Threat Hunting in Windows Using Big Security Log Data
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
System logs are one of the most important sources of information for anomaly and intrusion detection systems. In a general log-based anomaly detection system, network, devices, and host logs are all collected and used together for analysis and the detection of anomalies. However, the ever-increasing volume of logs remains as one of the main challenges that anomaly detection tools face. Based on Sysmon, this chapter proposes a host-based log analysis system that detects anomalies without using network logs to reduce the volume and to show the importance of host-based logs. The authors implement a Sysmon parser to parse and extract features from the logs and use them to perform detection methods on the data. The valuable information is successfully retained after two extensive volume reduction steps. An anomaly detection system is proposed and performed on five different datasets with up to 55,000 events which detects the attacks using the preserved logs. The analysis results demonstrate the significance of host-based logs in auditing, security monitoring, and intrusion detection systems.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.024 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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