An Innovative Keylogger Detection System Using Machine Learning Algorithms and Dendritic Cell Algorithm
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
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.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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