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This work proposes the use of machine learning (ML) as a candidate for the detection of various types of message injection attacks against automatic dependent surveillance-broadcast (ADSB) messaging systems. Authentic ADS-B messages from a high-traffic area are collected from an open-source platform. These messages are combined with others imposing path modification, ghost aircraft injection, and velocity drift obtained from simulations. Then, ADS-B-related features are extracted from such messages and used to train different ML models for binary classification. For this purpose, authentic ADS-B data is considered as Class 1 (i.e., no attack), while the injection attacks are considered as Class 2 (i.e., presence of attack). The performance of the models is analyzed with metrics, including detection, misdetection, and false alarm rates, as well as validation accuracy, precision, recall, and Fl-score. The resulting models enable identifying the presence of injection attacks with a detection rate of 99.05%, and false alarm and misdetection rates of 0.76% and 1.10%, respectively.
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Optimizing parameters for accurate position data mining in diverse classrooms layouts
Spatial analytics receive increased attention in educational data mining. A critical issue in stop detection (i.e., the automatic extraction of timestamped and located stops in the movement of individuals) is a lack of validation of stop accuracy to represent phenomena of interest. Next to a radius that an actor does not exceed for a certain duration to establish a stop, this study presents a reproducible procedure to optimize a range parameter for K-12 classrooms where students sitting within a certain vicinity of an inferred stop are tagged as being visited. This extension is motivated by adapting parameters to infer teacher visits (i.e., on-task and off-task conversations between the teacher and one or more students) in an intelligent tutoring system classroom with a dense layout. We evaluate the accuracy of our algorithm and highlight a tradeoff between precision and recall in teacher visit detection, which favors recall. We recommend that future research adjust their parameter search based on stop detection precision thresholds. This adjustment led to better cross-validation accuracy than maximizing parameters for an average of precision and recall (F1 = 0.18 compared to 0.09). As stop sample size shrinks with higher precision cutoffs, thresholds can be informed by ensuring sufficient statistical power in offline analyses. We share avenues for future research to refine our procedure further. Detecting teacher visits may benefit from additional spatial features (e.g., teacher movement trajectory) and can facilitate studying the interplay of teacher behavior and student learning.
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- Award ID(s):
- 2119501
- PAR ID:
- 10470775
- Editor(s):
- Feng, M.; Käser, K.; Talukdar, P.
- Publisher / Repository:
- International Educational Data Mining Society
- Date Published:
- Edition / Version:
- Proceedings of the 16th International Conference on Educational Data Mining
- Page Range / eLocation ID:
- 310–317
- Subject(s) / Keyword(s):
- stop detection hyperparameters optimization spatial analytics position mining classroom analytics position sensing
- Format(s):
- Medium: X
- Location:
- Bengaluru, India
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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