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Enhancing malware detection performance: leveraging K-Nearest Neighbors with Firefly Optimization Algorithm

Multimedia Tools and Applications 2025年第12期84卷 10071-10094页

作者： Al Saaidah, Adeeb Abualhaj, Mosleh M. Shambour, Qusai Y. Abu-Shareha, Ahmad Adel Abualigah, Laith Al-Khatib, Sumaya N. Alraba’nah, Yousef H Department of Networks and Cybersecurity Al-Ahliyya Amman University Amman Jordan Department of Software Engineering Al-Ahliyya Amman University Amman Jordan Department of Data Science and Artificial Intelligence Al-Ahliyya Amman University Amman Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Research Center University of Tabuk Tabuk71491 Saudi Arabia MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon College of Engineering Yuan Ze University Taoyuan32003 Taiwan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia

Malware detection plays a crucial role in ensuring robust cybersecurity amidst the ever-evolving cyber threats. This research paper delves into the realm of machine learning (ML) algorithms for malware detection, with a specific emphasis on the K-Nearest Neighbors (KNN) algorithm, utilizing tailored parameter settings and the Firefly Optimization Algorithm (FOA). The study leverages the MalMem-2022 dataset to assess the efficacy of KNN and KNN with FOA in malware detection. The impact of parameter tuning and feature selection on classification is elucidated by comparing the performance of both approaches. Encouragingly, the results reveal promising advancements in one of the multiclass classification scenarios when employing KNN with FOA, yielding noteworthy enhancements in Accuracy, Recall, Precision, Matthews Correlation Coefficient, TNR, and F1-score by 2.65%, 2.65%, 2.47%, 3.59%, 0.17%, and 2.61%, respectively. These findings accentuate the significance of optimizing KNN’s parameters and implementing FOA for feature selection, culminating in heightened accuracy in malware detection. The symbiotic fusion of KNN and FOA exhibits remarkable effectiveness in augmenting the classification model’s performance, thus offering valuable insights for bolstering cybersecurity measures. Adopting ML algorithms, particularly KNN with specific parameter settings and FOA, presents a promising avenue for enhancing malware detection capabilities. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Nearest neighbor search

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An Economic Feasibility Study for Off-Grid Hybrid Renewable Energy Resources 31

An Economic Feasibility Study for Off-Grid Hybrid Renewable ...

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31st Southern African Universities Power engineering Conference, SAUPEC 2023

作者： Ogunwole, Emmanuel Idowu Krishnamurthy, Senthil Center for Substation Automation and Energy Management Systems Cape Peninsula University of Technology Department of Electrical Electronic and Computer Engineering PO BOX 1906 Symphonyway Cape Town Bellville7535 South Africa

Renewable energy resources (RES) are gaining popularity worldwide. As a result, renewable energy sources (RES) are now considered an alternative source of electric power generation in many communities. Furthermore, using renewable energy sources reduces pollution caused by coal and the release of carbon into the atmosphere by power plants. This article focuses on the economic feasibility of renewable energy resources since it is one of the obstacles in building and executing effective and efficient power generation with RES. An optimizer function of HOMER Pro software was employed in this study to develop a cost-effective power-generating RES configuration. The system uses an IEEE 14-Bus load demand of 259 MW supplied by five generators tested the design system in an IEEE 14-Bus test system with a total load demand of 259 MW supplied by five generators. The loads were classified as critical or non-critical. Three case studies were considered: 1. a case in which all five generators supplied the loads, 2. a case in which one generator was replaced by solar PV with the same generating capacity, and 3. a case in which two generators were replaced by both solar PV and wind turbine respectively. The decrease in net present cost (NPC), Levelized cost (COE), and operating cost (OC) demonstrate the efficacy of the proposed system topologies with various forms of RES. © 2023 IEEE.

关键词： Renewable energy resources

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Decoding the Biomimetic Mineralization of Metal-Organic Frameworks in Water

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ACS Nano 2024年第36期18卷 25170-25182页

作者： Lin, Shang-Wei Lam, Phuc Khanh Wu, Chin-Teng Su, Kuan-Hsuan Sung, Chi-Fang Huang, Sen-Ruo Chang, Je-Wei Shih, Orion Yeh, Yi-Qi Vo, Trung Hieu Tsao, Heng-Kwong Hsieh, Haw-Ting Jeng, U-Ser Shieh, Fa-Kuen Yang, Hsiao-Ching Department of Chemistry Fu Jen Catholic University New Taipei City24205 Taiwan Department of Chemistry National Central University Taoyuan32001 Taiwan National Synchrotron Radiation Research Center Hsinchu Science Park Hsinchu300092 Taiwan Department of Chemical and Materials Engineering National Central University Taoyuan32001 Taiwan Department of Electrical Engineering and Computer Sciences University of California at Berkeley CA94720 United States Department of Chemical Engineering College of Semiconductor Research National Tsing Hua University Hsinchu300044 Taiwan

This study unveils the "green" metal-organic framework (MOF) structuring mechanism by decoding proton transfer in water during ZIF-8 synthesis. Combining in situ small- to wide-angle X-ray scattering, multiscale simulations, and quantum calculations, we reveal that the ZIF-8 early-stage nucleation and crystallization process in aqueous solution unfolds in three distinct stages. In stage I, imidazole ligands replace water in zinc-water cages, triggering an "acidity flip" that promotes proton transfer. This leads to the assembly of structures from single zinc ions to 3D amorphous cluster nuclei. In stage II, amorphous nuclei undergo a critical transformation, evolving into crystalline nuclei and subsequently forming mesoscale-ordered structures and crystallites. The process proceeds until the amorphous precursors are completely consumed, with the transformation kinetics governed by an energy barrier that determines the rate-limiting step. In stage III, stable crystallite nanoparticles form in solution, characterized by a temperature-dependent thermal equilibrium of molecular interactions at the crystal-solution interface. Beyond these core advancements, we explore the influence of encapsulated pepsin and nonencapsulated lysozyme on ZIF-8 formation, finding that their amino acid proton transfer capacity and concentration influence the resulting biomolecule-MOF composite’s shape and encapsulation efficiency. The findings contribute to understanding the molecular mechanisms behind biomimetic mineralization and have potential implications for engineering proteins within amorphous MOF nuclei as protein embryo growth sites. © 2024 American Chemical Society.

关键词： X ray scattering

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A Novel Approach to Transformer Selection Based on Its Power Losses and Load Profile: Study Cases

A Novel Approach to Transformer Selection Based on Its Power...

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2024 International Conference on Materials and Energy: Energy in electrical engineering, ICOME-EE 2024

作者： Jiravoradej, Nitirak Wiwatcharagoses, Nophadon Liutanakul, Pisit Faculty of Engineering Department of Electrical and Computer Engineering Thailand KMUTNB Techno Park King Mongkut's University of Technology North Bangkok Operations Center of Integrated Innovation Research and Development under Industrial Standards Bangkok Thailand

ISBN: (纸本)9798331531577

The efficiency and energy loss of electrical devices are crucial aspects in engineering work. Transformers, being integral components of electrical systems, incur power losses continuously, both when supplying loads and when idle. Selecting a high-efficiency transformer reduces energy losses but increases the initial investment cost. This research study has investigated the selection of transformers to reduce energy losses for various electricity usage patterns by Load Profile data from the Provincial Electricity Authority (PEA) across five cases with load factors ranging from 26.86% to 74.37%. The data were scaled down and used to size transformers according to the electrical capacity of each case study. The model of transformers used in the study were determined by considering the power losses and transformer sizes according to EN 50464-1: 2007 and EN 50588-1: 2017 standards. Five types of power losses and eleven sizes of transformers were considered. The study results show that when considering the energy cost of transformer losses compared to the T1 transformer (DoDk), in case study 3, the T5 transformer (AAAoAk) had a reduction in energy loss by 46.08% with a break-even point in the third year. When considering transformer sizes, in case study 1, the T6 transformer (DoDk) had a reduction in energy loss by 14.21% with a break-even point in the third year. However, in the case of a very low load factor (case 5), the T7 and T8 transformers (DoDk) had an increase in energy loss by 4.54% and 15.07%, respectively, with no break-even point determined. © 2024 IEEE.

关键词： Distribution transformers

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Decisioning-Based Approach for Optimising Control engineering Tools Using Digital Twin Capabilities and Other Cyber-Physical Metaverse Manufacturing System Components

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 3212-3221页

作者： Mourad, Nahia Alsattar, Hassan A. Qahtan, Sarah Zaidan, Aws Alaa Deveci, Muhammet Sangaiah, Arun Kumar Pedrycz, Witold British University in Dubai Faculty of Engineering and IT Dubai United Arab Emirates University of Mashreq College of Administrative Science Department of Business Administration Baghdad10067 Iraq Middle Technical University College of Health and Medical Technology-Baghdad Department of Computer Center Baghdad10047 Iraq SP Jain School of Global Management SydneyNSW2141 Australia National Defence University Turkish Naval Academy Department of Industrial Engineering Istanbul34040 Turkey Lebanese American University Department of Electrical and Computer Engineering Byblos Lebanon International Graduate Institute of AI National Yunlin University of Science and Technology Yunlin64002 Taiwan University of Alberta Faculty of Engineering Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Istanbul34010 Turkey

The optimisation of control engineering tools based on digital twin capabilities and other cyber-physical metaverse manufacturing system (CPMMS) components are crucial for the successful performance. This study proposes a model for optimising control engineering tools using digital twin capabilities and other CPMMS components to solve the open issues. The main contributions and novelty aspects of the methodological process are outlined as follows: Formulated and developed is a decision matrix based on a utility procedure for 10 control engineering tools with digital twin capabilities and other three CPMMS components (Programmable-Logic-Controller and Human-Machine-Interface, Internet of Things connectivity and cybersecurity features). This matrix accounts for the uncertainty associated with tool assessment and transformation evaluation issue;formulated and develop an integrating fuzzy weighted with zero-inconsistency-interval-valued spherical fuzzy rough sets (IvSFRS-FWZIC) and combined compromise solution (CoCoSo) methods. The IvSFRS-FWZIC method is utilised to assign importance degrees to the digital twin capabilities and other CPMMS components. The applicability and robustness of the proposed approach are validated and evaluated through conducting sensitivity, correlation, and comparative analyses. The proposed approach can assist managers in analysing and selecting the most suitable tool for developing CPMMS. © 1975-2011 IEEE.

关键词： Internet of things

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Unsupervised Unlearning of Concept Drift with Autoencoders

Unsupervised Unlearning of Concept Drift with Autoencoders

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Artelt, Andre Malialis, Kleanthis Panayiotou, Christos G. Polycarpou, Marios M. Hammer, Barbara Bielefeld University Faculty of Technology Bielefeld Germany Kios Research and Innovation Center of Excellence Cyprus University of Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus

ISBN: (纸本)9781665430654

Concept drift refers to a change in the data distribution affecting the data stream of future samples. Consequently, learning models operating on the data stream might become obsolete, and need costly and difficult adjustments such as retraining or adaptation. Existing methods usually implement a local concept drift adaptation scheme, where either incremental learning of the models is used, or the models are completely retrained when a drift detection mechanism triggers an alarm. This paper proposes an alternative approach in which an unsupervised and model-agnostic concept drift adaptation method at the global level is introduced, based on autoencoders. Specifically, the proposed method aims to 'unlearn' the concept drift without having to retrain or adapt any of the learning models operating on the data. An extensive experimental evaluation is conducted in two application domains. We consider a realistic water distribution network with more than 30 models in-place, from which we create 200 simulated data sets / scenarios. We further consider an image-related task to demonstrate the effectiveness of our method. © 2023 IEEE.

关键词： autoencoders concept drift adaptation data streams nonstationary environments

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Towards Atomic Defect-Aware Physical Design of Silicon Dangling Bond Logic on the H -Si (100)-2× 1 Surface

Towards Atomic Defect-Aware Physical Design of Silicon Dangl...

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2024 Design, automation and Test in Europe Conference and Exhibition, DATE 2024

作者： Walter, Marcel Croshaw, Jeremiah Hang Ng, Samuel Sze Walus, Konrad Wolkow, Robert Wille, Robert Technical University Of Munich Design Automation Germany University Of Alberta Department Of Physics Edmonton Canada Quantum Silicon Inc. Edmonton Canada University Of British Columbia Department Of Electrical And Computer Engineering Vancouver Canada Software Competence Center Hagenberg GmbH Austria

ISBN: (纸本)9798350348590

Recent advancements in Silicon Dangling Bond (SiDB) fabrication have transitioned from manual to automated processes. However, sub-nanometer substrate defects remain a significant challenge, thus preventing the fabrication of functional logic. Current design automation techniques lack defect-aware strategies. This paper introduces an idea for a surface defect model based on experimentally verified defects, which can be applied to enhance the robustness of established gate libraries. Additionally, a prototypical automatic placement and routing algorithm is presented, utilizing STM data from physical experiments to obtain dot- accurate circuitry resilient to atomic surface defects. Initial evaluations on surfaces with varying defect rates demonstrate their critical impact, suggesting that fabrication processes must achieve defect rates of around 0.1 % to further advance this circuit technology. © 2024 EDAA.

关键词： Surface defects

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Enhanced Atrial Fibrillation (AF) Detection via Data Augmentation with Diffusion Model 13

Enhanced Atrial Fibrillation (AF) Detection via Data Augment...

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13th International Conference on computer and Knowledge engineering, ICCKE 2023

作者： Vashagh, Arash Akhoondkazemi, Amirhossein Zahabi, Sayed Jalal Shafie, Davood Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan841568311 Iran Isfahan University of Medical Sciences Heart Failure Research Center Cardiovascular Research Institute Isfahan*** Iran

ISBN: (纸本)9798350330151

Atrial Fibrillation (AF) is the most common type of cardiac arrhythmia, posing significant health risks such as strokes and cardiac failure. Deep learning methods have been employed to automatically diagnose AF from electrocardiogram (ECG) signals. In this study, transforming the ECG signal into two-dimensional Poincaré plots, we explore the application of diffusion model in the augmentation of AF samples to improve the performance of AF detection via deep learning. In this regard, we compare the performance of diffusion model and generative adversarial network (GAN). Our results suggest that diffusion model can potentially generate more satisfactory samples in terms of both the FID score and the downstream classification task. © 2023 IEEE.

关键词： Electrocardiograms

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Atrial Fibrillation (AF) Detection Using Deep Learning with GAN-based Data Augmentation 31

Atrial Fibrillation (AF) Detection Using Deep Learning with ...

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31st International Conference on electrical engineering, ICEE 2023

作者： Akhoondkazemi, Amirhossein Vashagh, Arash Zahabi, Sayed Jalal Shafie, Davood Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan841568311 Iran Cardiovascular Research Institute Isfahan University of Medical Sciences Heart Failure Research Center Isfahan*** Iran

ISBN: (纸本)9798350312560

Atrial Fibrillation (AF) is the most common cardiac arrhythmia that may lead to stroke and heart failure and because of this, a lot of research has gone into detecting AF from the electrocardiogram (ECG) signal. In this paper, we propose an AF detection pipeline, which first transforms the ECG data into an informative 2-D image by the aid Poincaré recurrence plot, and then addresses the imbalance of the data by augmenting AF samples using a form of generative adversarial network (GAN). The augmented dataset is then used within the training set to train a 5-layer convolutional neural network (CNN) as a classifier. The performance of the proposed classifier is finally evaluated based on a 4-fold cross-validation scheme. The performance metrics suggest that the proposed method provides acceptable sensitivity and specificity. © 2023 IEEE.

关键词： Electrocardiograms

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Coverage analysis and handover strategy in RF/VLC heterogeneous networks

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Applied Optics 2025年第15期64卷 4160-4173页

作者： Feng, Renhai Guo, Yatong Yousaf, Muhammad Zain Khan, Baseem Luo, Liping School of Electrical and Information Engineering Tianjin University Tianjin300072 China School of Physics and Electronic Engineering Xinjiang Normal University Ürümqi830054 China Center for Renewable Energy and Microgrids Huanjiang Laboratory Zhejiang University Zhejiang Zhuji311816 China School of Electrical and Information Engineering Hubei University of Automotive Technology Shiyan China School of Aeronautics and Astronautics Zhejiang University Hangzhou310027 China Department of Electrical and Computer Engineering Hawassa University Hawassa Ethiopia School of Electronics and Information Guangxi Minzu University Nanning530006 China Guangxi Colleges Universities Engineering Research Center for Multi-modal Information Intelligent Sensing Processing and Application Nanning530006 China

Coverage analysis in heterogeneous networks (HetNets) remains insufficient, especially under user mobility and diverse association strategies. To address this, this paper proposes a contact probability and coverage approximation based on association strategy (CPCA-AS) to analyze the coverage probability (CPr) in visible light communication (VLC) and radio frequency (RF) HetNets. Based on the proposed stochastic geometry framework, CPCA-AS evaluates coverage performance under various configurations, including RF-only, VLC-only, and hybrid RF/VLC scenarios. The model incorporates a contact probability mechanism and considers two strategies: opportunistic VLC and maximum received signal power (MRSP). For VLC networks, CPCA-AS utilizes the Gil-Pelaez inversion theorem, and Laplace transforms to efficiently approximate CPr. For RF networks, Markov and Gamma inequalities form conservative accurate approximations and lower bounds. CPCA-AS effectively captures the influence of the photodetector’s field-of-view (FoV) on interference and association behavior. Monte Carlo simulations validate the accuracy of the CPCA-AS. Results demonstrate that CPCA-AS improves CPr. The opportunistic VLC strategy performs better in high-interference and large FoV scenarios. In contrast, the MRSP strategy is more effective in low-interference and low-rate environments. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Markov processes

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