An Innovative Keylogger Detection System Using Machine Learning Algorithms and Dendritic Cell Algorithm
Bibliographic record
Abstract
Every computer user deals with serious privacy and security challenges. Keyloggers are a type of software malware that records keystroke events from the console and saves them to a log file. It allows to obtain sensitive information like passwords, PINs, and usernames and communicates with vengeful attackers without attracting the attention of users. Keyloggers are also types of session hijackers that record user keystrokes made on the computer to steal any sensitive information from the system. Keyloggers are the most dangerous and covert malware for our system since they are difficult to detect because they run in the background of the computer. The primary issue with keylogger detection in a system is its time-consuming nature and its reliance on a particular type of input traffic behaviour. Keyloggers can be prevented using antiviruses, but, cannot be detected once they entered into the system. We proposed a system that combines Dendritic Cell Algorithms (DCA) and Machine Learning Algorithms (MLA) to address these problems. Our system can accurately detect a software keylogger if it is present which is based on the rate at which inputs are given to the system. The best accuracy was attained by our hybrid SVM-NB-DCA and SVM-DCA approach, with accuracies of 99.8% and 96%, respectively. Hence, results have shown that our hybrid system is effective and accurate for keylogger detection.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".