Malware Investigation and Analysis for Cyber Threat Intelligence: A Case Study of Flubot Malware
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
Android operating systems have swiftly outpaced other operating systems (OS) in popularity, making them vulnerable to assaults since hackers are continuously looking for flaws to exploit. This is why several organisations have long been plagued by various types of mobile security threats. Utilizing a cyber-threat intelligence tool to evaluate, track, and prevent planned attacks is one crucial strategy to combat this effect. This paper discusses and investigates the FluBot malware, using the Dagah tool and Android Studio to phish, harvest and exploit malicious applications over SMS on Android devices. The Capability Maturity Model (CMM) was adopted and used for the investigation. The methodology adopted describes the operation of the FluBot malware through a cloned website, and demonstrates how FluBot is used to share a malicious link through the short message service (SMS), which is then used to grab a victim’s credentials. The outcome of the study displayed the information on the FluBot malware, including its source, domain, and destination. Similar malware analysis and assessments of cyber threat intelligence may be conducted using the techniques used in this study.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.007 |
| 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