Malware Detection through Memory Forensics and Windows Event Log Analysis
Bibliographic record
Abstract
With the increasing reliance of human society on computer systems in daily life, cybercrime is also on the rise. Malware is increasingly used by cybercriminals to attack, compromise, and steal sensitive information, and more critically, to demand ransom from users of infected systems. Existing antivirus solutions often fall short in detecting and alerting users to attacks carried out by newly developed or evolving malware strains. This highlights the need for a more robust and proactive strategy for malware detection. This paper presents a hybrid approach for advanced malware detection, integrating the identification of suspicious code executing in main memory with the analysis of malware-related events in Windows Event Logs. Experiments were conducted using a code injection technique on Windows 7 and Windows 10 systems, and the corresponding memory images and Event Logs were analyzed to validate the effectiveness of the proposed approach. Training and testing were performed on both code-based and event-based datasets to evaluate detection accuracy. For the detection of suspicious code, we employed the Canadian Institute for Cybersecurity-Malware in Memory 2023 (CIC-MalMem 2023) dataset. For event-based analysis, we utilized the EVTX-ATTACK-SAMPLES and the Windows Event Log dataset. Experimental results using the Random Forest (RF)classifier demonstrate a detection accuracy of 99% based on suspicious code and 95% based on Event Log data
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".