Detection of Anomalous Behavior of Smartphone Devices using Changepoint Analysis and Machine Learning Techniques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Detecting anomalous behavior on smartphones is challenging since malware evolution. Other methodologies detect malicious behavior by analyzing static features of the application code or dynamic data samples obtained from hardware or software. Static analysis is prone to code’s obfuscation while dynamic needs that malicious activities to cease to be dormant in the shortest possible time while data samples are collected. Triggering and capturing malicious behavior in data samples in dynamic analysis is challenging since we need to generate an efficient combination of user’s inputs to trigger these malicious activities. We propose a general model which uses a data collector and analyzer to unveil malicious behavior by analyzing the device’s power consumption since this summarizes the changes in software. The data collector uses an automated tool to generate user inputs. The data analyzer uses changepoint analysis to extract features from power consumption and machine learning techniques to train these features. The data analyzer stage contains two methodologies that extract features using parametric and non-parametric changepoint. Our methodologies are efficient in data collection time than a manual method and the data analyzer provides higher accuracy compared to other techniques, reaching over 94% F1-measure for emulated and real malware.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it