Automated malware classification based on network behavior
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
Over the past decade malware, i.e., malicious software, has become a major security threat on the Internet. Today anti-virus companies receive thousands of malicious samples every day. However the vast majority of these samples are variants of the existing malware. Due to the sheer number of malware variants it is important to accurately determine whether a sample belongs to a known malware family or exhibits a new behavior and thus requires further analysis and separate detection signature. Despite of the importance of network activity, the existing research on malware analysis does not fully leverage the malware network behavior for classification. In this paper, we propose an automated malware classification system that focuses on network behavior of malware samples. Our approach employs behavioral profiles that summarize the network behavior of malware samples. The proposed approach is applied to a real world malware corpus. Our experimental results show the effectiveness of the proposed approach in classifying malware samples only based on the network activity exhibited by the samples.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 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 it