Anomaly Based Network Intrusion Detection with Unsupervised Outlier Detection
Bibliographic record
Abstract
Anomaly detection is a critical issue in Network Intrusion Detection Systems (NIDSs). Most anomaly based NIDSs employ supervised algorithms, whose performances highly depend on attack-free training data. However, this kind of training data is difficult to obtain in real world network environment. Moreover, with changing network environment or services, patterns of normal traffic will be changed. This leads to high false positive rate of supervised NIDSs. Unsupervised outlier detection can overcome the drawbacks of supervised anomaly detection. Therefore, we apply one of the efficient data mining algorithms called random forests algorithm in anomaly based NIDSs. Without attack-free training data, random forests algorithm can detect outliers in datasets of network traffic. In this paper, we discuss our framework of anomaly based network intrusion detection. In the framework, patterns of network services are built by random forests algorithm over traffic data. Intrusions are detected by determining outliers related to the built patterns. We present the modification on the outlier detection algorithm of random forests. We also report our experimental results over the KDD'99 dataset. The results show that the proposed approach is comparable to previously reported unsupervised anomaly detection approaches evaluated over the KDD' 99 dataset.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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 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".