Deep Learning Based Malapps Detection in Android Powered Mobile Cyber-Physical System
Bibliographic record
Abstract
The Mobile Cyber-Physical System (MCPS) integrates the mobility of various smart devices to exchange information between physical and cyber systems. Among those intelligent devices, Android-powered smartphone usage increased significantly due to its low cost and simplicity. But this global prominence of Android operating system also makes it more appealing for cyberattacks to obtain users’ physical private information. Since attackers mostly prefer malicious applications to spread different viruses and take control of the user’s device, it is crucial to classify and categorize the malignant application for secure MCPS. Modern machine learning algorithms have shown promising performance in identifying dangerous applications compared to traditional signature-based methods. But most existing works identify only the malicious application where category identification is essential for proper precaution. Also, the static analysis is insufficient for polymorphic malware, which includes regenerating code and changing its properties frequently to evade the detection process. In this study, we compare several state-of-the-art deep learning methods for malapps classification and categorization. Moreover, we propose an ensemble Dynamic Weighted Voting model to identify and label a wide variety of malicious applications using the CCCS-CIC-AndMal-2020 [1] dataset, which contains an extensive collection of Android malware samples. Our proposed ensemble model outperforms the baseline ensemble method Majority Voting by 1% and the classical LSTM model by 2%.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".