HIDS:DC-ADT : An Effective Hybrid Intrusion Detection System based on Data Correlation and Adaboost based Decision Tree classifier
Bibliographic record
Abstract
Due to the rapid development of computer networks, intrusions and attacks into these networks have grown, and occur in various ways. Thus, usually an intrusion detection system can play an important role in security protection and intruders‟ accessibility to network prevention. In this paper, a new hybrid approach, which is called HIDS:DC-ADT, is used to design proposed detection engine. In the proposed intrusion detection system, the anomaly detection engine is responsible to detect new and unknown attacks and the misuse detection engine is responsible to protect anomaly detection system. Through this, it is assured that collected data and patterns be safe for anomaly detection system. In the intrusion anomaly detection using statistical correlation method that is of data correlation methods, normal behavior of network is analyzed statistically by KDD-Cup99 data-set. Further, the Data Correlation Graph (DCG) has been proposed to show behaviors deviation of normal behavior. In misuse detection, Principal Components Analysis (PCA) is used to dimensionality reduction. More, a new classification method by Adaboost algorithm using base classifier of decision tree C4.5 has been introduced for classification. Simulation results show that this hybrid system can reach a competitive accuracy and efficiency.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".