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Robust security risk estimation for android apps using nearest neighbor approach and hamming distance

Soft Computing 2025年第2期29卷 593-611页

作者： Deypir, Mahmood Zoughi, Toktam Faculty of Computer Engineering Shahid Sattari Aeronautical University of Science and Technology Tehran Iran Tehran Iran

Nowadays, Android-based devices such as smart phones, tablets, smart watches, and virtual reality headsets have found increasing use in our daily lives. Along with the development of various applications for these devices, new malicious apps are released by intruders, which are more difficult to identify and deal with because they exploit more sophisticated techniques. Although methods have been provided to calculate the security risk and identify malicious apps in Android operating system, but with the expansion of the level and depth of the threats, the need for more effective methods in this context is still required. In this paper, we have devised a new algorithm to calculate the security risk of Android apps, which can be used to identify malicious apps from benign ones. In this algorithm, to estimate the security risk of an unknown input app, its nearest neighbors to malicious apps and its nearest neighbors to normal apps are computed separately using Hamming distance. Then, the security risk of the input app can be computed using a simple formulation. After implementing this algorithm, its parameter for the number of neighbors using various real datasets is adjusted. The extensive experiments conducted on these data show the superiority of the proposed method over the previously proposed ones in terms of detection rate. Our additional experimentations show the robustness of the proposed algorithm in adversarial situations. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Android malware

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Adaptive Cooperative Quality Weight Spectrum Sensing for Mitigating Byzantine Attacks in Cognitive Radio

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IEEE Access 2025年 13卷 80930-80940页

作者： Srisomboon, Kanabadee Pipanmekaporn, Luepol Prayote, Akara Lee, Wilaiporn Division of Electrical Engineering Department of Electrical and Computer Engineering Faculty of Engineering Bangkok10800 Thailand King Mongkut’s University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Science Bangkok10800 Thailand

Spectrum sensing data falsification (SSDF) attack, i.e., Byzantine attack, is one of the critical threats of the cooperative spectrum sensing where the Byzantine attackers (BAs) forward incorrect local sensing results to mislead the fusion center on channel availability decisions. By using traditional voting rule, the cooperative spectrum sensing performance deteriorates significantly due to incorrect local sensing results. Then, reliability weight strategy becomes the popular solution to avoid incorrect sensing results from BAs and unreliable cognitive radio users (CRUs). However, it is very difficult to detect the attackers since they also occasionally provide correct sensing results to the fusion center for concealing the attack objective. Based on existing techniques, the BAs and CRUs may be assigned with low reliability weights or distinguished from the data fusion account. However, it is very difficult to detect the attackers since they also occasionally provide correct sensing results to the fusion center for concealing the attack objective. Then, existing techniques still suffer from BAs and negative impact of unreliable CRUs. In this paper, we propose the adaptive cooperative quality weight algorithm for mitigating the Byzantine attack issue by distinguishing the BAs and CRUs from the data fusion account while selecting only useful CRUs since the number of members in the account is also the important factor for cooperative spectrum sensing. In our proposed algorithm, we adopt a stable preference ordering towards ideal solution (SPOTIS) for determining the reliability of SUs which shows low computational complexity as compared to other reliability weight-based techniques. To achieve high sensing performance, our global decision threshold is adapted according to the reliability of reliable users. From the simulation results, our proposed algorithm significantly improves global detection probability and total error probability compared to the traditional votin

关键词： Sensors Reliability Barium Radio spectrum management Cooperative communication Cognitive radio Data integration Signal to noise ratio Heuristic algorithms Dynamic spectrum access

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Optimizing Image Retrieval in Cloud Servers with TN-AGW: A Secure and Efficient Approach

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Journal of The Institution of Engineers (India): Series B 2025年第2期106卷 459-473页

作者： Ponnuviji, N.P. Nirmala, G. Kokila, M. L. Sworna Priyadharshini, S. Indra Department of Computer Science and Engineering RMK College of Engineering and Technology Puduvoyal Thiruvallur India Department of Computer Science and Engineering RMD Engineering College Chennai India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nādu Kattankulathur Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

The increasing use of cloud-based image storage and retrieval systems has made ensuring security and efficiency crucial. The security enhancement of image retrieval and image archival in cloud computing has received considerable attention in transmitting data and ensuring data confidentiality among cloud servers and users. Various traditional image retrieval techniques regarding security have developed in recent years but they do not apply to large-scale environments. This paper introduces a new approach called Triple network-based adaptive grey wolf (TN-AGW) to address these challenges. The TN-AGW framework combines the adaptability of the Grey Wolf Optimization (GWO) algorithm with the resilience of Triple Network (TN) to enhance image retrieval in cloud servers while maintaining robust security measures. By using adaptive mechanisms, TN-AGW dynamically adjusts its parameters to improve the efficiency of image retrieval processes, reducing latency and utilization of resources. However, the image retrieval process is efficiently performed by a triple network and the parameters employed in the network are optimized by Adaptive Grey Wolf (AGW) optimization. Imputation of missing values, Min–Max normalization, and Z-score standardization processes are used to preprocess the images. The image extraction process is undertaken by a modified convolutional neural network (MCNN) approach. Moreover, input images are taken from datasets such as the Landsat 8 dataset and the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset is employed for image retrieval. Further, the performance such as accuracy, precision, recall, specificity, F1-score, and false alarm rate (FAR) is evaluated, the value of accuracy reaches 98.1%, the precision of 97.2%, recall of 96.1%, and specificity of 917.2% respectively. Also, the convergence speed is enhanced in this TN-AGW approach. Therefore, the proposed TN-AGW approach achieves greater efficiency in image retrieving than other existing

关键词： Extraction

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Optimizing Stand-Alone PV Systems:A Metaheuristic-Enhanced Fuzzy Approach for Adaptive MPPT

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China Communications 2025年第1期22卷 61-74页

作者： Tina Samavat Mostafa Nazari Lin Fuhong Lei Yang Department of Computer Science and Engineering University of NevadaRenoNVUSA Faculty of Mechanical Engineering Shahrood University of TechnologyShahroodIran School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China

This paper introduces a simple yet effective approach for developing fuzzy logic controllers(FLCs)to identify the maximum power point(MPP)and optimize the photovoltaic(PV)system to extract the maximum power in different environmental *** propose a robust FLC with low computational complexity by reducing the number of membership functions and *** optimize the performance of the FLC,metaheuristic algorithms are employed to determine the parameters of the *** evaluate the proposed FLC in various panel configurations under different environmental *** results indicate that the proposed FLC can easily adapt to various panel configurations and perform better than other benchmarks in terms of enhanced stability,responsiveness,and power transfer under various scenarios.

关键词： genetic algorithm imperialist competitive algorithm invasive weed algorithm maximum power point tracking

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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

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Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and Technology Department of Electrical and Computer Engineering University of Alberta Systems Research Institute Polish Academy of Sciences Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University

Changes in the Atmospheric Electric Field Signal(AEFS) are highly correlated with weather changes, especially with thunderstorm activities. However, little attention has been paid to the ambiguous weather information implicit in AEFS changes. In this paper, a Fuzzy C-Means(FCM) clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by AEFS. First, a time series dataset is created in the time domain using AEFS attributes. The AEFS-based weather is evaluated according to the time-series Membership Degree(MD) changes obtained by inputting this dataset into the FCM. Second, thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF apparatus. Thus, a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space domain. Finally, the rationality and reliability of the proposed method are verified by combining radar charts and expert experience. The results confirm that this method accurately characterizes the weather attributes and changes in the AEFS, and a negative distance-MD correlation is obtained for the first time. The detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field (AEF) Thunderstorm Fuzzy C-means (FCM) Attribute

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MDNET: A Novel Neural Network Based on CNN and Fuzzy Rough Set with Adaptive Parameters for Intrusion Detection in the Internet of Things

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International Journal of engineering, Transactions B: Applications 2025年第12期38卷 2965-2993页

作者： Zendehdel, M. DehakiToroghi, A. Hamidzadeh, J. Faculty of Computer Engineering and Information Technology Sadjad University Mashhad Iran

The internet of things (IoT) has become increasingly widespread, finding applications in various fields like transportation fleet management, smart homes, traffic management, and smart greenhouses. However, a significant concern with IoT devices is the vast amount of data they store. Sensitive data, including personal information, requires strong security, as unauthorized access can lead to cyberattacks. Attack scenarios, including data theft, system hijacking, and unauthorized access, pose significant threats, making early detection crucial to minimize potential damage. This paper proposes the MDNET neural network for enhanced IoT intrusion detection. The MDNET network aims to achieve high accuracy in detecting IoT network intrusions without requiring manual hyperparameter tuning. To achieve this, the CEO Optimization Algorithm was employed to adaptively select the hyperparameters for the proposed network. Our proposed method employs fuzzy rough set theory to select appropriate features from the network traffic dataset. This process removes features significantly impacted by noise, thereby enhancing the overall efficiency of the *** proposed approach was evaluated using five standard metrics accuracy, average precision, Kappa coefficient, Hamming loss, and Jaccard similarity;compared with seven existing models (FWP-SVM-GA, FBiNN, GA-FR-CNN, APSO-CNN, LSTM, GRU, ANN). The proposed method achieved an impressive 99.57% accuracy, 99.7% average-precision, and 0.99 Kappa coefficient, significantly outperforming the best-performing existing algorithm (FBiNN) with 97.28% accuracy and 0.96 Kappa coefficient. Additionally, MDNET exhibited lower Hamming loss (0.003) and a higher Jaccard similarity (0.99), confirming its superior classification accuracy and robustness in IoT attack detection. ©2025 The author(s).

关键词： Network intrusion

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Performance *** Comparative Analysis of Multimodal Bilinear Pooling Fusion Approaches for Deep Learning-Based Visual Arabic-Question Answering Systems

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computer Modeling in engineering & Sciences 2025年第4期143卷 373-411页

作者： Sarah M.Kamel Mai A.Fadel Lamiaa Elrefaei Shimaa I.Hassan Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairo11629Egypt Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer and Systems Engineering Faculty of Engineering and TechnologyBadr University in Cairo(BUC)Cairo11829Egypt Communication Systems Engineering Department Faculty of EngineeringBenha National UniversityObour11846QalyubiaEgypt

Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate *** this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in *** support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the *** the model complexity and the overall model *** fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA *** far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no ***,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA *** indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that *** the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model *** Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.

关键词： Arabic-VQA deep learning-based VQA deep multimodal information fusion multimodal representation learning VQA of yes/no questions VQA model complexity VQA model performance performance-complexity trade-off

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Deep Learning Utility for Gas/Odor Sensor Signature Analysis 2

Deep Learning Utility for Gas/Odor Sensor Signature Analysis

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Raut, Yashasvi Chaudhri, Shiv Nath Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra India

ISBN: (纸本)9798331505745

Gas/odor sensors are integral components of sensor systems used in diverse applications such as food quality control, environmental monitoring, medical diagnostics, odor profiling, industrial safety, agriculture, explosives detection, wine tasting, pharmaceutical quality control, and toxic gas detection. Traditional approaches in this domain of gas/odor sensing technologies face challenges related to accuracy, cross-sensitivity, and drifted real-time performance. The integration of deep learning techniques with gas sensors has shown promise in overcoming these limitations. Moreover, this amalgamation of gas sensing and deep learning strategies enables accurate identification of gases/odors, handling complex patterns, and leading optimal consumption of resources. In this paper, an overview of advancements in deep learning-enabled gas sensing has been presented that focused on methodologies, sensor types, and applications. This review also highlights the state-of-the-art techniques and identifies gaps for future directions for research. © 2025 IEEE.

关键词： Damage detection

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Code context-based reviewer recommendation

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Frontiers of computer Science 2025年第1期19卷 97-108页

作者： Dawei YUAN Xiao PENG Zijie CHEN Tao ZHANG Ruijia LEI School of Computer Science and Engineering Macao University of Science and TechnologyMacao 999078China Faculty of Science University of AmsterdamAmsterdam WB 1018Netherlands

Code review is a critical process in software development, contributing to the overall quality of the product by identifying errors early. A key aspect of this process is the selection of appropriate reviewers to scrutinize changes made to source code. However, in large-scale open-source projects, selecting the most suitable reviewers for a specific change can be a challenging task. To address this, we introduce the Code Context Based Reviewer Recommendation (CCB-RR), a model that leverages information from changesets to recommend the most suitable reviewers. The model takes into consideration the paths of modified files and the context derived from the changesets, including their titles and descriptions. Additionally, CCB-RR employs KeyBERT to extract the most relevant keywords and compare the semantic similarity across changesets. The model integrates the paths of modified files, keyword information, and the context of code changes to form a comprehensive picture of the changeset. We conducted extensive experiments on four open-source projects, demonstrating the effectiveness of CCB-RR. The model achieved a Top-1 accuracy of 60%, 55%, 51%, and 45% on the Android, OpenStack, QT, and LibreOffice projects respectively. For Mean Reciprocal Rank (MRR), CCB achieved 71%, 62%, 52%, and 68% on the same projects respectively, thereby highlighting its potential for practical application in code reviewer recommendation.

关键词： code reviewer recommendation code context pull request

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Security to text (S2T): multi-layered based security approaches for secret text content

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Multimedia Tools and Applications 2025年第15期84卷 15151-15171页

作者： Kashid, Shamal Awasthi, Lalit K. Berwal, Krishan Department of Computer Science and Engineering National Institute of Technology Uttarakhand IN Srinagar India Faculty of Communication Engineering Military College of Telecommunication Engineering IN Mhow India

In the digital world, text data is produced in an unstructured manner across various communication channels. Extracting valuable information from such data with security is crucial and requires the development of techniques in text mining, information retrieval, and natural language processing (NLP). To solve this issue, we introduce two novel approaches: keyword extraction (KE) and a multi-layered secret sharing scheme (MLSS) to provide security to extracted keywords rather than overall text documents. The KE approach encompasses a sequence of text pre-processing procedures, including tokenization, stopword removal, stemming, and bag of words representation, followed by indexing. This methodology aims to efficiently extract keywords from text datasets. For this research work, we have proposed three datasets, including text messages, whatsapp messages, and electronic mail. MLSS enhances the security of extracted textual data by leveraging text pre-processing steps. This scheme ensures better confidentiality and the non-revealment of sensitive information. Additionally, we evaluate our KE model on our dataset as well as on standard datasets. Experimental results demonstrate the effectiveness of our proposed security to text (S2T) model, which outperform existing state-of-the-art approaches. The model obtains a 100% correlation between the reconstructed text and the original text. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Extraction

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