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Enhancing intrusion detection in IIoT: optimized CNN model with multi-class SMOTE balancing

Neural Computing and Applications 2024年第24期36卷 14643-14659页

作者： Eid, Abdulrahman Mahmoud Soudan, Bassel Nassif, Ali Bou Injadat, MohammadNoor Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan

This work introduces an intrusion detection system (IDS) tailored for industrial internet of things (IIoT) environments based on an optimized convolutional neural network (CNN) model. The model is trained on a dataset that was balanced using a novel multi-class implementation of synthetic minority over-sampling technique (SMOTE) that ensures equal representation of all classes. Additionally, systematic optimization will be used to fine tune the hyperparameters of the CNN model and mitigate the effects of the increased size of the training dataset. Evaluation results will demonstrate substantial improvement in performance when the optimized CNN model is trained on the balanced dataset. The proposed IDS will be evaluated using the IIoT-specific WUSTL-IIOT-2021 dataset, and then its generalization capability will be verified using the non-domain specific UNSW_NB15 dataset. The model’s performance will be evaluated using accuracy, precision, recall, and F1-score metrics. The results will demonstrate that the proposed IDS is highly effective with performance exceeding 99.9% on all performance metrics. The IDS is also highly effective in detecting intrusion for generic IT networks achieving improvements in excess of 30% compared to the default baseline model. The results emphasize the versatility and effectiveness of the proposed IDS model, making it a reliable and adaptable solution for enhancing network security across diverse network environments. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Neural network models

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Self-stabilizing Byzantine-Tolerant Recycling 25th

Self-stabilizing Byzantine-Tolerant Recycling

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25th International Symposium on Stabilization, Safety, and Security of Distributed Systems, SSS 2023

作者： Georgiou, Chryssis Raynal, Michel Schiller, Elad M. Computer Science University of Cyprus Nicosia Cyprus IRISA Univ. Rennes 1 Rennes France Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden

ISBN: (纸本)9783031442735

Numerous distributed applications, such as cloud computing and distributed ledgers, necessitate the system to invoke asynchronous consensus objects for an unbounded number of times, where the completion of one consensus instance is followed by the invocation of another. With only a constant number of objects available, object reuse becomes vital. We investigate the challenge of object recycling in the presence of Byzantine processes, which can deviate from the algorithm code in any manner. Our solution must also be self-stabilizing, as it is a powerful notion of fault tolerance. Self-stabilizing systems can recover automatically after the occurrence of arbitrary transient-faults, in addition to tolerating communication and (Byzantine or crash) process failures, provided the algorithm code remains intact. We provide a recycling mechanism for asynchronous objects that enables their reuse once their task has ended, and all non-faulty processes have retrieved the decided values. This mechanism relies on synchrony assumptions and builds on a new self-stabilizing Byzantine-tolerant synchronous multivalued consensus algorithm, along with a novel composition of existing techniques. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fault tolerance

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An Interpolation-based Method for Numerical Simulation of Digital Controllers in Power System Dynamic Studies

TechRxiv

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TechRxiv 2023年

作者： Jafari, Mehran Bureau, Gautier Chiaramello, Marco Guironnet, Adrien Panciatici, Patrick Aristidou, Petros The Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Cyprus France

In recent years, the number of digital controllers employed in electric power systems has increased drastically. Either from the modernization of existing analog ones or the introduction of new, data-driven, or optimization-based controllers, they are now dominating the behavior of power systems. However, this introduces a challenge in the simulation and analysis of power systems dynamics as the existing numerical simulation methods are very time-consuming when tackling hybrid differential-algebraic systems. In this paper, a novel interpolation-based method is proposed for handling discrete time events in the numerical simulation of hybrid systems. This method allows to fully exploit the potential of variable time-step integration methods without requiring a time-step reduction in the case of discrete events. Therefore, it accelerates the numerical simulation of large-scale systems containing many digital controllers, which would be necessary for the near future. © 2023, CC BY.

关键词： Interpolation

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Distributed computing and shared memory-based utility list buffer miner with parallel frameworks for high utility itemset mining

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International Journal of Business Intelligence and Data Mining 2023年第2期23卷 125-149页

作者： Atmaja, Eduardus Hardika Sandy Sonawane, Kavita Department of Computer Engineering St. Francis Institute of Technology Mumbai India Department of Informatics Sanata Dharma University Yogyakarta Indonesia

High utility itemset mining (HUIM) is a well-known pattern mining technique. It considers the utility of the items that leads to finding high profit patterns which are more useful for real conditions. Handling large and complex dataset are the major challenges in HUIM. The main problem here is the exponential time complexity. Literature review shows multi-core approaches to solve this problem by parallelising the tasks, but it is limited to single machine resources and also needs a novel strategy. To address this problem, we proposed new strategies namely distributed computing (DC-PLB) and shared memory (SM-PLB)-based utility list buffer miner with parallel frameworks (PLB). It utilises cluster nodes to parallelise and distribute the tasks efficiently. Thorough experiments with results proved that the proposed frameworks achieved better runtime (448 s) in dense datasets compared to the existing PLB (2,237 s). It has effectively addressed the challenges of handing large and complex datasets. © 2023 Inderscience Enterprises Ltd.

关键词： Data mining

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Text Analysis and Recognition of Emotional Content Using Deep Learning Methods and BERT 23

Text Analysis and Recognition of Emotional Content Using Dee...

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23rd IEEE/ACIS International Conference on computer and Information Science, ICIS 2023

作者： Andrikakis, Efthymios Perikos, Isidoros Paraskevas, Michael Hatzilygeroudis, Ioannis University of Patras Department of Computer Engineering & Informatics Patras Greece University of Patras Computer Technology Institute and Press Diophantus Department of Computer Engineering & Informatics Patras Greece Department of Electrical and Computer Engineering University of Peloponnese Computer Technology Institute and Press Diophantus Patras Greece

ISBN: (纸本)9798350345841

Recognizing the emotional content of Natural Language sentences can improve the way humans communicate with a computer system by enabling them to recognize and imitate emotional expressions. In this paper, deep learning, deep neural networks as well as Transformers were examined. Specifically, we designed and developed deep learning methods and BERT-based implementations for recognizing emotional content in user-generated data. Extensive experiments were conducted using these models on a variety of textual data and all the designed methods were evaluated. The results of the study show that BERT achieved the best overall performance, getting an F1-score of 0.86 on the Twitter Data dataset. Also, the Bi-LSTM with Attention Mechanism and Bi-LSTM using Word2vec performed quite well, achieving F1-scores of 0.83 and 0.82, respectively. © 2023 IEEE.

关键词： Deep neural networks

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Operation Performance of a Ground Source Heat Pump System in the Mediterranean Climate Zone. First Results

SSRN

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SSRN 2023年

作者： Christodoulides, Paul Christou, Christakis Aresti, Lazaros Stylianou, Iosifina I. Florides, Georgios A. Faculty of Engineering and Technology Cyprus University of Technology Limassol Cyprus Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

This paper presents the configuration and performance of a Low Enthalpy Geothermal System successfully installed and utilized in the Mediterranean climate zone. Additionally, it examines the performance of different types of Ground Heat Exchangers (GHE), all installed in the same System. The Ground Source Heat Pump (GSHP) of the system consists of vertical ground heat exchangers (GHEs) in five different configurations, one double helicoidal coil in a well and an open loop (well) system. The entire system is constantly monitored by a Building Management System (BMS) that records the energy, flow, volume, incoming and outgoing temperature at critical points of the system. Based on the recorded values, the performance of the System was analyzed in a winter and summer working period, after examining the power flows in and out from critical points of the System. Results show higher heat exchange values inside the open well, both in winter and summer mode suggesting the usage of this type of GHE, where applicable. Additionally, the electric power consumed by the chillers which are the largest electricity consumers within the System, is approximately five times lower than the power placed in the building by the Geothermal System (COPsys between 4.5 and 5). In terms of primary energy savings, we can say with confidence the GSHP systems working under Mediterranean climate zone conditions, can be consider as high efficiency solutions. © 2023, The Authors. All rights reserved.

关键词： Geothermal heat pumps

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Wirelessly Enabled Beam Steering MSF Antenna for Biomedical Imaging Applications

Wirelessly Enabled Beam Steering MSF Antenna for Biomedical ...

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2023 URSI International Symposium on Electromagnetic Theory, EMTS 2023

作者： Bilal, Asif Quddious, Abdul Darvazehban, Amin Rezaeieh, Sasan Ahdi Iezekiel, Stavros Antoniades, Marco A. EMPHASIS Research Centre University of Cyprus Nicosia Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus KIOS Research Center for Excellence University of Cyprus Nicosia Cyprus TU Dresden Dresden Germany School of Information Technology and Electrical Engineering The University of Queensland Brisbane Australia Department of Electrical Computer & Biomedical Engineering Toronto Metropolitan University TorontoON Canada

ISBN: (纸本)9798350399288

In this work, a wirelessly enabled beam steering metasurface (MSF) antenna for bio-imaging is proposed. The design consists of multiple layers, the top layer consists of an MSF layer, while the bottom layer consists of two layers, one that contains the feeding antennas and the second consists of the feeding network. In the feeding network of the MSF antenna, an RF energy harvesting (EH) system has been incorporated that allows dynamic switching to achieve the desired beam steering required for torso scanning. In the designed EH system, a receiving antenna and rectifying circuit harvests the low power received RF signal at three different frequencies in the 2.4 GHz ISM band. In order to avoid coupling between the EH system and MSF antenna, the former operates in the 2.4 GHz ISM band, while the latter operates in the 900 MHz ISM band. The proposed approach has potential to be used in biomedical imaging applications and to provide an alternative to complex biasing networks required to reconfigure devices. © 2023 IEEE.

关键词： Energy harvesting

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Direct Against Indirect Short-Term Net Load Forecasting Using Machine Learning Principles for Renewable Microgrids 9

Direct Against Indirect Short-Term Net Load Forecasting Usin...

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9th IEEE International Smart Cities Conference, ISC2 2023

作者： Tziolis, Georgios Livera, Andreas Michail, Anna Makrides, George Georghiou, George E. University of Cyprus Pv Technology Laboratory Foss Research Centre for Sustainable Energy Department of Electrical and Computer Engineering Nicosia Cyprus

ISBN: (纸本)9798350397758

Net load forecasting (NLF) is a key component for the efficient operation and management of microgrids at high shares of renewables. Depending on the forecasting strategy followed, NLF is classified as direct or indirect. In this paper, a performance comparison was conducted between indirect and direct short-term NLF (STNLF) strategies in renewable microgrids. A STNLF model was constructed by utilizing Bayesian neural network (BNN) principles applied to datasets obtained from the university of cyprus microgrid and buildings. For the indirect STNLF, historical load and photovoltaic (PV) generation data, along with weather and categorical time-related data were used as inputs to develop the optimized BNN models for load and PV generation forecasting. The direct STNLF model achieved lower error (3.98% at the microgrid level) compared to the indirect one. © 2023 IEEE.

关键词： Machine learning

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A fusion of machine learning and cryptography for fast data encryption through the encoding of high and moderate plaintext information blocks

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Multimedia Tools and Applications 2025年第8期84卷 5349-5375页

作者： Shafique, Arslan Mehmood, Abid Alawida, Moatsum Elhadef, Mourad Rehman, Mujeeb Ur School of Biomedical Engineering University of Glasgow Glasgow United Kingdom Department of Computer Sciences Abu Dhabi University Abu Dhabi United Arab Emirates Cyber Technology Institute School of Computer Science and Informatics De Montfort University Leicester United Kingdom

Within the domain of image encryption, an intrinsic trade-off emerges between computational complexity and the integrity of data transmission security. Protecting digital images often requires extensive mathematical operations for robust security. However, this computational burden makes real-time applications unfeasible. The proposed research addresses this challenge by leveraging machine learning algorithms to optimize efficiency while maintaining high security. This methodology involves categorizing image pixel blocks into three classes: high-information, moderate-information, and low-information blocks using a support vector machine (SVM). Encryption is selectively applied to high and moderate information blocks, leaving low-information blocks untouched, significantly reducing computational time. To evaluate the proposed methodology, parameters like precision, recall, and F1-score are used for the machine learning component, and security is assessed using metrics like correlation, peak signal-to-noise ratio, mean square error, entropy, energy, and contrast. The results are exceptional, with accuracy, entropy, correlation, and energy values all at 97.4%, 7.9991, 0.0001, and 0.0153, respectively. Furthermore, this encryption scheme is highly efficient, completed in less than one second, as validated by a MATLAB tool. These findings emphasize the potential for efficient and secure image encryption, crucial for secure data transmission in rea-time applications. © The Author(s) 2024.

关键词： Cryptography

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Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

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computer Modeling in engineering & Sciences 2025年第2期142卷 1611-1636页

作者： Irfan Khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

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