EMD-SCS: A Dynamic Behavioral Approach for Early Malware Detection with Sonification of System Call Sequences
Bibliographic record
Abstract
The privacy and security of users are increasingly threatened due to the rising frequency of malware assaults. Both Host-based Intrusion Detection Systems (HIDSes) and Antivirus software rely on signature-based or anomaly-based techniques for malware detection. However, the escalating diversity and sophistication of malware pose significant obstacles. In this research, we introduce EMD-SCS, an early malware detection methodology, employing sonification and system call sequence analysis. In our methodology, we interpret an executing program/process as a sequence of system calls, leveraging a Long Short-Term Memory network (LSTM) to hold a record of preceding system calls within this chain, consequently facilitating the prediction of future calls. After this prediction phase, the BLEU and hamming distance scores are utilized to classify the system call sequence. Importantly, these results are attained by analyzing just a small segment of the data for early prediction, which is crucial for a sonification-based approach as it enables us to notify administrators in advance of potential threats. This early warning system would allow admins to protect the host before a potential compromise. EMD-SCS uses sonification to convey the prediction outcomes using natural and animal sounds, offering a broader monitoring scope than visual observation. Evaluation results from the ADFA-LD dataset suggest that EMD-SCS surpasses prior techniques in early malware detection with an accuracy of 91.2%, a detection rate of 87.7%, and a false-positive rate of 15.3%, achieved by only processing 40% of the input system call sequences before they infiltrate the host.
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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".