Efficacy of Hidden Markov Models Over Neural Networks in Anomaly Intrusion Detection
Bibliographic record
Abstract
The timely and accurate detection of novel attacks is a persistent necessity to insure the dependability of information processing systems. Although anomaly intrusion detection systems (AIDSs) have the potential to discover novel attacks, AIDSs suffer from the lack of generalization capability and the presence of high false alarm rates. Many machine learning techniques have been proposed to overcome the lack of generalization in existing AIDSs. Unfortunately, the main stream of these techniques is static techniques that perform structural pattern recognition. Such techniques are not capable of efficiently modeling an essential property of the behaviors of the monitored objects. This property is the sequential relationship between the events of the patterns that constitute the normal and abnormal behaviors. In this research, we show that the sequential relationship between the events of the normal and abnormal behaviors is vital for anomaly detection. Moreover, the techniques that efficiently model this property can build robust AIDSs. To illustrate this reality, we investigate the performance of two different detection techniques: Hidden Markov Models (HMMs), a sequential learning mechanism, and Multilayer Perceptron (MLP) neural network, a structural pattern recognition technique. We demonstrate that the detection of HMMs classifiers outperforms the detection of the MLP classifiers in a noticeable manner.
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 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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".