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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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Recent advances in breathable electronics

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Nano research 2023年第3期16卷 4130-4142页

作者： Zhuocheng Yan Jian Xiong Bin Wang Min Gao Guangqiang Yin Tao Hu Taisong Pan Xinzhong Wang Yuan Lin Department of Electronic Communication and Technology Shenzhen Institute of Information TechnologyShenzhen 518172China School of Materials and Energy University of Electronic Science and Technology of ChinaChengdu 610054China School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054China Engineering Cooperation on Applied Medicine Research Center University of Electronic Science and Technology of ChinaChengdu 610054China

The successful implementation of bioelectronic devices attached to living organism hinges on a number of material and device characteristics,including not only electrical and mechanical performances to gather physiological signals from living organism thus enabling status monitoring,but also permeability or breathability for gas/nutrient exchange between living organisms and surroundings to ensure minimum perturbation of the intrinsic biological ***,most bioelectronic devices built on planar polymeric substrates,such as polydimethylsiloxane(PDMS),polyurethane(PU),and polyimide(PI),lack efficient gas permeability,which may hinder the emission of volatile compounds from the surface of living organism,affecting the natural metabolism and reducing the comfort of ***,achieving permeability or breathability in bioelectronic devices is a significant ***,the devices made of gas-permeable materials with porous structures,that combine electronic components with daily garments,such as fibric and textile,offer exciting opportunities for breathable *** this review,several types of gas-permeable materials with their synthesis and processing routes are ***,two methods for measuring water vapor transmission rate of materials are discussed in ***,recent progress in the use of gaspermeable materials for the applications of plant-and skin-attached electronics is summarized systematically.

关键词： breathability gas permeability porous sponge foam nano materials plant electronics wearable electronics

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Opposition-Based White Shark Optimizer for Optimizing Modified EfficientNetV2 in Road Crack Classification

IEEE Open Journal of the Computer Society

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IEEE Open Journal of the Computer Society 2025年第1期6卷 762-775页

作者： Al-Shalabi, Mohammed Mahdi, Mohammed A. Braik, Malik Al-Betar, Mohammed Azmi Ahamad, Shahanawaj Saad, Sawsan A. University of Ha'il Department of Information and Computer Science Ha'il Saudi Arabia Al-Balqa Applied University Department of Computer Science Jordan Ajman United Arab Emirates Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman11942 Jordan University of Ha'il Department of Software Engineering Ha'il Saudi Arabia University of Ha'il Department of Computer Engineering Ha'il Saudi Arabia

Maintaining reliable and long-lasting road infrastructure requires accurate identification and management of pavement cracks, as these cracks can significantly weaken asphalt and concrete surfaces over time. Although Convolutional Neural Networks (CNNs) and meta-heuristic algorithms have proven effective in solving real-world problems, their use in low-contrast pavement crack images is worth investigating. This study proposes an automated crack detection framework that integrates three key components: (1) a new variant of a pre-trained CNN architecture, referred to as Modified EfficientNetV2 (MEfficientNetV2) for pavement crack classification;(2) a combination of opposition-based learning with White Shark Optimizer (WSO), known as Opposition WSO (OWSO), to improve the balance between exploration and exploitation;and (3) Principal Component Analysis (PCA) for efficient dimensionality reduction and feature selection. This method is validated on various publicly available asphalt crack datasets that contain low-contrast natural images. Preprocessing techniques are first applied to eliminate noise and enhance image quality. The OWSO algorithm is then integrated to optimize the classification performance of MEfficientNetV2, while PCA accelerates the learning process by retaining critical features in the thresholds of the varying components. Comparative evaluations with state-of-the-art methods demonstrate that the proposed model excels in terms of precision, robustness, and generalizability. The outcome emphasizes its ability to identify the most effective solution for crack detection in practical scenarios, where PCA-based feature selection improves computational efficiency without compromising performance. This study focuses on the potential of hybrid deep learning and bio-inspired optimization strategies to improve automated pavement maintenance systems. © 2020 IEEE.

关键词： Convolutional neural networks

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Haptic-Aided Navigation Vehicle: Enhancing Obstacle Detection in Blind Spots and Transparent Object Scenarios

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Journal of Shanghai Jiaotong University (Science) 2025年 1-9页

作者： Li, Mingwang Li, Xinde Zhang, Zhentong Zhang, Zeyu Zhao, Haoming School of Software Southeast University Suzhou Jiangsu215000 China School of Automation Southeast University Nanjing210018 China School of Cyber Science and Engineering Southeast University Nanjing211102 China Key Laboratory of Measurement and Control of Complex Systems of Engineering of Ministry of Education Nanjing214135 China Nanjing Center for Applied Mathematics Nanjing211135 China Southeast University Shenzhen Research Institute Shenzhen Guangdong518063 China

As autonomous mobile robots are increasingly deployed in complex environments, traditional vision sensors and LiDAR encounter considerable limitations, particularly in detecting obstacles in blind spots or transparent objects. To address the issue of blind spots, we design a specialized haptic sensing structure and develop the haptic-aided navigation vehicle (HANV). This system integrates haptic sensors and LiDAR to deliver comprehensive perception, significantly enhancing close-range obstacle detection in areas that are typically beyond the range of conventional sensors. To tackle the challenge of transparent obstacles, which are often undetected by both vision and LiDAR sensors, we employ a fusion of haptic sensors and LiDAR. The haptic system provides physical contact feedback, ensuring reliable detection of transparent obstacles such as glass, while LiDAR offers long-range sensing capabilities. This combination enables HANV to navigate effectively in environments with transparent obstacles, overcoming the limitations of traditional sensing systems. Experiment results indicate that the proposed haptic and LiDAR integration substantially improves obstacle detection in both blind spots and environments with transparent obstacles. HANV achieves high success rates, minimal collisions, and efficient obstacle avoidance, particularly excelling in complex, confined spaces where conventional systems prove inadequate. These findings emphasize the efficacy of our approach in enhancing navigation performance in dynamic and challenging environments. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： Mobile robots

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Class and Attribute-Aware Logit Adjustment for Generalized Long-Tail Learning 39

Class and Attribute-Aware Logit Adjustment for Generalized L...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhou, Xiaoling Wu, Ou Yang, Nan Center for Applied Mathematics Tianjin University China National Engineering Research Center for Software Engineering Peking University China HIAS University of Chinese Academy of Sciences Hangzhou China

ISBN: (纸本)157735897X

Compared to conventional long-tail learning, which focuses on addressing class-wise imbalances, generalized long-tail (GLT) learning considers that samples within each class still conform to long-tailed distributions due to varying attributes, known as attribute imbalance. In the presence of such imbalance, the assumption of equivalence between the class-conditional probability densities of the training and testing sets is no longer tenable. Existing GLT approaches typically employ regularization techniques to avoid directly modeling the class-conditional probability density (CCPD) ratio between training and test data, leading to suboptimal performance. This study aims to directly estimate this ratio, for which a novel class-attribute aware logit-adjusted (CALA) loss incorporating both the CCPD ratio and the class priors is presented. Two new GLT learning methods, named Heuristic-CALA and Meta-CALA, are then proposed, which estimate the CCPD ratio in the CALA loss by leveraging the neighborhood information of samples. Extensive experiments across diverse scenarios susceptible to class and attribute imbalances showcase the state-of-the-art performance of Meta-CALA. Furthermore, while Heuristic-CALA exhibits inferior performance compared to Meta-CALA, it incurs only negligible additional training time compared to the Cross-Entropy loss, yet surpasses existing methods by a significant margin. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Size distribution

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Detection and Replay of Distributed Denial of Service Attacks in Smart Cities Using a Hybrid Deep Learning Approach 2

Detection and Replay of Distributed Denial of Service Attack...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Iqbal, Muhammad Waseem Issa, Ghassan F. Yousif, Muhammad Atif, Muhammad Superior University Department of Software Engineering Lahore Pakistan Skyline University College School of Information Technology Sharjah United Arab Emirates Department of Computer Science NCBAE Lahore Pakistan Applied Science Research Center Applied Science Private University Amman11937 Jordan

ISBN: (纸本)9798350335644

Infrastructure for smart cities is presently being built, mostly because of the (IoT) platform, which connects a wide variety of things by using the website's underlying infrastructure. As a result, it is possible to employ a uniform platform to automate the services that are delivered. Smart city infrastructure, on the other hand, is susceptible to cyberattacks owing to security flaws in IoT networks. Distributing denial of service (DDoS) and replay assaults, for example, violate the requirements for certification in smart cities. The lack of citations in this part backs up many of the statements it makes. Who needs a citation? Both assaults have the potential to substantially damage smart city infrastructure, which might also cost money and even result in human fatalities. This paper covers the creation of a blended deep- learning algorithm for replay and DDoS intrusion prevention on a growth-oriented smart city platform. This is made possible by combining proven machine-learning approaches with more recent advances in artificial intelligence. We simulate scattered denial of service and replay attacks on three datasets acquired from real-world smart cities to assess the efficacy of the proposed hybrid strategy on environmental, smart river, and smart soil dataset. The suggested model has shown excellent rates of accuracy. The ecosystem collection has an accuracy level of 98.37%, the smart riverbed dataset must have an accuracy of 98.13%, and the smart mud dataset must have an accuracy of 99.51%. The findings demonstrated that the proposed model outperformed past instances of evolutionary computation and machine learning techniques employed in the corpus of academic literature. © 2023 IEEE.

关键词： Internet of things

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Internet of Things (IoT) in Smart Cities: A Statistical Survey 2

Internet of Things (IoT) in Smart Cities: A Statistical Surv...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Iqbal, Muhammad Waseem Khaliq, Khowla Al-Dmour, Nidal A. Aqeel, Muhammad Ali, Nasir Hamid, K. Superior University Department of Software Engineering Lahore54000 Pakistan Superior University Department of Computer Sciecnce Lahore54000 Pakistan Mutah University College of Engineering Department of Computer Engineering Jordan NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan

ISBN: (纸本)9798350335644

Internet of things is progressing very rapidly and involving multiple domains of everyday life including environment, governance, healthcare system, transportation system, energy management system, etc. smart city is a platform for collecting and storing the information that is accessed through various sensor-based IoT devices and make their information available in required and authorized domains. This interoperability can be achieved by semantic web technology. In this paper, I have reviewed multiple papers related to IoT in Smart Cities and presented a comparison among the semantic parameters. Moreover, I've presented my future domain of research which is about delivering the COVID-19 patients report to the concerned domains by the healthcare system domain. © 2023 IEEE.

关键词： Internet of things

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Application of a p-version curved C^(1)finite element based on the nonlocal Kirchhoff plate theory to the vibration analysis of irregularly shaped nanoplates

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Science China(Technological Sciences) 2023年第10期66卷 3025-3047页

作者： XIANG Wei NI Hua TIAN YiFeng WU Yang LIU Bo School of Mechanical Engineering Southwest Jiaotong UniversityChengdu 610031China Technology and Equipment of Rail Transit Operation and Maintenance Key Laboratory of Sichuan Province Chengdu 610031China Southwest China Research Institute of Electronic Equipment Chengdu 610036China CAEP Software Center for High Performance Numerical Simulation Beijing 100088China Institute of Applied Physics and Computational Mathematics Beijing 100088China The Solid Mechanics Research Centre Beihang University(BUAA)Beijing 100191China

Nanoplates have been widely used as elementary components for ultrasensitive and ultrafine resolution applications in the field of nano-electro-mechanical systems because of their potentially remarkable mechanical *** accurate analysis of their mechanical behavior is currently of particular interest in the function design and reliability analysis of nano-scaled *** examine the size-dependent bending and vibration behavior of nanoplates with curvilinear and irregular shapes,a new p-version curved C^(1)finite element is formulated in the framework of the nonlocal Kirchhoff plate *** newly developed element not only enables an accurate geometry representation and easy mesh generation of curvilinear domains but also overcomes the difficulty of imposing C^(1)conformity required by the nonlocal Kirchhoff plate model,particularly on the curvilinear inter-element *** examples show that this element can produce an exponential rate of convergence even when curved elements are used in the domain *** numerical results are presented for nanoplates with various geometric shapes,including rectangular,circular,elliptic,annular,and *** high accuracy of the present element is verified by comparing the obtained results with analytical and numerical results in the ***,a comprehensive parametric analysis is conducted to investigate the influences of nonlocal parameters,plate dimensions,and boundary conditions on the nonlocal behavior of *** present element can be envisaged to allow large-scale mechanical simulations of nanoplates,with a guarantee of accuracy and efficiency.

关键词： nanoplates nonlocal theory p-version finite element method C^(1)conformity irregular shape

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Optimizing beyond boundaries: empowering the salp swarm algorithm for global optimization and defective software module classification

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Neural Computing and Applications 2024年第30期36卷 18727-18759页

作者： Kassaymeh, Sofian Al-Betar, Mohammed Azmi Rjoubd, Gaith Fraihat, Salam Abdullah, Salwani Almasri, Ammar Software Engineering Department Faculty of Information Technology Aqaba University of Technology Aqaba Jordan College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Department of Information Technology Al-Huson University College Al-Balqa Applied University Irbid Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Artificial Intelligence Department Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Data Mining and Optimization Research Group Center for Artificial Intelligence Technology Universiti Kebangsaan Malaysia Bangi Malaysia Management Information Systems Department Al-Balqa Applied University Amman Jordan

This work presents a new version of the salp swarm optimizer (SSA), called "mSSA," that uses complex mathematical expressions to dynamically manipulate the crucial control parameter (c1) during optimization. These expressions are carefully designed to modulate the shift in search strategy from exploratory to exploitative, improving the flexibility and speed of convergence of the algorithm. To evaluate the performance of the developed mSSA variants, a thorough examination is carried out on twenty-three benchmark test functions alongside their application to the complex task of software module classification. The process of classifying defective software modules involves developing a multilayer perceptron (MLP) classifier that is suited to the particular complexity and heterogeneity of the task. Selecting the best optimizer is made easier by systematically evaluating the different mSSA versions as MLP classifier trainers. Based on metrics like classification accuracy, convergence speed, and avoidance of local minima, a comparative analysis in opposition to six previously published metaheuristic optimizers shows that mSSA3, when combined with the developed MLP classifier, outperforms both other mSSA variations and state-of-the-art metaheuristic optimizers in terms of overall performance. The excellent classification accuracy, swift convergence, and ability to avoid local minima of mSSA3 highlight its superiority and establish it as a cutting-edge method in the application of metaheuristic algorithms. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Global optimization

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Empowering Donors: How Blockchain Technology Can Help Ensure Their Contributions Reach the Right Recipients 2

Empowering Donors: How Blockchain Technology Can Help Ensure...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Ajmal, Sahar Muzammil, Muteeb Bin Shoaib, Muhammad Mehmood, Muhammad Hassaan National University of Computer and Emmerging Sciences Department of Software Engineering Faisalabad Pakistan Riphah International University College of Computing Faisalabad Pakistan University of Engineering and Technology Department of Computer Science Lahore Pakistan School of Computer Science NCBA&E Lahore Pakistan Applied School Research Center Applied Science Private University Amman11937 Jordan

ISBN: (纸本)9798350335644

Charity giving is a fundamental aspect of society, but there are concerns about the accountability and transparency of the donation process. Blockchain technology has emerged as a potential solution, offering a secure and auditable platform for managing charitable contributions. In this paper, we explore the concept of empowering donors through blockchain technology and examine how it can help ensure that contributions reach the right recipients. We review existing literature on the topic and identify key areas for future research, including the development of hybrid blockchain solutions along with the implementation of novel consensus mechanisms. Moreover, providing the idea of decentralized autonomous organizations, self-executing smart contracts, and integration of blockchain with artificial intelligence and Internet of things. By providing a comprehensive overview of the current state of the field and outlining a roadmap for future research, we hope to stimulate further exploration of the potential of blockchain technology to revolutionize charity giving, and create a more accountable and transparent system for managing charitable funds. Some research questions are designed for each novel idea. Our analysis suggests that blockchain has the potential to empower donors and ensure that their contributions make a positive impact in the world, but further research is needed to fully realize its potential. © 2023 IEEE.

关键词： Transparency

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