A Comprehensive Review on Intrusion Detection and Prevention Schemes for Network Coding Enabled Mobile Small Cells
Bibliographic record
Abstract
With technological breakthroughs and increasing network technology reliability in daily life, it is vital to provide robust network-based system operations. According to the Comprehensive Error Rate Test Report, as network attacks become more common, the number of attacks nearly doubles or triples every year. The fifth-generation mobile communication is going to provide a connecting world with almost zero Latency. Network coding has emerged as a viable answer to the mostly efficiency, secure network needs for coming-generation networking technology. A small cell environment with network coding allows an effective system to communicate with the high rate of data. A significant countermeasure for several forms of network attacks is the Intrusion Detection Systems (IDS). Unique IDS solutions which are lightweight but also provide a high level of security are therefore demanded. The main security concerns in collaborative networks are Distributed -Denial- of- Service (DDOS) attacks, pollution attacks etc. While the network efficiency of non-DDOS attacks is also impaired, the impact of DDOS attacks is severe. In DDOS attacks, the specific node as a victim floods and mass traffic jams occurs, affecting the entire network performance. However, small, NC-enabled mobile cells, due to the essential NC vulnerabilities, are susceptible to pollution attacks. This article presents a brief survey on intrusion detection and mitigation schemes for Network Coding environment Mobile Small Cells.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".