Automated Android Malware Detection Using Optimal Ensemble Learning Approach for Cybersecurity
Current technological advancement in computer systems has transformed the lives of humans from real to virtual environments. Malware is unnecessary software that is often utilized to launch cyber-attacks. Malware variants are still evolving by using advanced packing and obfuscation methods. These ap...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10177906/ |
| _version_ | 1797774910188158976 |
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| author | Hayam Alamro Wafa Mtouaa Sumayh Aljameel Ahmed S. Salama Manar Ahmed Hamza Aladdin Yahya Othman |
| author_facet | Hayam Alamro Wafa Mtouaa Sumayh Aljameel Ahmed S. Salama Manar Ahmed Hamza Aladdin Yahya Othman |
| author_sort | Hayam Alamro |
| collection | DOAJ |
| description | Current technological advancement in computer systems has transformed the lives of humans from real to virtual environments. Malware is unnecessary software that is often utilized to launch cyber-attacks. Malware variants are still evolving by using advanced packing and obfuscation methods. These approaches make malware classification and detection more challenging. New techniques that are different from conventional systems should be utilized for effectively combating new malware variants. Machine learning (ML) methods are ineffective in identifying all complex and new malware variants. The deep learning (DL) method can be a promising solution to detect all malware variants. This paper presents an Automated Android Malware Detection using Optimal Ensemble Learning Approach for Cybersecurity (AAMD-OELAC) technique. The major aim of the AAMD-OELAC technique lies in the automated classification and identification of Android malware. To achieve this, the AAMD-OELAC technique performs data preprocessing at the preliminary stage. For the Android malware detection process, the AAMD-OELAC technique follows an ensemble learning process using three ML models, namely Least Square Support Vector Machine (LS-SVM), kernel extreme learning machine (KELM), and Regularized random vector functional link neural network (RRVFLN). Finally, the hunter-prey optimization (HPO) approach is exploited for the optimal parameter tuning of the three DL models, and it helps accomplish improved malware detection results. To denote the supremacy of the AAMD-OELAC method, a comprehensive experimental analysis is conducted. The simulation results portrayed the supremacy of the AAMD-OELAC technique over other existing approaches. |
| first_indexed | 2024-03-12T22:28:02Z |
| format | Article |
| id | doaj.art-cdce2396a4224d839132596f2e41eef8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T22:28:02Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-cdce2396a4224d839132596f2e41eef82023-07-21T23:00:25ZengIEEEIEEE Access2169-35362023-01-0111725097251710.1109/ACCESS.2023.329426310177906Automated Android Malware Detection Using Optimal Ensemble Learning Approach for CybersecurityHayam Alamro0Wafa Mtouaa1Sumayh Aljameel2https://orcid.org/0000-0001-8246-4658Ahmed S. Salama3Manar Ahmed Hamza4Aladdin Yahya Othman5https://orcid.org/0009-0000-8770-4390Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Mathematics, Faculty of Sciences and Arts, King Khalid University, Muhayil, Saudi ArabiaDepartment of Computer Science, College of Computer Science and Information Technology, Saudi Aramco Cybersecurity Chair, Imam Abdulrahman Bin Faisal University, Dammam, Saudi ArabiaDepartment of Electrical Engineering, Faculty of Engineering and Technology, Future University in Egypt, New Cairo, EgyptDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi ArabiaCurrent technological advancement in computer systems has transformed the lives of humans from real to virtual environments. Malware is unnecessary software that is often utilized to launch cyber-attacks. Malware variants are still evolving by using advanced packing and obfuscation methods. These approaches make malware classification and detection more challenging. New techniques that are different from conventional systems should be utilized for effectively combating new malware variants. Machine learning (ML) methods are ineffective in identifying all complex and new malware variants. The deep learning (DL) method can be a promising solution to detect all malware variants. This paper presents an Automated Android Malware Detection using Optimal Ensemble Learning Approach for Cybersecurity (AAMD-OELAC) technique. The major aim of the AAMD-OELAC technique lies in the automated classification and identification of Android malware. To achieve this, the AAMD-OELAC technique performs data preprocessing at the preliminary stage. For the Android malware detection process, the AAMD-OELAC technique follows an ensemble learning process using three ML models, namely Least Square Support Vector Machine (LS-SVM), kernel extreme learning machine (KELM), and Regularized random vector functional link neural network (RRVFLN). Finally, the hunter-prey optimization (HPO) approach is exploited for the optimal parameter tuning of the three DL models, and it helps accomplish improved malware detection results. To denote the supremacy of the AAMD-OELAC method, a comprehensive experimental analysis is conducted. The simulation results portrayed the supremacy of the AAMD-OELAC technique over other existing approaches.https://ieeexplore.ieee.org/document/10177906/Cybersecuritymalware detectionensemble learninghunter prey optimizationmachine learning |
| spellingShingle | Hayam Alamro Wafa Mtouaa Sumayh Aljameel Ahmed S. Salama Manar Ahmed Hamza Aladdin Yahya Othman Automated Android Malware Detection Using Optimal Ensemble Learning Approach for Cybersecurity IEEE Access Cybersecurity malware detection ensemble learning hunter prey optimization machine learning |
| title | Automated Android Malware Detection Using Optimal Ensemble Learning Approach for Cybersecurity |
| title_full | Automated Android Malware Detection Using Optimal Ensemble Learning Approach for Cybersecurity |
| title_fullStr | Automated Android Malware Detection Using Optimal Ensemble Learning Approach for Cybersecurity |
| title_full_unstemmed | Automated Android Malware Detection Using Optimal Ensemble Learning Approach for Cybersecurity |
| title_short | Automated Android Malware Detection Using Optimal Ensemble Learning Approach for Cybersecurity |
| title_sort | automated android malware detection using optimal ensemble learning approach for cybersecurity |
| topic | Cybersecurity malware detection ensemble learning hunter prey optimization machine learning |
| url | https://ieeexplore.ieee.org/document/10177906/ |
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