Field Portioning Approach for Lightweight Java Rule-based Anomaly Detection in IPv6 Tunneling Environments
Bibliographic record
Abstract
The transition from IPv4 to IPv6 introduces new security risks, particularly through tunneling mechanisms that encapsulate IPv6 traffic within IPv4 headers. Conventional Network Intrusion Detection Systems (NIDS) often fail to detect threats hidden in tunneled or multi-layered packets due to limited protocol awareness and high resource consumption. This paper proposes a lightweight, modular Java-based NIDS that employs a Field Portioning Approach (FPA) for efficient, rule-based anomaly detection in IPv6 tunneling environments. The system architecture integrates real-time packet capture, selective decapsulation, field extraction, and context-aware signature matching. Experimental evaluations conducted in a controlled testbed with enterprise and IoT-like devices, where tunneling attacks such as Denial6, NDPExhaust26, and THCSyn6 were launched alongside benign traffic, confirm that the proposed NIDS achieves detection rates exceeding 98% for most tunneling attack types. Its performance is equivalent to Snort enhanced with adaptive FPA, but with significantly lower CPU and memory usage. The Java-based system also maintains low detection latency, demonstrating suitability for resource-constrained environments such as IoT gateways. The main contribution of this work lies in introducing a selective and context-aware field portioning mechanism tailored for tunneled traffic, enabling lightweight yet accurate detection. The results confirm the effectiveness of the Field Portioning Approach in strengthening security for modern, heterogeneous network infrastructures during the IPv6 transition.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".