Malicious iOS Apps Detection Through Multi-Criteria Decision-Making Approach
Guardado en:
| Udgivet i: | Informatica vol. 49, no. 1 (Mar 2025), p. 207 |
|---|---|
| Hovedforfatter: | |
| Andre forfattere: | |
| Udgivet: |
Slovenian Society Informatika / Slovensko drustvo Informatika
|
| Fag: | |
| Online adgang: | Citation/Abstract Full Text Full Text - PDF |
| Tags: |
Ingen Tags, Vær først til at tagge denne postø!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3185278983 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 0350-5596 | ||
| 022 | |a 1854-3871 | ||
| 024 | 7 | |a 10.31449/inf.v49il.5664 |2 doi | |
| 035 | |a 3185278983 | ||
| 045 | 2 | |b d20250301 |b d20250331 | |
| 084 | |a 179436 |2 nlm | ||
| 100 | 1 | |a Bhatt, Arpita Jadhav |u Department of Computer Science & Engineering and Information Technology, Jaypee Institute of Information Technology, India | |
| 245 | 1 | |a Malicious iOS Apps Detection Through Multi-Criteria Decision-Making Approach | |
| 260 | |b Slovenian Society Informatika / Slovensko drustvo Informatika |c Mar 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a In today's era, smartphones are used in daily lives because they are ubiquitous and can be customized by installing third-party apps. As a result, the menaces because of these apps, which are potentially risky for user's privacy, have increased. Information on smartphones is perhaps, more personal than compared to data stored on desktops or computers, making it an easy target for intruders. After Android, the most prevalently used mobile operating system is Apple's iOS. Both Android and iOS follow permission-based access control to protect user's privacy. However, the users are unaware whether the app is breaching the user's privacy. To combat this problem, in the paper we propose a hybrid approach to detect malicious iOS apps based on its permissions. In the first phase, weights have been assigned to app permissions using multi-criteria decision-making (MCDM) approach namely Analytic Hierarchy Process (AHP), and in the second phase machine learning & ensemble learning techniques have been employed to train the classifiers for detecting malicious apps. To test the efficacy of the proposed method dataset comprising 1150 apps from 12 app categories has been used. The results demonstrate the proposed approach improves the efficacy of detecting malicious iOS apps for majority of categories. | |
| 610 | 4 | |a Apple Inc | |
| 653 | |a Operating systems | ||
| 653 | |a Analytic hierarchy process | ||
| 653 | |a Smartphones | ||
| 653 | |a Applications programs | ||
| 653 | |a Multiple criterion | ||
| 653 | |a Personal computers | ||
| 653 | |a Privacy | ||
| 653 | |a Multiple criteria decision making | ||
| 653 | |a Effectiveness | ||
| 653 | |a Access control | ||
| 653 | |a Hierarchies | ||
| 653 | |a Machine learning | ||
| 653 | |a Decision making | ||
| 653 | |a Ensemble learning | ||
| 653 | |a Mobile operating systems | ||
| 700 | 1 | |a Sardana, Neetu |u Department of Computer Science & Engineering and Information Technology, Jaypee Institute of Information Technology, India | |
| 773 | 0 | |t Informatica |g vol. 49, no. 1 (Mar 2025), p. 207 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3185278983/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3185278983/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3185278983/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |