Formal and Automatic Security Policy Enforcement on Android Applications by Rewriting1
Bibliographic record
Abstract
With the wide variety of applications offered by Android, this system has been able to dominate the smartphone market. These applications provide all kinds of features and services that have become highly requested and welcomed by users. Besides, these applications represent risky vehicles for malware on Android devices. In this paper, we propose a novel formal technique to enforce the security of Android applications. We start off with an untrusted Android application and a security policy, and we end up in a new version of the application that behaves according to the policy. To ensure the correctness of results, we use formal methods in each step of the process, either in the system and the security policy specification or in the enforcement technique itself. The target application is reverse-engineered to its assembly-like code, Smali. An executable semantics called k-Smali was defined for this code using a language definitional framework, called k Framework. Security policies are specified in LTL-logic. The enforcement step consists of integrating the LTL formula in the k-Smali program using rewriting. It aims to rewrite the system specification automatically so that it satisfies the requested formula.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".