Machine Learning Approach to Combat False Alarms in Wireless Intrusion Detection System
Bibliographic record
Abstract
Wireless Networks facilitate the ease of communication for sharing the crucial information. Recently, most of the small and large-scale companies, educational institutions, government organizations, medical sectors, military and banking sectors are using the wireless networks. Security threats, a common term found both in wired as well as in wireless networks. However, it holds lot of importance in wireless networks because of its susceptible nature to threats. Security concerns in WLAN are studied and many organizations concluded that Wireless Intrusion Detection Systems (WIDS) is an essential element in network security infrastructure to monitor wireless activity for signs of attacks. However, it is an indisputable fact that the art of detecting attacks remains in its infancy. WIDS generally collect the activities within the protected network and analyze them to detect intrusions and generates an intrusion alarm. Irrespective of the different types of Intrusion Detection Systems, the major problems arising with WIDS is its inability to handle large volumes of alarms and more prone to false alarm attacks. Reducing the false alarms can improve the overall efficiency of the WIDS. Many techniques have been proposed in the literature to reduce the false alarm rates. However, most of the existing techniques are failed to provide desirable result and the high complexity to achieve high detection rate with less false alarm rates. This is the right time to propose a new technique for providing high detection accuracy with less false alarm rate. This paper made an extensive survey about the role of machine learning techniques to reduce the false alarm rate in WLAN IEEE 802.11. This survey proved that the substantial improvement has been achieved by reducing false alarm rate through machine learning algorithms. In addition to that, advancements specific to machine learning approaches is studied meticulously and a filtration technique is proposed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".