Deep Learning-Based Attack Detection and Classification in Android Devices
importance 7/10
ai assisted
Advanced Malware Detection Techniques mobile security stage: research
Problem
Detecting and classifying malware attacks on Android devices, which dominate the global market, remains an ongoing challenge.
Why it matters
Android's 72% market share makes billions of devices prime targets for attackers.
What the AI did
A supervised deep learning technique trained on a labeled dataset of over 18,000 samples across five categories (Adware, Banking, SMS, Riskware, Benign) to detect and classify Android malware.
Results
not stated in source
Impact
Abstract claims promising results validated on established benchmark datasets, but no numbers are given.
Record
| Venue | Electronics |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Alfonso Gómez, Antonio Muñoz |
| Citations | 41 (OpenAlex, at import time) |
| References | 65 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/electronics12153253 |
| Open access | gold |
| Keywords | Android, malware detection, deep learning, classification, cybersecurity |
Importance rating
7
significant — thousands to millions affected
Abstract states Android holds 72% global market share, making billions of devices prime malware targets; a major cybersecurity problem, though one detection technique among many.
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