A Review of the Recent Trends in Mobile Malware Evolution, Detection, and Analysis
The rapid rise of smartphones and mobile applications has fostered an environment conducive to the advancement of mobile malware, presenting considerable obstacles for both detection and mitigation efforts. This analysis delves into the latest developments in mobile malware, underscoring the shift f...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11048468/ |
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| Summary: | The rapid rise of smartphones and mobile applications has fostered an environment conducive to the advancement of mobile malware, presenting considerable obstacles for both detection and mitigation efforts. This analysis delves into the latest developments in mobile malware, underscoring the shift from basic threats to more complex forms of attacks, including ransomware, spyware, and banking trojans. It discusses significant progress in malware detection techniques, particularly those utilizing behavioral analysis, artificial intelligence (AI), and machine learning (ML). The paper points out deficiencies in current detection methods and stresses the importance of hybrid approaches that integrate both static and dynamic analyses to tackle issues such as obfuscation, polymorphism, and encrypted threats. Furthermore, the research investigates the impact of collaborative threat intelligence sharing and AI-driven tools in improving malware detection and analysis. This systematic review offers a thorough framework for comprehending the progression of mobile malware, assessing existing detection strategies, and pinpointing future research and security initiatives. |
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| ISSN: | 2169-3536 |