Malware Investigation and Analysis for Cyber Threat Intelligence: A Case Study of Flubot Malware
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".