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International Middle-East Power System Conference, MEPCON

作者： Yusuf Ademola Sodiq Tarek Gaber Reham Amin Angel Jimenez-Aranda Mohamed Galeela School of Science Engineering & Environment The University of Salford Manchester United Kingdom Computer Sciences Department Faculty of Computers and Informatics Suez Canal University Ismailia Egypt Centre for Sustainable Innovation Salford Business School The University of Salford Manchester United Kingdom TNEI Service LTD Manchester United Kingdom Electrical Power Engineering Department Faculty of Engineering Cairo University

ISBN: (数字)9798350379648

ISBN: (纸本)9798350379655

Smart grid systems are essential components of modern power infrastructures, where machine learning has found wide-ranging applications, particularly in the development of intrusion detection systems. However, such systems remain vulnerable to adversarial attacks, including data poisoning techniques like label flipping attack. This paper proposes a novel, adversarial-aware machine learning-based intrusion detection system that is robust against label flipping attacks, thereby enhancing the integrity and reliability of the energy distribution network. The proposed solution was evaluated using the ICS cyber-attack dataset from the University of Queensland, with multiple models undergoing progressive testing. Initially, the base model employed three key algorithms: Random Forest, K-Nearest Neighbors, and XGBoost. These models were subjected to simulated label flipping attacks with varying intensities (10%-50%), which caused a significant decline in performance. To counter this, a countermeasure technique was integrated into the system, successfully restoring model accuracy and achieving a consistent accuracy of 75.96% across all attack intensities. Among the algorithms, Random Forest demonstrated the greatest resilience, showing a notable recovery after mitigation. This study underscores the critical role of preventive defense strategies and robust machine learning algorithms in safeguarding smart grids against cyber threats.

关键词： Accuracy Machine learning algorithms Prevention and mitigation Intrusion detection Smart grids Security Reliability Integrated circuit modeling Random forests Resilience

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Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems

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IEEE Transactions on Evolutionary Computation 2025年

作者： Shiraishi, Hiroki Hayamizu, Yohei Hashiyama, Tomonori Takadama, Keiki Ishibuchi, Hisao Nakata, Masaya Yokohama National University Faculty of Engineering Yokohama240-8501 Japan State University of New York at Binghamton Department of Computer Science BinghamtonNY13902 United States University of Electro-Communications Department of Informatics Tokyo182-8585 Japan University of Tokyo Information Technology Center Chiba277-0882 Japan Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China

Rule representations significantly influence the search capabilities and decision boundaries within the search space of Learning Classifier Systems (LCSs). However, it is very difficult to choose an appropriate rule representation for each problem. Additionally, some problems benefit from using different representations for different subspaces within the input space. Thus, an adaptive mechanism is needed to choose an appropriate rule representation for each rule in LCSs. This article introduces a flexible rule representation using a four-parameter beta distribution and integrates it into a fuzzy-style LCS. The four-parameter beta distribution can form various function shapes, and this flexibility enables our LCS to automatically select appropriate representations for different subspaces. Our rule representation can represent crisp/fuzzy decision boundaries in various boundary shapes, such as rectangles and bells, by controlling four parameters, compared to the standard representations such as trapezoidal ones. Leveraging this flexibility, our LCS is designed to adapt the appropriate rule representation for each subspace. Moreover, our LCS has a generalization bias to produce as many crisp rules as possible. Experimental results on real-world classification tasks show that our LCS significantly outperformed LCSs with popular rule representations in test classification accuracy on up to 17 of the 25 datasets tested. © 1997-2012 IEEE.

关键词： Adversarial machine learning

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SEMU5G: an Emulator for Improving Model Accuracy and Performance Evaluation of 5G Network Core Architectures based on SDN

SEMU5G: an Emulator for Improving Model Accuracy and Perform...

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International Conference on Wireless and Telematics (ICWT)

作者： Eki Ahmad Zaki Hamidi Arga Valen Lazuardi Muhammad Litfan Rahmansyah Salman Alfarizi Novel Bajri Rohmat Tulloh Ridha Muldina Negara Syaiful Ahdan Jupriyadi Electrical Engineering Department UIN Sunan Gunung Djati Bandung Bandung Indonesia School of Electrical Engineering Telkom University Bandung Indonesia Telecommunication Technology School of Applied Science Telkom University Bandung Indonesia The University Center of Excellence Intelligent Sensing-IoT Telkom University Indonesia Faculty of Engineering & Computer Science Universitas Teknokrat Indonesia Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Bandarlampung Indonesia

ISBN: (数字)9798350377057

ISBN: (纸本)9798350377064

The rapid advancement of 5G Radio Access Network (RAN) architecture is facilitating the construction of 5G networks, marking a significant milestone in telecommunications evolution. Given the complexity of the 5G core architecture, traditional simulation methods are insufficient, necessitating novel approaches. Emulator systems are crucial for creating dynamic, controlled environments that enable exploration of real-world scenarios without physical constraints. SEMU5G, a 5G core emulator, SDN controller, and RAN simulator, utilizes Open5GS implemented in Docker Container for test system flexibility and isolation. Additionally, an SDN controller is integrated to monitor data flows in the User Plane Function (UPF) and gNB simulated by UERANSIM in Mininet-WiFi. This comprehensive integration facilitates effective and flexible real-world exploration, providing a dynamic and controlled test environment for 5G core research. Scenario testing comprises two stages: firstly, a fixed network topology is employed to compare resource usage and confirm successful SEMU5G integration. Secondly, a mobile network topology is utilized to implement a mobile device scenario and compare the Quality of Service (QoS) of SEMU5G with other available emulators. These stages ensure thorough evaluation of SEMU5G's performance and its comparative advantage over existing solutions.

关键词： Performance evaluation 5G mobile communication Network topology Emulation computer architecture Quality of service Aerodynamics

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Efficient Peer-to-Peer Unicasting for VANET Architectures via Enhanced Monolithic Protocols 7

Efficient Peer-to-Peer Unicasting for VANET Architectures vi...

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7th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2022

作者： Amponis, George Lagkas, Thomas Karampatzakis, Dimitris Radoglou-Grammatikis, Panagiotis Argyriou, Vasileios Moscholios, Ioannis Goudos, Sotirios Sarigiannidis, Panagiotis Department of Computer Science International Hellenic University Kavala Campus Greece K3Y Ltd. Sofia Bulgaria Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Faculty of Science Engineering and Computing Kingston University London United Kingdom Department of Informatics and Telecommunications University of Peloponnese Tripolis Greece Department of Physics Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (数字)9798350398588

ISBN: (纸本)9798350398588

With the advent of vehicular networks and the corresponding requirements, developers are faced with new challenges associated with fundamental protocol attributes and capabilities. In our work, we consider ESP-NOW: a connectionless WiFi communication protocol developed by Espressif and featuring short packet (up to 250 bytes) transmission. It is different from more traditional WiFi protocols, as the upper five layers of the Open Systems Interconnection (OSI) stack are simplified to a single monolithic layer. The data does not need to travel through all the OSI-or Transmission Control Protocol (TCP)-specific layers, which measurably reduces over-head, processing delay all while increasing system responsiveness. The presented work targets the considerable research gap in regards to this protocol and constitutes an attempt to dissect it and provide experimental results, which prove to be promising for highly mobile clusters. Relevant developments find great applicability in ad hoc and vehicular communications, where real-time connectivity and sufficient coverage are pivotal in supporting reliable networking. In this paper, we also investigate the usage of a modified 802.11 standard to enable long-range and low-power interfacing amongst networked vehicular nodes. © 2022 IEEE.

关键词： Vehicular ad hoc networks

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Zero-Day Malware Classification Using Deep Features with Support Vector Machines 10

Zero-Day Malware Classification Using Deep Features with Sup...

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10th IEEE International Conference on Intelligent Computing and Information Systems, ICICIS 2021

作者： El-Sayed, Rania El-Ghamry, Amir Gaber, Tarek Hassanien, Aboul Ella Computer Engineering Cairo University Scientific Research Group Cairo Egypt Mansoura University Faculty of Computers and Information Mansoura Egypt Suez Canal University Faculty of Computers Informatics Ismailia Egypt School of Science Engineering and Environment University of Salford United Kingdom Faculty of Computer and Ai Cairo University Scientific Research Group Cairo Egypt

ISBN: (纸本)9781665440769

IoT devices are increasingly used every day. However, their limited resources cause them to be vulnerable to any malware, malicious software that causes harm to any device without the user's knowledge. Malwares are called zero-day attacks, a serious threat to internet security since they exploit zero-day vulnerabilities with unknown nature, making them difficult to detect. To solve this problem, the structure of these malware need to be known and analyzed, therefore a small dataset of different types of malware including zero-day attack, is live-captured from network traffic to form 1000 PCAP files representing malware and normal behavior that is used as a source for traffic analysis and malware classification. Most traditional malware detection systems proposed in the literature use signature-based methods, so these systems cannot detect unknown malware types. This paper aims to introduce novel IoT image-based malware traffic analysis approaches (i.e., anomaly detection) using machine and deep learning techniques that can detect unknown malware. To achieve better analysis, PCAP files are represented as RGB images, then the supervised machine or deep learning algorithm is carefully selected to achieve the best results. In this paper, seven supervised learning algorithms with different s and levels of complexity are tested and compared with analysis to have the best one selected. The seven s are divided into two categories: Two-Layer CNN, Four-Layer CNN, VGG16, and under the category of CNN classifiers and Logistic Regression, Support Vector Machine, and K-Nearest Neighbours under the category of Ordinary Classifiers. Experimental results show that the highest performance reached is 94% with the SVM classifier on MobileNetv2 features due to its fast and stable training with fewer resources compared to the other models. © 2021 IEEE.

关键词： Internet of things

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Real Time Sound Source Localization Using von-Mises ResNet

Real Time Sound Source Localization Using von-Mises ResNet

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IEEE/SICE International Symposium on System Integration

作者： Mert Bozkurtlar Benjamin Yen Katsutoshi Itoyama Kazuhiro Nakadai Department of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo Japan Department of Industrial Engineering Faculty of Management Istanbul Technical University Harbiye Maçka/Beşiktaş/İstanbul Türkiye Department of Computer Engineering Faculty of Computer and Informatics Engineering Istanbul Technical University Ayazaga Sariyer/İstanbul Türkiye Japan Society for the Promotion of Science (JSPS) at the Nakadai Lab Tokyo Institute of Technology Honda Research Institute Japan Co. Ltd Saitama Japan

This paper addresses the task of learning periodic information using deep neural networks to achieve real-time, environment-independent sound source localization. Previous papers showed phase data is the most significant cue in sound source localization tasks and the proposed vM-B DNN was validated to be able to handle such periodic information using on synthesized data. However, they haven't shown its effectiveness and robustness in realistic use cases. This paper introduces a more complex model based on residual networks and adapts vM-B activation function for convolutional layers for use cases that require real-time predictions in dynamically changing environments.

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LEARNING SURROGATE EQUATIONS FOR THE ANALYSIS OF AN AGENT-BASED CANCER MODEL

arXiv

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arXiv 2025年

作者： Burrage, Kevin Burrage, Pamela Kreikemeyer, Justin N. Uhrmacher, Adelinde M. Weerasinghe, Hasitha N. School of Mathematical Sciences Queensland University of Technology BrisbaneQLD Australia Department of Computer Science University of Oxford United Kingdom Modelling and Simulation Institute for Visual and Analytic Computing Faculty of Informatics and Electrical Engineering University of Rostock Rostock Germany

In this paper, we adapt a two species agent-based cancer model that describes the interaction between cancer cells and healthy cells on a uniform grid to include the interaction with a third species – namely immune cells. We run six different scenarios to explore the competition between cancer and immune cells and the initial concentration of the immune cells on cancer dynamics. We then use coupled equation learning to construct a population-based reaction model for each scenario. We show how they can be unified into a single surrogate population-based reaction model, whose underlying three coupled ordinary differential equations are much easier to analyse than the original agent-based model. As an example, by finding the single steady state of the cancer concentration, we are able to find a linear relationship between this concentration and the initial concentration of the immune cells. This then enables us to estimate suitable values for the competition and initial concentration to reduce the cancer substantially without performing additional complex and expensive simulations from an agent-based stochastic model. The work shows the importance of performing equation learning from agent-based stochastic data for gaining key insights about the behaviour of complex cellular dynamics. © 2025, CC BY.

关键词： Lung cancer

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Newsgroup Topic Extraction using Probabilistic Inverse Cluster Frequency Term-Cluster Weighting and Growing Neural Gas Clustering

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2021年第1期48卷

作者： Adinugroho, Sigit Rahman, Muh Arif Syauqy, Dahnial Department of Informatics Faculty of Computer Science Brawijaya University Malang Indonesia Department of Computer Engineering Faculty of Computer Science Brawijaya University Malang Indonesia

Effectiveness of a topic extraction method is depends on the capability to extract information from a large amount of data. This paper proposes a new solution for selecting a set of topics from text documents as well as a new approach to set the weight of a term in clusters of documents. The process of revealing topics from multiple documents starts with a preprocessing step, which aims to omit the unnecessary portion of the text. After that, the weights of terms in documents are calculated based on the term frequency-inverse document frequency method. Then, feature transformation based on singular value decomposition is employed to build weight for clustering. The clustering process is conducted using the Growing Neural Gas method. Finally, to determine the weights of terms in clusters as a way of selecting topics from clusters, the proposed probabilistic inverse cluster frequency term-cluster method is applied. Experiments show that the framework attains satisfactory results indicated by the average accuracy of 0.8606, 0.7406, 0.4039, and 0.6647 for topics obtained from Binary2, Multi5, Multi7, and Multi10 categories of 20Newsgroup dataset. © 2021, IAENG International Journal of computer Science. All Rights Reserved.

关键词： Singular value decomposition

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Virtual Horizons: Pioneering the Metaverse in Education and Industry Across ASEAN and Beyond

Virtual Horizons: Pioneering the Metaverse in Education and ...

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International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON

作者： Ittipon Yamyuan Sushank Chaudhary Muhammad Saadi Piya Kovintavewat Juthatip Wisanmongkol Mitsunori Makino La-or Kovavisaruch Kamol Kaemarungsi Taweesak Sanpechuda Lunchakorn Wuttisittikulkij Nitthita Chirdchoo Petchpilin Tontiwinyoopong Pisit Vanichchanunt Pruk Sasithong Suebphong Noisri Avishi Waidyaratne Panithan La-aiddee Rosa Delima Department of Electrical Engineering Chulalongkorn University Thailand Department of Electrical Engineering University of Central Punjab Pakistan Faculty of Science and Technology Nakhon Pathom Rajabhat University Nakhon Pathom Thailand National Electronics and Computer Technology Center Pathumthani Thailand Department of Information and System Engineering Chuo University Japan Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Informatics Department Universitas Kristen Duta Wacana Yogyakarta Indonesia

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

The integration of the Metaverse into the educational sector heralds a transformative era in teaching and learning paradigms, with the ASEAN region at its forefront. This manuscript examines the Metaverse's potential to transcend traditional educational barriers such as resource limitations, geographic isolation, and the lack of personalized learning. Active case studies from Nakhon Pathom Rajabhat University (NPRU), Thailand, the University of Central Punjab (UCP), Pakistan, the National Electronics and computer Technology Center, Thailand, and Chuo University, Japan, are presented to offer a comprehensive overview of the Metaverse's current applications. These case studies showcase the Metaverse's role in creating inclusive, immersive, and engaging educational experiences. The paper identifies critical research gaps in technological feasibility, pedagogical adaptation, and equitable access that need bridging to harness the Metaverse's full potential. It underscores the necessity for robust technological infrastructure, innovative teaching methodologies for 3D learning environments, and inclusivity strategies for diverse socioeconomic and physical ability spectra. The inclusion of Chuo University's advancements extends the discourse beyond ASEAN, indicating the global momentum of Metaverse adoption in education. The paper posits that ASEAN's venture into this digital frontier, alongside international collaborations, prepares the ground for a more interconnected and technologically advanced future. This investigation into the ASEAN region and its collaboration with Japanese innovation underscores a unique global position, suggesting that the convergence of technology and education is key to resolving deep-rooted disparities in educational access and quality.

关键词： Industries Technological innovation Three-dimensional displays Metaverse Education Collaboration International collaboration

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E-Commerce Supply Chains with Considerations of Cyber-Security

E-Commerce Supply Chains with Considerations of Cyber-Securi...

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computer Science and Emerging Technologies (CSET), International Conference on

作者： Firas H. Zawaideh Waheeb Abu-ulbeh Yousef I. Majdalawi Muhammad D. Zakaria Julaily Aida Jusoh Sunanda Das Department of Cyber Security Faculty of Science and Informaiton Technology Irbid National University Irbid Jordan Cybersecurity Department Faculty of Administrative sciences and Informatics Al-Istiqlal University Palestine Business & E-commerce Department Faculty of Management and Financial Sciences Petra University Amman Faculty of Informatics and Computing UniSZA Malaysia School of Computer Science and Engineering Jain University Bangalore India

As the global e-commerce landscape continues to expand rapidly, the efficiency and security of supply chains have become critical areas of concern. With the increasing reliance on digital platforms and the interconnected nature of supply chains, the vulnerability to cyber threats has escalated significantly. This research paper delves into the multifaceted domain of e-commerce supply chains with a primary focus on cyber-security considerations. The objective of this study is to assess the existing cyber-security measures and vulnerabilities within e-commerce supply chains while proposing effective strategies to mitigate potential risks. Through an extensive literature review, the paper highlights various cyber-security challenges faced by e-commerce enterprises, including data breaches, ransomware attacks, and supply chain disruptions caused by cyber-attacks on logistics partners. By analyzing real-world case studies and industry reports, the research provides valuable insights into the consequences of cyber-security lapses and their impact on supply chain resilience and consumer trust. The research also explores the technological advancements and best practices that can fortify e-commerce supply chains against cyber threats. It examines the role of encryption, multi-factor authentication, secure payment gateways, and cloud-based security solutions in safeguarding sensitive data and maintaining the integrity of supply chain operations. Additionally, the study investigates the importance of employee training and awareness programs to cultivate a cyber-security-oriented organizational culture. Furthermore, this paper considers the legal and regulatory aspects of cyber-security in the context of e-commerce supply chains. By examining international laws, industry standards, and compliance requirements, it highlights the responsibilities and obligations of e-commerce businesses to protect customer data and secure the flow of goods throughout the supply chain. The efficie

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