Malware detection for mobile computing using secure and privacy-preserving machine learning approaches: A comprehensive survey
<p dir="ltr">Mobile devices have become an essential element in our day-to-day lives. The chances of mobile attacks are rapidly increasing with the growing use of mobile devices. Exploiting vulnerabilities from devices as well as stealing personal information, are the principal targe...
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| مؤلفون آخرون: | , , , |
| منشور في: |
2024
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| _version_ | 1864513542709837824 |
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| author | Faria Nawshin (21841598) |
| author2 | Radwa Gad (21841601) Devrim Unal (16864224) Abdulla Khalid Al-Ali (17983786) Ponnuthurai N. Suganthan (17347024) |
| author2_role | author author author author |
| author_facet | Faria Nawshin (21841598) Radwa Gad (21841601) Devrim Unal (16864224) Abdulla Khalid Al-Ali (17983786) Ponnuthurai N. Suganthan (17347024) |
| author_role | author |
| dc.creator.none.fl_str_mv | Faria Nawshin (21841598) Radwa Gad (21841601) Devrim Unal (16864224) Abdulla Khalid Al-Ali (17983786) Ponnuthurai N. Suganthan (17347024) |
| dc.date.none.fl_str_mv | 2024-04-11T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.compeleceng.2024.109233 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Malware_detection_for_mobile_computing_using_secure_and_privacy-preserving_machine_learning_approaches_A_comprehensive_survey/29715122 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Cybersecurity and privacy Data management and data science Machine learning Mobile malware analysis Privacy-preserving machine-learning Secure machine-learning Mobile security attacks Federated learning Mobile vulnerabilities |
| dc.title.none.fl_str_mv | Malware detection for mobile computing using secure and privacy-preserving machine learning approaches: A comprehensive survey |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Mobile devices have become an essential element in our day-to-day lives. The chances of mobile attacks are rapidly increasing with the growing use of mobile devices. Exploiting vulnerabilities from devices as well as stealing personal information, are the principal targets of the attackers. Researchers are also developing various techniques for detecting and analyzing <u>mobile malware</u> to overcome these issues. As new <u>malware</u> gets introduced frequently by <u>malware developers</u>, it is very challenging to come up with comprehensive algorithms to detect this malware. There are many machine-learning and deep-learning algorithms have been developed by researchers. The accuracy of these models largely depends on the size and quality of the training dataset. Training the model with a diversified dataset is necessary to predict new malware accurately. However, this training process may raise the issue of privacy loss due to the disclosure of<u> sensitive information</u> of the users. Researchers have proposed various techniques to mitigate this issue, such as <u>differential privacy, homomorphic encryption, </u>and<u> federated learning.</u> This survey paper explores the significance of applying federated learning to the <u>mobile operating systems, contrasting traditional machine learning </u>and<u> deep learning</u> approaches for mobile malware detection. We delve into the unique challenges and opportunities of the architecture of in-built mobile operating systems and their implications for user privacy and security. Moreover, we assess the risks associated with federated learning in real-life applications and recommend strategies for developing a secure federated learning framework in the domain of mobile malware detection.</p><h2>Other Information</h2><p dir="ltr">Published in: Computers and Electrical Engineering<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.compeleceng.2024.109233" target="_blank">https://dx.doi.org/10.1016/j.compeleceng.2024.109233</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_94aa55bcf1ff9708cf3d6e191fd53df9 |
| identifier_str_mv | 10.1016/j.compeleceng.2024.109233 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29715122 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Malware detection for mobile computing using secure and privacy-preserving machine learning approaches: A comprehensive surveyFaria Nawshin (21841598)Radwa Gad (21841601)Devrim Unal (16864224)Abdulla Khalid Al-Ali (17983786)Ponnuthurai N. Suganthan (17347024)Information and computing sciencesCybersecurity and privacyData management and data scienceMachine learningMobile malware analysisPrivacy-preserving machine-learningSecure machine-learningMobile security attacksFederated learningMobile vulnerabilities<p dir="ltr">Mobile devices have become an essential element in our day-to-day lives. The chances of mobile attacks are rapidly increasing with the growing use of mobile devices. Exploiting vulnerabilities from devices as well as stealing personal information, are the principal targets of the attackers. Researchers are also developing various techniques for detecting and analyzing <u>mobile malware</u> to overcome these issues. As new <u>malware</u> gets introduced frequently by <u>malware developers</u>, it is very challenging to come up with comprehensive algorithms to detect this malware. There are many machine-learning and deep-learning algorithms have been developed by researchers. The accuracy of these models largely depends on the size and quality of the training dataset. Training the model with a diversified dataset is necessary to predict new malware accurately. However, this training process may raise the issue of privacy loss due to the disclosure of<u> sensitive information</u> of the users. Researchers have proposed various techniques to mitigate this issue, such as <u>differential privacy, homomorphic encryption, </u>and<u> federated learning.</u> This survey paper explores the significance of applying federated learning to the <u>mobile operating systems, contrasting traditional machine learning </u>and<u> deep learning</u> approaches for mobile malware detection. We delve into the unique challenges and opportunities of the architecture of in-built mobile operating systems and their implications for user privacy and security. Moreover, we assess the risks associated with federated learning in real-life applications and recommend strategies for developing a secure federated learning framework in the domain of mobile malware detection.</p><h2>Other Information</h2><p dir="ltr">Published in: Computers and Electrical Engineering<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.compeleceng.2024.109233" target="_blank">https://dx.doi.org/10.1016/j.compeleceng.2024.109233</a></p>2024-04-11T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.compeleceng.2024.109233https://figshare.com/articles/journal_contribution/Malware_detection_for_mobile_computing_using_secure_and_privacy-preserving_machine_learning_approaches_A_comprehensive_survey/29715122CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/297151222024-04-11T09:00:00Z |
| spellingShingle | Malware detection for mobile computing using secure and privacy-preserving machine learning approaches: A comprehensive survey Faria Nawshin (21841598) Information and computing sciences Cybersecurity and privacy Data management and data science Machine learning Mobile malware analysis Privacy-preserving machine-learning Secure machine-learning Mobile security attacks Federated learning Mobile vulnerabilities |
| status_str | publishedVersion |
| title | Malware detection for mobile computing using secure and privacy-preserving machine learning approaches: A comprehensive survey |
| title_full | Malware detection for mobile computing using secure and privacy-preserving machine learning approaches: A comprehensive survey |
| title_fullStr | Malware detection for mobile computing using secure and privacy-preserving machine learning approaches: A comprehensive survey |
| title_full_unstemmed | Malware detection for mobile computing using secure and privacy-preserving machine learning approaches: A comprehensive survey |
| title_short | Malware detection for mobile computing using secure and privacy-preserving machine learning approaches: A comprehensive survey |
| title_sort | Malware detection for mobile computing using secure and privacy-preserving machine learning approaches: A comprehensive survey |
| topic | Information and computing sciences Cybersecurity and privacy Data management and data science Machine learning Mobile malware analysis Privacy-preserving machine-learning Secure machine-learning Mobile security attacks Federated learning Mobile vulnerabilities |