Semi-Supervised Intrusion Detection System for Detecting Gray Hole and Hello Flood Attacks
Bibliographic record
Abstract
Network security faces challenges due to advanced threats like gray hole and hello flood attacks. Traditional Intrusion Detection Systems (IDS) using misuse-based detection methods (MBIDS) and anomaly-based detection methods (ABIDS) often fall short.Misuse-based detection relies on predefined signatures, making it ineffective against novel attacks, while anomaly-based detection suffers from high false positive rates.The proposed Semi-Supervised Hybrid Intrusion Detection System (SSHIDS) combines the strengths of misuse-based and anomaly-based detection techniques using semi-supervised machine learning algorithms.This approach enhances detection accuracy by 0.10% and reduces false positives by 0.15% compared to conventional methods.SSHIDS learns from both labeled and unlabelled data, improving detection capabilities and adapting to evolving attack patterns.SSHIDS significantly improves detection accuracy, reduces false positive rates, and increases computational efficiency.By integrating misuse and anomaly detection techniques, SSHIDS offers a scalable and adaptive defense mechanism for dynamic network environments, addressing critical gaps in existing IDS solutions and providing a more reliable and efficient means of protecting networks against sophisticated attacks.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".