The Power of Temporal Pattern Processing in Anomaly Intrusion Detection
Bibliographic record
Abstract
A clear deficiency in most of todays anomaly intrusion detection systems (AIDS) is their inability to distinguish between a new form of legitimate normal behavior and a malicious attack based on known previous normal behaviors. This deficiency is known as the lack of generalization ability. The lack of generalization ability of the present AIDS results mainly in two direct consequences. As a first consequence, the current AIDS are capable of detecting neither new sophisticated attacks nor slight variations of known attacks launched against computing systems. The high rate of false positive and false negative alerts generated by the current AIDS is the second consequence. Many research initiatives that utilize machine learning techniques including neural networks have been proposed to overcome the lack of generalization. Unfortunately, most of such research initiatives have intrinsically focused on utilizing static techniques, that perform structural pattern recognition. Temporal pattern processing techniques have not gained much attention in this arena. In this research, we present a novel anomaly intrusion detection system based on recurrent neural networks (RNN) which is a temporal pattern processing technique. We show that RNN can efficiently discriminate novel intrusive behaviors while recognizing new normal behaviors. Thus, they reduce the false positive and negative alarms, and address the lack of generalization problem associated with the current AIDS. The ability of RNN to generalize normal as well as intrusive behavior outperforms Multilayer Perceptron (MLP) neural network, a structural pattern recognition technique, in a significant way.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".