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Mahindru A, Arora H, Kumar A, Gupta SK, Mahajan S, Kadry S, Kim J. PermDroid a framework developed using proposed feature selection approach and machine learning techniques for Android malware detection. Sci Rep 2024; 14:10724. [PMID: 38730228 DOI: 10.1038/s41598-024-60982-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 04/29/2024] [Indexed: 05/12/2024] Open
Abstract
The challenge of developing an Android malware detection framework that can identify malware in real-world apps is difficult for academicians and researchers. The vulnerability lies in the permission model of Android. Therefore, it has attracted the attention of various researchers to develop an Android malware detection model using permission or a set of permissions. Academicians and researchers have used all extracted features in previous studies, resulting in overburdening while creating malware detection models. But, the effectiveness of the machine learning model depends on the relevant features, which help in reducing the value of misclassification errors and have excellent discriminative power. A feature selection framework is proposed in this research paper that helps in selecting the relevant features. In the first stage of the proposed framework, t-test, and univariate logistic regression are implemented on our collected feature data set to classify their capacity for detecting malware. Multivariate linear regression stepwise forward selection and correlation analysis are implemented in the second stage to evaluate the correctness of the features selected in the first stage. Furthermore, the resulting features are used as input in the development of malware detection models using three ensemble methods and a neural network with six different machine-learning algorithms. The developed models' performance is compared using two performance parameters: F-measure and Accuracy. The experiment is performed by using half a million different Android apps. The empirical findings reveal that malware detection model developed using features selected by implementing proposed feature selection framework achieved higher detection rate as compared to the model developed using all extracted features data set. Further, when compared to previously developed frameworks or methodologies, the experimental results indicates that model developed in this study achieved an accuracy of 98.8%.
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Affiliation(s)
- Arvind Mahindru
- Department of Computer Science and applications, D.A.V. University, Sarmastpur, Jalandhar, 144012, India.
| | - Himani Arora
- Department of Mathematics, Guru Nanak Dev University, Amritsar, India
| | - Abhinav Kumar
- Department of Nuclear and Renewable Energy, Ural Federal University Named after the First President of Russia Boris Yeltsin, Ekaterinburg, Russia, 620002
| | - Sachin Kumar Gupta
- Department of Electronics and Communication Engineering, Central University of Jammu, Jammu, 181143, UT of J&K, India.
- School of Electronics and Communication Engineering, Shri Mata Vaishno Devi University, Katra, 182320, UT of J&K, India.
| | - Shubham Mahajan
- Department of Applied Data Science, Noroff University College, Kristiansand, Norway.
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, Kristiansand, Norway
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, 346, United Arab Emirates
- MEU Research Unit, Middle East University, Amman 11831, Jordan
- Applied Science Research Center, Applied Science Private University, Amman, Jordan
| | - Jungeun Kim
- Department of Software, Department of Computer Science and Engineering, Kongju National University, Cheonan, 31080, Korea.
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Wu P, Ye H, Cai X, Li C, Li S, Chen M, Wang M, Heidari AA, Chen M, Li J, Chen H, Huang X, Wang L. An Effective Machine Learning Approach for Identifying Non-Severe and Severe Coronavirus Disease 2019 Patients in a Rural Chinese Population: The Wenzhou Retrospective Study. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:45486-45503. [PMID: 34786313 PMCID: PMC8545214 DOI: 10.1109/access.2021.3067311] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 03/15/2021] [Indexed: 06/13/2023]
Abstract
This paper has proposed an effective intelligent prediction model that can well discriminate and specify the severity of Coronavirus Disease 2019 (COVID-19) infection in clinical diagnosis and provide a criterion for clinicians to weigh scientific and rational medical decision-making. With indicators as the age and gender of the patients and 26 blood routine indexes, a severity prediction framework for COVID-19 is proposed based on machine learning techniques. The framework consists mainly of a random forest and a support vector machine (SVM) model optimized by a slime mould algorithm (SMA). When the random forest was used to identify the key factors, SMA was employed to train an optimal SVM model. Based on the COVID-19 data, comparative experiments were conducted between RF-SMA-SVM and several well-known machine learning algorithms performed. The results indicate that the proposed RF-SMA-SVM not only achieves better classification performance and higher stability on four metrics, but also screens out the main factors that distinguish severe COVID-19 patients from non-severe ones. Therefore, there is a conclusion that the RF-SMA-SVM model can provide an effective auxiliary diagnosis scheme for the clinical diagnosis of COVID-19 infection.
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Affiliation(s)
- Peiliang Wu
- Department of Pulmonary and Critical Care MedicineThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhou325000China
| | - Hua Ye
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Xueding Cai
- Department of Pulmonary and Critical Care MedicineThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhou325000China
| | - Chengye Li
- Department of Pulmonary and Critical Care MedicineThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhou325000China
| | - Shimin Li
- College of Computer Science and Artificial IntelligenceWenzhou UniversityWenzhou325035China
| | - Mengxiang Chen
- Department of Information TechnologyWenzhou Vocational College of Science and TechnologyWenzhou325006China
| | - Mingjing Wang
- College of Computer Science and Artificial IntelligenceWenzhou UniversityWenzhou325035China
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of EngineeringUniversity of TehranTehran1417466191Iran
- Department of Computer ScienceSchool of ComputingNational University of SingaporeSingapore117417
| | - Mayun Chen
- Department of Pulmonary and Critical Care MedicineThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhou325000China
| | - Jifa Li
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Huiling Chen
- College of Computer Science and Artificial IntelligenceWenzhou UniversityWenzhou325035China
| | - Xiaoying Huang
- Department of Pulmonary and Critical Care MedicineThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhou325000China
| | - Liangxing Wang
- Department of Pulmonary and Critical Care MedicineThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhou325000China
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Ye H, Wu P, Zhu T, Xiao Z, Zhang X, Zheng L, Zheng R, Sun Y, Zhou W, Fu Q, Ye X, Chen A, Zheng S, Heidari AA, Wang M, Zhu J, Chen H, Li J. Diagnosing Coronavirus Disease 2019 (COVID-19): Efficient Harris Hawks-Inspired Fuzzy K-Nearest Neighbor Prediction Methods. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:17787-17802. [PMID: 34786302 PMCID: PMC8545238 DOI: 10.1109/access.2021.3052835] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 01/15/2021] [Indexed: 05/26/2023]
Abstract
This study is devoted to proposing a useful intelligent prediction model to distinguish the severity of COVID-19, to provide a more fair and reasonable reference for assisting clinical diagnostic decision-making. Based on patients' necessary information, pre-existing diseases, symptoms, immune indexes, and complications, this article proposes a prediction model using the Harris hawks optimization (HHO) to optimize the Fuzzy K-nearest neighbor (FKNN), which is called HHO-FKNN. This model is utilized to distinguish the severity of COVID-19. In HHO-FKNN, the purpose of introducing HHO is to optimize the FKNN's optimal parameters and feature subsets simultaneously. Also, based on actual COVID-19 data, we conducted a comparative experiment between HHO-FKNN and several well-known machine learning algorithms, which result shows that not only the proposed HHO-FKNN can obtain better classification performance and higher stability on the four indexes but also screen out the key features that distinguish severe COVID-19 from mild COVID-19. Therefore, we can conclude that the proposed HHO-FKNN model is expected to become a useful tool for COVID-19 prediction.
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Affiliation(s)
- Hua Ye
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Peiliang Wu
- Department of Pulmonary and Critical Care MedicineThe 1st Affiliated Hospital, Wenzhou Medical UniversityWenzhou325000China
| | - Tianru Zhu
- The Second Clinical CollegeWenzhou Medical UniversityWenzhou325000China
| | - Zhongxiang Xiao
- Department of PharmacyAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Xie Zhang
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Long Zheng
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Rongwei Zheng
- Department of UrologyAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Yangjie Sun
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Weilong Zhou
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Qinlei Fu
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Xinxin Ye
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Ali Chen
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Shuang Zheng
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of EngineeringUniversity of TehranTehran1417466191Iran
- Department of Computer ScienceSchool of ComputingNational University of SingaporeSingapore117417
| | - Mingjing Wang
- Institute of Research and Development, Duy Tan UniversityDa Nang550000Vietnam
| | - Jiandong Zhu
- Department of Surgical OncologyAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Huiling Chen
- College of Computer Science and Artificial IntelligenceWenzhou UniversityWenzhou325035China
| | - Jifa Li
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
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Hu Z, Wang J, Zhang C, Luo Z, Luo X, Xiao L, Shi J. Uncertainty Modeling for Multi center Autism Spectrum Disorder Classification Using Takagi-Sugeno-Kang Fuzzy Systems. IEEE Trans Cogn Dev Syst 2021. [DOI: 10.1109/tcds.2021.3073368] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Zhongyi Hu
- Intelligent Information Systems Institute, Wenzhou University, Wenzhou 325035, China. (e-mail: )
| | - Jun Wang
- Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai Ins titute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
| | - Chunxiang Zhang
- School of Artificial Intelligence and Computer Science, Jiangnan University, WuXi 214122, China
| | - Zhenzhen Luo
- Intelligent Information Systems Institute, Wenzhou University, Wenzhou 325035, China
| | - Xiaoqing Luo
- School of Artificial Intelligence and Computer Science, Jiangnan University, WuXi 214122, China
| | - Lei Xiao
- Intelligent Information Systems Institute, Wenzhou University, Wenzhou 325035, China
| | - Jun Shi
- Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai Ins titute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
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