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Understanding the Bad Development Practices of Android Custom Permissions in the Wild

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Zhang, Xiaohan Yu, Zhiyuan Li, Xinghua Zhang, Cen Sun, Cong Zhang, Ning Deng, Robert H. Xidian University State Key Laboratory of Integrated Service Networks School of Cyber Engineering Xi'an710071 China Ministry of Education Engineering Research Center of Big data Security China Washington University in St. Louis Department of Computer Science and Engineering St. LouisMO63130 United States Nanyang Technological University 639798 Singapore Singapore Management University 188065 Singapore

Android system provides application developers with the ability to define custom permissions, which serve to regulate the sharing of resources and functionalities with other applications. However, developers' improper development practices can render the permission mechanism ineffective, facilitating easy exploitation by attackers. This paper presents a comprehensive examination of the problematic practices surrounding custom permissions employed by developers, referred to as Bad Practices of Custom Permissions (BPCP issues). To accomplish this, we conducted an empirical study and identified nine common BPCP issue patterns that can lead to various adverse consequences, such as installation failures, crashes, and even component hijacking. To automatically identify these patterns of bad practices, we devised PERMEAGRE, a static analysis tool. Employing PERMEAGRE, we performed a large-scale analysis of 83,085 applications obtained from seven major app markets, aiming to detect instances of BPCP issues. The results revealed that more than 26% of the analyzed apps contained at least one issue, and a significant number of apps had garnered millions of downloads. Our analysis delved into the underlying causes of these issues. Consequently, this analysis sheds light on the potential threat landscape associated with bad practices in custom permissions, emphasizing the urgent requirement for effective mitigation strategies. © 2004-2012 IEEE.

关键词： Android custom permission empirical study static analysis

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Tensor-Based Multi-Scale Correlation Anomaly Detection for aioT-Enabled Consumer applications

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IEEE Transactions on Consumer Electronics 2024年

作者： Zeng, Jiuzhen Yang, Laurence T. Wang, Chao Deng, Xianjun Yang, Xiangli Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan430074 China Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou450001 China St. Francis Xavier University Department of Computer Science AntigonishNSB2G 2W5 Canada University of South China School of Electrical Engineering Hengyang421001 China

Artificial Intelligence of Things (aioT) is an innovative paradigm expected to enable various consumer applications that is transforming our lives. While enjoying benefits and services from these applications, we also face serious security issues due to malicious cyber attacks on the massive growth of aioT consumer devices. Accurate anomaly detection is one of the critical tasks for the trustworthy aioT removing those obstacles. However, limited by the vector-based data pattern and ill-considered factors for anomalous samples analysis, existing methods suffer from the low detection performance. In this paper, a multi-scale correlation tensor convolutional Gaussian mixture network (named as MTCGM) is presented for ameliorating this actuality. Specifically, MTCGM suggests to construct the multi-scale correlation tensor by stacking one self-correlation matrix and multiple surrounding-correlations of different scales, which well characterizes the network status of aioT. Subsequently, a 3D-convolutional autoencoder (3DCA) is designed for capturing inter-feature correlations, and followed with a Gaussian mixture probability (GMP) network for the observations likelihood estimation. Moreover, low-dimensional space features, relative Euclidean distance and tensor cosine similarity (TCS) are adopted in MTCGM as the multi-factor to boost the likelihood estimation. Extensive experiments on public benchmark datasets verify the validity of MTCGM, and demonstrate its superiority over the state-of-the-art baselines even in presence of contaminated training samples and input noise. © 1975-2011 IEEE.

关键词： Tensors

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Identification of Clinical Features Associated with Mortality in COVID-19 Patients

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Operations research Forum 2023年第1期4卷 16页

作者： Eskandarian, Rahimeh Alizadehsani, Roohallah Behjati, Mohaddeseh Zahmatkesh, Mehrdad Sani, Zahra Alizadeh Haddadi, Azadeh Kakhi, Kourosh Roshanzamir, Mohamad Shoeibi, Afshin Hussain, Sadiq Khozeimeh, Fahime Darbandy, Mohammad Tayarani Joloudari, Javad Hassannataj Lashgari, Reza Khosravi, Abbas Nahavandi, Saeid Islam, Sheikh Mohammed Shariful Internal Medicine Research Center Semnan University of Medical Sciences Semnan Iran Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Victoria Australia Rajaei Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran Omid Hospital Iran University of Medical Sciences Tehran Iran Department of Biology Faculty of Basic Sciences Shahrekord Branch Islamic Azad University Shahrekord Iran Department of Computer Engineering Faculty of Engineering Fasa University Fasa 74617-81189 Iran Data Science and Computational Intelligence Institute University of Granada Granada Spain System Administrator Dibrugarh University Assam 786004 India School of Architecture Islamic Azad University Taft Taft *** Iran Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Department of Computer Engineering Amol Institute of Higher Education Amol Iran Institute of Medical Science and Technology Shahid Beheshti University Tehran Iran School of Engineering and Applied Sciences Harvard Paulson Harvard University Allston 02134 MA United States Institute for Physical Activity and Nutrition School of Exercise and Nutrition Sciences Deakin University Geelong 3220 VIC Australia Cardiovascular Division The George Institute for Global Health Newtown Australia Sydney Medical School University of Sydney Camperdown Australia

Understanding clinical features and risk factors associated with COVID-19 mortality is needed to early identify critically ill patients, initiate treatments and prevent mortality. A retrospective study on COVID-19 patients referred to a tertiary hospital in Iran between March and November 2020 was conducted. COVID-19-related mortality and its association with clinical features including headache, chest pain, symptoms on computerized tomography (CT), hospitalization, time to infection, history of neurological disorders, having a single or multiple risk factors, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia were investigated. Based on the investigation outcome, decision tree and dimension reduction algorithms were used to identify the aforementioned risk factors. Of the 3008 patients (mean age 59.3 ± 18.7 years, 44% women) with COVID-19, 373 died. There was a significant association between COVID-19 mortality and old age, headache, chest pain, low respiratory rate, oxygen saturation < 93%, need for a mechanical ventilator, having symptoms on CT, hospitalization, time to infection, neurological disorders, cardiovascular diseases and having a risk factor or multiple risk factors. In contrast, there was no significant association between mortality and gender, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia. Our results might help identify early symptoms related to COVID-19 and better manage patients according to the extracted decision tree. The proposed ML models identified a number of clinical features and risk factors associated with mortality in COVID-19 patients. These models if implemented in a clinical setting might help to early identify patients needing medical attention and care. However, more studies are needed to confirm these findings. © 2023, The Author(s).

关键词： COVID‐19 Machine learning Mortality Risk factors Symptoms

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DiGAN Breakthrough: Advancing diabetic data analysis with innovative GAN-based imbalance correction techniques

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computer Methods and Programs in Biomedicine Update 2024年 5卷

作者： Zhao, Puyang Liu, Xinhui Yue, Zhiyi Zhao, Qianyu Liu, Xinzhi Deng, Yuhui Wu, Jingjin Department of Biostatistics The University of Texas Health Science Center at Houston Houston TX United States Department of Statistics The London School of Economics and Political Science London United Kingdom Department of Civil and Environmental Engineering Duke University Durham NC United States School of Physics Southeast University Nanjing China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science Department of Statistics and Data Science BNU-HKBU United International College Zhuhai China

In the rapidly evolving field of medical diagnostics, the challenge of imbalanced datasets, particularly in diabetes classification, calls for innovative solutions. The study introduces DiGAN, a groundbreaking approach that leverages the power of Generative Adversarial Networks (GAN) to revolutionize diabetes data analysis. Marking a significant departure from traditional methods, DiGAN applies GANs, typically seen in image processing, to the realm of diabetes data. This novel application is complemented by integrating the unsupervised Laplacian Score for sophisticated feature selection. The pioneering approach not only surpasses the limitations of existing techniques but also sets a new benchmark in classification accuracy with a 90% weighted F1-score, achieving a remarkable improvement of over 20% compared to conventional methods. Additionally, DiGAN demonstrates superior performance over popular SMOTE-based methods in handling extremely imbalanced datasets. This research, focusing on the integrated use of Laplacian Score, GAN, and Random Forest, stands at the forefront of diabetic classification, offering a uniquely effective and innovative solution to the long-standing data imbalance issue in medical diagnostics. © 2024 The Authors

关键词： Correction techniques Diabetes GAN Imbalanced classification Laplacian score

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Jointly Optimizing Power Allocation and Device Association for Robust IoT Networks under Infeasible Circumstances

arXiv

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arXiv 2024年

作者： Tung, Nguyen Xuan Van Chien, Trinh Hoang, Dinh Thai Hwang, Won Joo Department of Information Convergence Engineering Pusan National University Busan46241 Korea Republic of Viet Nam School of Electrical and Data Engineering University of Technology Sydney SydneyNSW2007 Australia School of Computer Science and Engineering Center for Artificial Intelligence Research Pusan National University Busan46241 Korea Republic of

Jointly optimizing power allocation and device association is crucial in Internet-of-Things (IoT) networks to ensure devices achieve their data throughput requirements. Device association, which assigns IoT devices to specific access points (APs), critically impacts resource allocation. Many existing works often assume all data throughput requirements are satisfied, which is impractical given resource limitations and diverse demands. When requirements cannot be met, the system becomes infeasible, causing congestion and degraded performance. To address this problem, we propose a novel framework to enhance IoT system robustness by solving two problems, comprising maximizing the number of satisfied IoT devices and jointly maximizing both the number of satisfied devices and total network throughput. These objectives often conflict under infeasible circumstances, necessitating a careful balance. We thus propose a modified branch-and-bound (BB)-based method to solve the first problem. An iterative algorithm is proposed for the second problem that gradually increases the number of satisfied IoT devices and improves the total network throughput. We employ a logarithmic approximation for a lower bound on data throughput and design a fixed-point algorithm for power allocation, followed by a coalition game-based method for device association. Numerical results demonstrate the efficiency of the proposed algorithm, serving fewer devices than the BB-based method but with faster running time and higher total throughput. Copyright © 2024, The Authors. All rights reserved.

关键词： Approximation algorithms

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Cooperative and Competitive Functional Connectivity Based on Improved Ising Model

Cooperative and Competitive Functional Connectivity Based on...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Gengqian Wei Chuang Liang Tülay Adali Rongtao Jiang Daoqiang Zhang Vince D. Calhoun Shile Qi Department of Artificial Intelligence Nanjing University of Aeronautics and Astronautics Nanjing China Department of Computer Science and Electrical Engineering University of Maryland Baltimore Maryland USA Department of Radiology and Biomedical Imaging Yale University New Haven CT USA Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Atlanta GA USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

As a highly interconnected complex network system, the brain exhibits changes in interactions due to common brain disorders. Studying changes in brain network interactions can help us quantitatively analyze functional network patterns and changes in these patterns that are linked to brain disorders. However, relationships between brain regions estimated by most current approaches use a single connectivity that does not fully reflect multiple interactions. Here, we propose a novel functional connectivity (FC) construction method, which can estimate both cooperative and competitive (C-C) relationships between the same regions of interest (ROIs) through improved Ising model. We redefine the Ising dynamic equation to represent pairwise interactions from single to C-C relationships. Results show that the estimated C-C connectivities are normally distributed, with intra-subjects’ (n=970) similarity being consistently and significantly higher than inter-subjects’ similarity across datasets. C-C FCs between occipital, parietal, temporal cortex and the limbic system of schizophrenia (SZ, n=178) are more competitive, while healthy control (HC, n=219) tends to be more cooperative. Group differences in C-C patterns between SZ and HC show significant differences in frontal, parietal and occipital regions. The proposed C-C approach provide new insights into the brain dysfunction in SZ, which can also be applied to investigate other brain disorders.

关键词： Complex networks Signal processing Schizophrenia Brain modeling Mathematical models Limbic system Acoustics Reliability Speech processing

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AADNet: An End-to-End Deep Learning Model for Auditory Attention Decoding

arXiv

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arXiv 2024年

作者： Nguyen, Nhan Duc Thanh Phan, Huy Geirnaert, Simon Mikkelsen, Kaare Kidmose, Preben Center for Ear-EEG Department of Electrical and Computer Engineering Aarhus University Aarhus N8200 Denmark Amazon CambridgeMA02142 United States Stadius Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven and Leuven.AI - KU Leuven institute for AI Kasteelpark Arenberg 10 LeuvenB-3001 Belgium The Department of Neurosciences Research Group ExpORL Herestraat 49 box 721 LeuvenB-3000 Belgium

Auditory attention decoding (AAD) is the process of identifying the attended speech in a multi-talker environment using brain signals, typically recorded through electroencephalography (EEG). Over the past decade, AAD has undergone continuous development, driven by its promising application in neuro-steered hearing devices. Most AAD algorithms are relying on the increase in neural entrainment to the envelope of attended speech, as compared to unattended speech, typically using a two-step approach. First, the algorithm predicts representations of the attended speech signal envelopes;second, it identifies the attended speech by finding the highest correlation between the predictions and the representations of the actual speech signals. In this study, we proposed a novel end-to-end neural network architecture, named AADNet, which combines these two stages into a direct approach to address the AAD problem. We compare the proposed network against the traditional approaches, including linear stimulus reconstruction, canonical correlation analysis, and an alternative non-linear stimulus reconstruction using two different datasets. AADNet shows a significant performance improvement for both subject-specific and subject-independent models. Notably, the average subject-independent classification accuracies from 56.1% to 82.7% with analysis window lengths ranging from 1 to 40 seconds, respectively, show a significantly improved ability to generalize to data from unseen subjects. These results highlight the potential of deep learning models for advancing AAD, with promising implications for future hearing aids, assistive devices, and clinical assessments. © 2024, CC BY-NC-SA.

关键词： Hearing aids

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Reference-Guided Parallel Independent Component Analysis: Estimating Cognition Associated Multimodal Patterns In Schizophrenia

Reference-Guided Parallel Independent Component Analysis: Es...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jingxian Hu Chuang Liang Tülay Adali Qi Zhu Daoqiang Zhang Rongtao Jiang Vince D. Calhoun Shile Qi Department of Artificial Intelligence Nanjing University of Aeronautics and Astronautics Nanjing China Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA Department of Radiology and Biomedical Imaging Yale School of Medicine New Haven USA Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Atlanta GA USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal fusion provides cross-modality information to understand the human brain from different perspectives that may be missed in single modality analysis. Supervised fusion focuses on extracting multimodal patterns related to specific clinical measures by further incorporating a prior interested reference. However, existing supervised fusion methods cannot extract component that have weak correlations with the reference, which may be lost during the optimization process. Here, we propose a reference-guided parallel independent component analysis (RG-PICA) aiming at identifying multimodal covarying features related to interested reference through global optimization. The intra-modality independence, the inter-modality correlation, and the correlation between modalities and the reference are maximized globally. Simulations show that RG-PICA can accurately extract multimodal features correlated with the weak related reference while keeping cross-modality linkage comparing with seven fusion methods. In real data application, RG-PICA reveals co-varying patterns in schizophrenia (SZ) that links with cognition and correlates between modalities. These results demonstrate RG-PICA can jointly optimize for target components that correlate with the reference while keeping cross-modality linkage. This approach can improve the meaningful detection of reliable reference-linked multimodal brain patterns for brain disorders.

关键词： Couplings Analytical models Correlation Independent component analysis Schizophrenia Feature extraction Brain modeling Cognition data mining Optimization

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Causal Deep Learning for Real-Time Detection of Cardiac Surgery-Associated Acute Kidney Injury: Derivation and Validation in Seven Time-Series Cohorts

SSRN

引用

SSRN 2024年

作者： Zhong, Qin Cheng, Yuxiao Li, Zongren Wang, Dongjin Rao, Chongyou Jiang, Yi Li, Lianglong Wang, Ziqian Liu, Pan Che, Hebin Li, Pei Lv, Xin Suo, Jinli He, Kunlun Medical Big Data Research Center Chinese PLA General Hospital Beijing China National Engineering Research Center of Medical Big Data Application Technology Chinese PLA General Hospital Beijing China Department of Automation Tsinghua University Beijing China Department of Cardiovascular Surgery Nanjing Drum Tower Hospital Chinese Academy of Medical Science Peking Union Medical College Nanjing China College of Systems Engineering National University of Defense Technology Changsha China Beijing Key Laboratory of Chronic Heart Failure Precision Medicine China

Background: Cardiac surgery-associated Acute Kidney Injury (CSA-AKI) is a complex complication significantly contributing to an increased risk of mortality. Effective CSA-AKI management relies on timely diagnosis and interventions. However, many cases are detected too late. Despite the advancements in novel biomarkers and data-driven predictive models, existing practices are primarily constrained due to the limited discriminative and generalization capabilities and stringent application requirements, presenting significant challenges to the timely and effective diagnosis and interventions in CSA-AKI management. Methods: This retrospective model development and prospective validation study aimed to develop a causal deep learning architecture, named REACT, to achieve precise and dynamic predictions of CSA-AKI within the subsequent 48 hours. This study included adult'patients (aged ≥18 years) from seven distinct cohorts undergoing major open heart surgery for model training and validation. data for model development and internal validation were sourced from electronic health records (EHR) of two large centers in Beijing, China, from January 2000 to December 2022. External validation was conducted on the EHRs of three independent centers in China between January 2000 and December 2022, along with cross-national data from the public databases MIMIC-IV and eICU in the United States. To facilitate implementation, we also developed a publicly accessible web calculator and applet. The model's prospective application was validated from June to October 2023 at two centers in Beijing and Nanjing. Findings: The final derivation cohort included 14,513 eligible patients with no critical information missing, a median age of 56 years, 38·0% women, and 21·0% developed CSA-AKI. The external validation dataset included 20,813 patients from China and 28,023 from the United States. REACT reduced 1,325 input variables to six essential "causal" factors for CSA-AKI prediction. In internal v

关键词： Cardiovascular surgery

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Implicit Supervision-Assisted Graph Collaborative Filtering for Third-Party Library Recommendation

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IEEE Transactions on Services Computing 2025年第3期18卷 1459-1471页

作者： Lianrong Chen Mingdong Tang Naidan Mei Fenfang Xie Guo Zhong Qiang He School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China Unicom (Guangdong) Industry Internet Company Guangzhou China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Computing Technologies Swinburne University of Technology Melbourne VIC Australia

Third-party libraries (TPLs) play a crucial role in software development. Utilizing TPL recommender systems can aid software developers in promptly finding useful TPLs. A number of TPL recommendation approaches have been proposed and among them graph neural network (GNN)-based recommendation is attracting the most attention. However, GNN-based approaches generate node representations through multiple convolutional aggregations, which is prone to introducing noise, resulting in the over-smoothing issue. In addition, due to the high sparsity of labelled data, node representations may be biased in real-world scenarios. To address these issues, this paper presents a TPL recommendation method named Implicit Supervision-assisted Graph Collaborative Filtering (ISGCF). Specifically, it takes the App-TPL interaction relationships as input and employs a popularity-debiased method to generate denoised App and TPL graphs. This reduces the noise introduced during graph convolution and alleviates the over-smoothing issue. It also employs a novel implicitly-supervised loss function to exploit the labelled data to learn enhanced node representations. Extensive experiments on a large-scale real-world dataset demonstrate that ISGCF achieves a significant performance advantage over other state-of-the-art TPL recommendation methods in Recall, NDCG and MAP. The experiments also validate the superiority of ISGCF in mitigating the over-smoothing problem.

关键词： Vectors Noise Training Collaborative filtering Recommender systems Libraries Predictive models Graph neural networks Electronic mail Convolution

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