Secured LTE-Wi-Fi Offloading Using RTT Based Evading Malicious Access Point (EMAP) Algorithm
Bibliographic record
Abstract
Exponential growth of mobile devices and increased usage of data by mobile users creates a high data traffic problem in cellular networks. Data offloading to Wi-Fi networks provides an alternative to relieve the congestion that occurs in such cellular networks. Wi-Fi Access Point to which data is offloaded must be chosen carefully as there is a possibility of Malicious Access Points (MAPs) in the network, which could trick the users to connect with them instead of Legitimate Access Points (LAPs). Thus, the offloaded data may be used for devious purposes by the MAP which results in a severe security breach like military and defense. Therefore, it is necessary to detect and weed out such MAPs. A normalized K Nearest Neighbors (KNN) algorithm, which is a supervised learning technique, is used to learn from a set of given data pertaining to previous history of Access Points with their characteristic information and decides if the Access Point is malicious or non-malicious. In this paper, we propose a KNN based Evading Malicious Access Point (EMAP) algorithm that identifies MAPs, by using a combination of Round Trip Time (RTT) probes sent and beacon frames received by the user, thus offloading safely to a LAP. The results obtained show that our algorithm has an efficiency of 85% in high as well as low traffic conditions, as compared to lower and variable efficiency of existing proposed methods for identifying MAPs.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".