Validation of Decisions of a Multilayer Perceptron Learning Algorithm for the Identification of Net Attacks with the Aid of Bayesian Classifiers
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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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".