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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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Security Enhancement of Patient Data in the Internet of Healthcare Systems (IoHT)-A Blockchain Approach 1st

Security Enhancement of Patient Data in the Internet of Heal...

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1st International Conference on Computing and Emerging Technologies, ICCET 2023

作者： Ali, Muhammad Masood, Tehreem Naz, Ammerha Department of Software Engineering The Superior University Lahore Punjab Pakistan Department of Computer Science University of Sialkot Punjab Pakistan

ISBN: (纸本)9783031776199

The department of the Internet of Things is developing very rapidly. We interact with its other fields in our daily life in one way or another way like smart vehicle systems, smart homes, smart medical systems, and more. Here our concern is a smart medical system that stores its data in cloud storage and this kind of storage mechanism causes many critical security vulnerabilities like unauthorized access to private data, data breaches, information theft, and more. So to avoid that kind of security mishap, we proposed a blockchain-based framework that stores information on a distribution instead of a central repository and makes information immutable. So every time a transaction takes place over a network, it is first authenticated. In this way, transactions take place on the network without any miscellaneous data or security threats. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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Energy consumption prediction using modified deep CNN-Bi LSTM with attention mechanism

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Heliyon 2025年第1期11卷 e41507页

作者： Binbusayyis, Adel Sha, Mohemmed Department of Software Engineering College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Saudi Arabia

The prediction of energy consumption in households is essential due to the reliance on electrical appliances for daily activities. Accurate assessment of energy demand is crucial for effective energy generation, preventing overloads and optimizing energy storage. Traditional techniques have limitations in accuracy and error rates, necessitating advancements in prediction techniques. To enhance prediction accuracy, a proposed smart city system utilizes the Household Energy Consumption dataset, employing deep learning algorithms. In the beginning, data pre-processing addresses missing values and performs feature scaling for normalizing independent variables. Followed by that, Modified Deep CNN-Bi-LSTM (Convolutional Neural Network and Bi-directional Long Short Term Memory) with attention mechanism is utilized for regression which extracts temporal and spatial complex features. Deep CNN extracts features impacting energy consumption whereas Bi-LSTM with attention layer finds suitability for regression as it is capable of modelling irregular trends in the time-series components, where the attention mechanism is implemented to enhance the decoder's ability to selectively focus on the most relevant segments of the input sequence. This is achieved through a weighted integration of all encoded input trajectories, allowing the model to dynamically emphasize the vectors that carry the highest significance for accurate predictions. Based on regression outcomes from analysis taken in hourly, daily and monthly time intervals, enhanced prediction accuracy is estimated through evaluation metrics such as MSE (Mean Square Error), MAPE (Mean Absolute Percentage Error) and RMSE (Root Mean Square Error) which determines the efficacy of the system, where Specifically, the proposed model achieves MSE of 0.123, MAE of 0.22, and MAPE of 324.12. Furthermore, this model demonstrates a training time of 692.12 s and a prediction time of just 1.87 s. Therefore, present research highlights the c

关键词： Bi directional long short term memory Convolutional neural network Deep learning Energy consumption and household energy dataset

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Runtime Enforcement of CPS against Signal Temporal Logic

arXiv

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arXiv 2025年

作者： Su, Han Shankar, Saumya Pinisetty, Srinivas Roop, Partha S. Zhan, Naijun Institute of Software CAS & University of CAS Beijing China Department of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand Indian Institute of Technology School of Electrical Sciences Bhubaneswar India The University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand Peking University School of Computer Science Institute of Software CAS Beijing China

Cyber-Physical Systems (CPSs), especially those involving autonomy, need guarantees of their safety. Runtime Enforcement (RE) is a lightweight method to formally ensure that some specified properties are satisfied over the executions of the system. Hence, there is recent interest in the RE of CPS. However, existing methods are not designed to tackle specifications suitable for the hybrid dynamics of CPS. With this in mind, we develop runtime enforcement of CPS using properties defined in Signal Temporal Logic (STL). In this work, we aim to construct a runtime enforcer for a given STL formula to minimally modify a signal to satisfy the formula. To achieve this, the STL formula to be enforced is first translated into a timed transducer, while the signal to be corrected is encoded as timed words. We provide timed transducers for the temporal operators until and release noting that other temporal operators can be expressed using these two. Our approach enables effective enforcement of STL properties for CPS. A case study is provided to illustrate the approach and generate empirical evidence of its suitability for CPS. © 2025, CC BY-NC-ND.

关键词： Transducers

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Academic Fraud Detection in Online Exams with DNN's Multilayer Model

Academic Fraud Detection in Online Exams with DNN's Multilay...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Bahaddin Erdem Murat Karabatak Department of Computer Programming Bitlis Eren University Bitlis Turkey Department of Software Engineering Firat University Elaziğ Turkey

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

This study aims to reveal the best deep learning models that are improved and optimized by predicting undesirable behavior patterns using a dataset consisting of artificial and real exam data of students taking online distance education courses in an online environment through the distance education system. Using online exam data of 129 students, the researchers conducted analysis with two different scenarios to determine the best prediction performance through regression and classification models. The model we proposed was determined as a four-layer DNN with 80.4% test performance in detecting students who “cheated” from undesirable behavior patterns, which was performed with K-10, K-5 and K-3 cross-validation. The results prove that students' online distance education exam data can be easily applied to the DNN model. The models presented in the study provide a roadmap for educational institutions to evaluate their online examination practices and develop more effective strategies for academic honesty.

关键词： Deep learning Analytical models Digital forensics Predictive models Nonhomogeneous media Data models Educational courses Fraud Security

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Optimization of Decentralized Caching with Improved Coded Delivery Under Heterogeneous File Popularity

Optimization of Decentralized Caching with Improved Coded De...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Siyu Zhang Jiasheng Liang Yong Deng Department of Software Engineering Lakehead University Ontario Canada Department of Computer Science Binghamton University New York USA

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

This paper considers the design and optimization of decentralized coded caching under heterogeneous file popularity. We propose a decentralized nested coded caching scheme (D-NCCS) that implements an improved nested coded delivery strategy to avoid using zero-padding, a common sub-optimal approach for simplifying the coded delivery under heterogeneous file popularity. We formulate a joint optimization problem to jointly optimize the cache placement and nested coded delivery strategies of the D-NCCS, and develop a successive approximation algorithm to solve the problem. Numerical results demonstrate the close to optimal performance of the optimized D-NCCS.

关键词： Approximation algorithms Optimization

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Development and Evaluation of Smartphones Interface for Elderly 1st

Development and Evaluation of Smartphones Interface for Elde...

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1st International Conference on Computing and Emerging Technologies, ICCET 2023

作者： Bhatti, Anam Maqsood Iqbal, Muhammad Waseem Siddique, Imran Department of Computer Science Superior University Lahore54000 Pakistan Department of Software Engineering Superior University Lahore Pakistan

ISBN: (纸本)9783031776199

It is vital to first comprehend the various wants and skills of seniors since the existing user interface is not flexible enough to meet their needs. Because seniors will no longer be a minority in the next few years, this research underscores the necessity of supporting elders by reworking the smartphone interface to improve the user experience for older users. The main goal of this research project is to provide a user-friendly smartphone interface for seniors. To do this, it is vital to learn how elderly people perceive and interact with the modern smartphone interfaces, as well as what challenges they confront. In the second stage, an elder-smartphone interface (ESI) design is created based on the preferences of the end-user, which are noted via a post-experimentation questionnaire. The Elderly-Smartphone Interface (ESI) design is assessed with actual end users during the third phase. The bulk of the evaluators give positive feedback while rating the prototype. The target group for this study is an active senior demographic that is capable of using cellphones on its own. The word "ancient" in this study refers to those who are sixty years of age or older. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Smartphones

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Assessing Customer Satisfaction and Loyalty in the Energy Sector Through Service Quality: A Modified SERVQUAL Analysis 24th

Assessing Customer Satisfaction and Loyalty in the Energy Se...

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24th International Symposium for Production Research, ISPR 2024

作者： Çiçeklidağ, Paşa Nebati, Emine Elif Gülen, Kemal Güven Zaim, Selim Computer Science and Software Engineer The University of Western Australia Perth Australia Department of Industrial Engineering Istanbul Sabahattin Zaim University Istanbul Turkey Department of Industrial Engineering Tekirdag Namık Kemal University Tekirdag Turkey Department of Management Ibn Haldun University Istanbul Turkey

ISBN: (纸本)9783031836107

This study examines the impact of service quality on customer satisfaction and loyalty in the energy sector using the SERVQUAL model. Key dimensions such as reliability, empathy, and physical features were found to significantly influence customer satisfaction. The research involved an online survey, with data analyzed using Structural Equation Modeling (SEM) to identify both direct and indirect effects of service quality dimensions on customer satisfaction and loyalty. Results show that improving reliability and empathy can notably enhance customer satisfaction, leading to stronger customer loyalty. However, the study acknowledges limitations, such as the generalizability of results due to the online nature of the survey and the study's focus on a specific geographical region. The use of verbal data may also introduce bias, which future research could address by applying more detailed data collection methods and examining sub-factors. The study concludes that energy companies should refine their service strategies to better meet customer expectations, suggesting further research in different regions and sectors. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Customer satisfaction

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An Efficient Privacy-Preserving Access Control Scheme for Cloud Computing Services

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IEEE Transactions on Consumer Electronics 2025年

作者： Xiong, Ling Wang, JunKai Yu, Linsheng Xiong, Neal Wu, Hanzhou Xihua university School of Computer and Software Engineering Chengdu610039 China Sul Ross State University School of Department of Computer Science and Mathematics AlpineTX79830 United States Shanghai University School of Computer and Software Shanghai China

To meet the future system requirements of Cloud Computing Services (CCSs) for large numbers of users, multiple services and high efficiency, authentication and access control technologies will evolve in a more secure and efficient direction. The integration of authentication and access control into a single module is critical to improving privacy and efficiency. However, current schemes may involve trade-offs and hard choices between privacy and efficiency. To overcome such limitations, our work proposes an efficient privacy-preserving scheme with integrated authentication and access control for CCSs environments. Unlike traditional authentication techniques, our scheme determines not only the legitimacy of the users identity but also the access permissions of remote users during the authentication process. Users will possess the capability to obtain entry to an array of cloud computing providers via utilization of a credential including the corresponding identity and permission. Moreover, the access permissions of different cloud service providers contained within a single credential are protected by a key-based Merkle tree technology to protect users privacy. That our proposed framework is impregnable within the context of the random oracle paradigm is indicated by the outcome of the security evaluation. Furthermore, the proposed framework exhibits superior computational and communicational proficiencies in contrast to relevant schemes. Therefore, this work effectively enhances the efficiency of services, which has important meaning for the use of the access control technology in CCSs environments. © 2025 IEEE.

关键词： Differential privacy

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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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