Validation of Decisions of a Multilayer Perceptron Learning Algorithm for the Identification of Net Attacks with the Aid of Bayesian Classifiers
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
An intrusion detection system (IDS) is a software application that monitors the network for potential malicious attacks against a single computer or a computer network. A multilayer perceptron (MLP) learning algorithm is used detect such attacks and identifies the kind of attack like WebAttack, DoS or BruteForce. A multilayer perceptron (MLP) is a class of feedforward artificial neural network (ANN), which consists of at least three layers of nodes: an input layer, a hidden layer and an output layer. Since ANNs belong to the so called black box algorithms, it is useful to validate its results. In this paper a method is presented to validate the decisions of the MLP algorithm concerning the type of net attack with the help of Bayesian Classifiers. Particularly the Naïve Bayesian Classifier and the Tree Augmented Naïve (TAN) Bayesian Classifier are used for this task. It will be shown that these classifiers are capable to satisfactorily validate the decisions of the MLP algorithm. This will be accomplished with aid of real datasets from the Canadian Institute for Cybersecurity along with appropriate metrics to evaluate Machine Learning algorithms.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it