the attacker must be able to control enough IP addresses. This type of attack can be mitigated by blocking incoming connections. Connected machines may only establish outbound connections to machines they trust, such as those on a whitelist that other network peers maintain. However, this technique is not scalable since the solution does not allow nodes with new incoming communications to join the network. In this paper, we propose a scalable and secure trust-based solution against eclipse attacks with a peer-selection strategy that minimizes the probability of eclipse attacks from nodes in the network by developing a trust point. Finally, we experimentally analyze the proposed solution by creating a network simulation environment. The analysis results show that the proposed solution reduces the probability of an eclipse attack and has a success rate of over 97%. © 2022 IEEE.

关键词： Blockchain

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An effective communication and computation model based on a hybridgraph-deeplearning approach for SIoT

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Digital Communications and Networks 2022年第6期8卷 900-910页

作者： M.S.Mekala Gautam Srivastava Ju H.Park Ho-Youl Jung Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38544South Korea RLRC LAb for Autonomous Vehicle Parts and Materials Innovation Yeungnam UniversityGyeongsan38544South Korea Department of Math and Computer Science Brandon UniversityBrandonCanada Department of Electrical Engineering Yeungnam UniversityGyeongsanSouth Korea Research Centre for Interneural Computing China Medical UniversityTaichungTaiwanChina

Social Edge Service(SES)is an emerging mechanism in the Social Internet of Things(SIoT)orchestration for effective user-centric reliable communication and *** services are affected by active and/or passive attacks such as replay attacks,message tampering because of sharing the same spectrum,as well as inadequate trust measurement methods among intelligent devices(roadside units,mobile edge devices,servers)during computing and *** issues lead to computation and communication overhead of servers and computation *** address this issue,we propose the HybridgrAph-Deep-learning(HAD)approach in two stages for secure communication and ***,the Adaptive Trust Weight(ATW)model with relation-based feedback fusion analysis to estimate the fitness-priority of every node based on directed graph theory to detect malicious nodes and reduce computation and communication ***,a Quotient User-centric Coeval-Learning(QUCL)mechanism to formulate secure channel selection,and Nash equilibrium method for optimizing the communication to share data over edge *** simulation results confirm that our proposed approach has achieved effective communication and computation performance,and enhanced Social Edge Services(SES)reliability than state-of-the-art approaches.

关键词： Edge computing Adaptive trust weight(ATW)model Quotient user-centric coeval-learning(QUCL)mechanism Deep learning Service reliability

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Characterizing Speed Performance of Multi-Agent Reinforcement Learning∗

Characterizing Speed Performance of Multi-Agent Reinforcemen...

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12th International Conference on Data Science, Technology and Applications, DATA 2023

作者： Wiggins, Samuel Meng, Yuan Kannan, Rajgopal Prasanna, Viktor Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles United States DEVCOM Army Research Lab Los Angeles United States

ISBN: (纸本)9789897586644

Multi-Agent Reinforcement Learning (MARL) has achieved significant success in large-scale AI systems and big-data applications such as smart grids, surveillance, etc. Existing advancements in MARL algorithms focus on improving the rewards obtained by introducing various mechanisms for inter-agent cooperation. However, these optimizations are usually compute- and memory-intensive, thus leading to suboptimal speed performance in end-to-end training time. In this work, we analyze the speed performance (i.e., latency-bounded throughput) as the key metric in MARL implementations. Specifically, we first introduce a taxonomy of MARL algorithms from an acceleration perspective categorized by (1) training scheme and (2) communication method. Using our taxonomy, we identify three state-of-the-art MARL algorithms-Multi-Agent Deep Deterministic Policy Gradient (MADDPG), Target-oriented Multi-agent Communication and Cooperation (ToM2C), and Networked Multi-agent RL (NeurComm) - as target benchmark algorithms, and provide a systematic analysis of their performance bottlenecks on a homogeneous multi-core CPU platform. We justify the need for MARL latency-bounded throughput to be a key performance metric in future literature while also addressing opportunities for parallelization and acceleration. © 2023 by SCITEPRESS–Science and Technology Publications, Lda.

关键词： Taxonomies

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Towards Cluster-Based Split Federated Learning Approach for Continuous User Authentication 7

Towards Cluster-Based Split Federated Learning Approach for ...

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7th Cyber Security in Networking Conference, CSNet 2023

作者： Wazzeh, Mohamad Arafeh, Mohamad Ould-Slimane, Hakima Talhi, Chamseddine Mourad, Azzam Otrok, Hadi Department of Software and It Engineering Montreal Canada Department of Mathematics and Computer Science Trois-Rivières Canada Cyber Security Systems and Applied Ai Research Center Lebanese American University Department of Csm Beirut Lebanon New York University Division of Science Abu Dhabi United Arab Emirates Khalifa University Department of Eecs Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350342871

In today's rapidly evolving technological landscape, ensuring the security of systems requires continuous authentication over sessions and comprehensive access management during user interaction with a device. With the increasing use of smartphones and Internet of Things (IoT) devices, Split Learning (SL) and Federated Learning (FL) have emerged as promising technologies that can tackle the authentication problem while protecting the user's private data. The SL distributed technology enables users with limited resources to complete neural network model training with server assistance, lessening the computational burden from the client side. In addition, FL aims to combine knowledge between different nodes collaboratively. The privacy and security of the user's data are ensured in both approaches, as only the models' weights are shared with a server. This study employs a cluster-based approach using split learning and federated learning techniques to improve the efficiency and robustness of training Machine Learning (ML) models. We compare the approaches' performance to baseline methods and demonstrate their advantages using the UMDAA-02-FD face detection and MNIST datasets. Our findings show that combining both technologies achieves high accuracy in continuous authentication scenarios while maintaining user privacy. These results highlight the importance of SL and FL in cybersecurity, enabling continuous authentication and demonstrating their potential to revolutionize how we address security. © 2023 IEEE.

关键词： Internet of things

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Probabilistic Graph Modeling based Safety Classifier Algorithm for Smart Transportation

Probabilistic Graph Modeling based Safety Classifier Algorit...

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2023 International Symposium on Green Technologies and Applications, ISGTA 2023

作者： Karkouri, Najib El Tigani, Smail Saadane, Rachid Chehri, Abdelah Pierre, Samuel Neya, Noureddine Mathematics and Informatics Lab Faculty of Sciences University Ibno Toufial Kenitra Morocco Research and Development Unit Accsellium LLC Fez Morocco SIRC-LaGeS Hassania School of Public Labors Casablanca20250 Morocco Department of Mathematics and Computer Science Royal Military College of Canada Kingston Canada Department of Computer and Software Engineering Polytechnique Montreal Canada

This research introduces a novel Probabilistic Graph Modeling-based Safety Classifier Algorithm designed for the purpose of classifying road safety in smart transportation systems. Leveraging a combination of numerical integration methods and Gaussian kernel density estimation models, the proposed algorithm offers an effective approach to assess and categorizing the safety levels of roads. The integration of these techniques enables a comprehensive analysis, allowing for a more nuanced understanding of the complex interactions within transportation networks. The algorithm's efficacy in road safety classification holds promising implications for enhancing traffic management and promoting safer urban environments. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Accident prevention

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Optimization of 5G Antenna Using Machine Learning

Optimization of 5G Antenna Using Machine Learning

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IEEE Middle East and North Africa Communications Conference (MENACOMM)

作者： Aline Hassan Aya Sharanek Rayan Hamzeh Dina Serhal Rached Zantout Electrical and Computer Engineering Department American University of Beirut Beirut Lebanon Electrical and Computer Engineering Department Rafik Hariri University Mechref Lebanon EFREI Research Lab EFREI Paris Paris France

ISBN: (数字)9798331519957

ISBN: (纸本)9798331519964

This paper presents a machine learning (ML)-based approach to optimize 5G base station patch antennas operating in the 3.3-4.2 GHz range. Traditional antenna design methods are often complex and time-consuming, particularly as the demand for high-speed communication is increasing, motivating the use of ML. Due to the lack of existing data for training the ML models, a custom dataset was created to be used in training and testing of the ML models. The optimized antenna achieved a return loss less than -10 dB and a resonant frequency of 3.5 GHz. An array of the optimized antennas was also designed and analyzed for base station applications. To select the best ML model, the project involved training and testing various models. Random Forest was identified as the best model for the single antenna, while Random Tree performed best for the antenna array.

关键词： Training Base stations 5G mobile communication Design methodology Resonant frequency Data models Random forests Optimization Antenna arrays Testing

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Big Data Value Graph: Enhancing Security and Generating New Value from Big Data

Big Data Value Graph: Enhancing Security and Generating New ...

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2023 Italian Conference on Cyber Security, ITASEC 2023

作者： Gervasi, Massimiliano Totaro, Nicolò G. Fornaio, Anna Caivano, Danilo Department of Innovation Engineering University of Salento Lecce Italy University of Salento Lecce Italy Cybersecurity Research Lab - CRLab University of Salento Lecce Italy Department of Computer Science University of Bari "Aldo Moro" Bari Italy

The opportunity to extract value from data has become a key capability for organisations, especially with the spread of Big Data. The Data Mesh is a model that proposes a solution for Big Data management. In this paper, we propose that the integration of this model with the Big Data Value Chain can provide benefits for organisations participating in the mesh. The two main benefits relate to security and Value Generation. The new framework resulting from this merging allows to increase the level of security and the Value Generated for participants. Throughout the paper, the concepts of Big Data and Data Mesh will be introduced. Then, our proposed integration and how it can positively affect the levels of security and the results of Value Generation will be explained. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Big data

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An Efficient Attention-Based Strategy for Anomaly Detection in Surveillance Video

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computer Systems Science & engineering 2023年第9期46卷 3939-3958页

作者： Sareer Ul Amin Yongjun Kim Irfan Sami Sangoh Park Sanghyun Seo Department of Computer Science and Engineering Chung-Ang UniversitySeoul06974Korea Intelligent Convergence Research Lab. ETRIDaeJeon34129Korea Department of Electrical and Electronics Engineering Chung-Ang UniversitySeoul06974Korea College of Art and Technology Chung-Ang UniversityAnseong17546Korea

In the present technological world,surveillance cameras generate an immense amount of video data from various sources,making its scrutiny tough for computer vision *** is difficult to search for anomalous events manually in thesemassive video records since they happen infrequently and with a low probability in real-world monitoring ***,intelligent surveillance is a requirement of the modern day,as it enables the automatic identification of normal and aberrant behavior using artificial intelligence and computer vision *** this article,we introduce an efficient Attention-based deep-learning approach for anomaly detection in surveillance video(ADSV).At the input of the ADSV,a shots boundary detection technique is used to segment prominent ***,The Lightweight ConvolutionNeuralNetwork(LWCNN)model receives the segmented frames to extract spatial and temporal information from the intermediate *** that,spatial and temporal features are learned using Long Short-Term Memory(LSTM)cells and Attention Network from a series of frames for each anomalous activity in a *** detect motion and action,the LWCNN received chronologically sorted ***,the anomaly activity in the video is identified using the proposed trained ADSV *** experiments are conducted on complex and challenging benchmark *** addition,the experimental results have been compared to state-ofthe-artmethodologies,and a significant improvement is attained,demonstrating the efficiency of our ADSV method.

关键词： Attention-based anomaly detection video shots segmentation video surveillance computer vision deep learning smart surveillance system violence detection attention model

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Automatic Assistance to Mitigate Rollback Inconsistencies in Collaborative Edits

Research Square

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research Square 2025年

作者： Mondal, Saikat Uddin, Gias Roy, Chanchal K. York University Canada Software Research Lab Department of Computer Science University of Saskatchewan Canada Lab Department of Electrical and Software Engineering University of Calgary Canada

The success of technical Q&A sites such as Stack Overflow depends on two key factors: (a) active user participation and (b) the quality of the shared knowledge. Stack Overflow introduced an edit system that allows users to suggest improvements to posts (i.e., questions and answers) to enhance the quality of the content. However, users, such as post owners or site moderators, can reject these suggested edits by rollbacks due to unsatisfactory, low-quality edits or violating edit guidelines. Unfortunately, subjectivity bias in determining whether an edit is satisfactory or unsatisfactory can lead to inconsistencies in the rollback decisions. For example, one user might accept the formatting of a method name (e.g., getActivity()) as a code term, while another might reject it. Such inconsistencies can demotivate and frustrate users whose edits are rejected. Furthermore, several post owners prefer to keep their content unchanged and even resist necessary edits. As a result, they sometimes roll back necessary edits and revert posts to a flawed version, which violates editing guidelines. The problems mentioned above are further compounded by the lack of specific guidelines and tools to assist users in ensuring consistency in user rollback actions. In this study, we investigate the types, prevalence, and impact of rollback edit inconsistencies and propose a solution to address them. The outcomes of this research are fivefold. First, we manually investigated 764 rollback edits (382 questions + 382 answers) and identified eight types of inconsistent rollback. Second, we surveyed 44 practitioners to assess the impact of rollback inconsistencies. More than 80% of the participants found our identified inconsistency types detrimental to post quality. Third, we developed rule-based algorithms and Machine Learning (ML) models to detect the eight types of rollback inconsistencies. Both approaches achieve over 90% accuracy. Fourth, we introduced a tool, iEdit, which integrates these

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Efficient Optimization in RIS-Assisted UAV System Using Deep Reinforcement Learning for mmWave-NOMA 6G Communications

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IEEE Internet of Things Journal 2025年

作者： Sobhi-Givi, Sima Nouri, Mahdi Shayesteh, Mahrokh G. Behroozi, Hamid Kwon, Hyun Han Piran, Md. Jalil Urmia University Electrical and Computer Engineering Department Urmia Iran University of Mohaghegh Ardabili Electrical and Computer Engineering Department Ardabil Iran Sharif University of Technology Electrical Engineering Department Tehran Iran Sharif University of Technology Wireless Research Lab ACRI Electrical Engineering Department Tehran Iran Sejong University Department of Civil and Environmental Engineering Seoul05006 Korea Republic of Sejong University Department of Computer Science and Engineering Seoul05006 Korea Republic of

In the evolving landscape of wireless communications for 5G, 6G, and beyond, the deployment of unmanned aerial vehicles (UAVs) has emerged as a groundbreaking strategy to expand coverage areas due to their flexibility and ease of deployment. Simultaneously, reflecting intelligent surfaces (RIS) have introduced a transformative paradigm aimed at improving key performance metrics, such as average sum-rate and energy efficiency (EE). The seamless integration of advanced technologies, including UAVs, RIS, and non-orthogonal multiple access (NOMA), presents a promising avenue for significantly boosting the performance and efficiency of next-generation communication systems. This study investigates EE maximization for two scenarios in a NOMA-enabled mmWave network: multi-UAV-mounted base stations (BS) and multi-UAV-mounted distributed RIS. In both cases, each UAV serves a NOMA cluster with imperfect successive interference cancellation (SIC), capturing the impact of hardware impairments in real-world NOMA systems. For each scenario, an optimization problem is formulated to maximize EE by jointly optimizing the beamforming matrix, phase shift matrix, NOMA power allocation, and UAV 3D placement. The non-convex problems are tackled using both model-based and model-free deep reinforcement learning (DRL) algorithms under constraints such as minimum quality of service (QoS), beamforming and phase shift limits, and UAV trajectory constraints. The simulation results demonstrate that the proposed DRL algorithms significantly enhance spectral efficiency (SE) and EE, showcasing their suitability for 6G communication systems. Furthermore, a comparative analysis with orthogonal multiple access (OMA) and spatial division multiple access (SDMA) confirms that NOMA outperforms both techniques, achieving substantial gains in efficiency and performance. © 2014 IEEE.

关键词： Deep reinforcement learning

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