Analysis of Airport Security Threat Prediction through AI Integration and Gesture Analysis : A Hypothetical Study in the Apron Zone
DOI:
https://doi.org/10.55606/jurritek.v4i2.5969Keywords:
airport-security, artificial-intelligence, gesture-analysis, apron-zone, early-detectionAbstract
Airport security is a vital component in maintaining the stability of air transportation systems. Although scanning technologies and access control systems have significantly advanced, the potential threat posed by internal actors remains an unresolved vulnerability. This study aims to examine the feasibility of integrating artificial intelligence (AI) technologies to detect threat intentions through gesture and body temperature analysis, with a specific focus on the apron zone a highly vulnerable area of the airport. Utilizing a hypothetical scenario based on the Red Team method, this study maps potential breach pathways conducted by individuals with authorized access. The findings suggest that the integration of computer vision, thermal imaging, and behavioral profiling has the potential to identify anomalous behaviors indicative of malicious intent. This research highlights the importance of combining technological approaches with human-centered security strategies to develop a more adaptive and accurate predictive security system.
Downloads
References
Aicardi, C., Bitsch, L., & Burton, S. (2020). (Informasi judul publikasi tidak lengkap – mohon dilengkapi agar bisa diformat sesuai APA)
Al-Mhiqani, M. N., Ahmad, R., Zainal Abidin, Z., Yassin, W., Hassan, A., Abdulkareem, K. H., Ali, N. S., & Yunos, Z. (2020). A review of insider threat detection: Classification, machine learning techniques, datasets, open challenges, and recommendations. Applied Sciences, 10(15), 5208. https://doi.org/10.3390/app10155208
Bishop, M., & Gates, C. (2008). Defining the insider threat. Communications of the ACM, 51(9), 44–49. https://doi.org/10.1145/1413140.1413158
Cox, L. A. (2008). What's wrong with risk matrices? Risk Analysis, 28(2), 497–512. https://doi.org/10.1111/j.1539-6924.2008.01030.x
Ekblom, P. (2011). Crime prevention, security and community safety using the 5Is framework. Palgrave Macmillan. https://doi.org/10.1057/9780230298996
European Union Aviation Safety Agency. (2021). Artificial intelligence roadmap: A human-centric approach to AI in aviation. Cologne, Germany: EASA. https://www.easa.europa.eu
International Air Transport Association. (2020). Security management systems (SeMS) implementation guide. Montreal/Geneva: IATA.
International Civil Aviation Organization. (2020). Security manual (Doc 8973, 11th ed.). Montréal, Canada: ICAO.
Jiao, T., Guo, C., Feng, X., Chen, Y., & Song, J. (2024). A comprehensive survey on deep learning multi-modal fusion: Methods, technologies and applications. Computers, Materials & Continua, 80, 1–35. https://doi.org/10.32604/cmc.2024.053204
Liu, Y., Chen, J., & Sun, F. (2022). Intelligent behavioral anomaly detection using deep hybrid networks for surveillance systems. Expert Systems with Applications, 191, 116323. https://doi.org/10.1016/j.eswa.2021.116323
Organization for Economic Cooperation and Development. (2019). OECD principles on artificial intelligence. https://www.oecd.org/going-digital/ai/principles
Singapore Infocomm Media Development Authority. (2020). Model AI governance framework (2nd ed.). Singapore: IMDA.
Transportation Security Administration. (2020). Insider threat program annual report. Washington, D.C.: U.S. Department of Homeland Security.
UNESCO. (2021). Recommendation on the ethics of artificial intelligence. Paris, France: United Nations Educational, Scientific and Cultural Organization. https://unesdoc.unesco.org/ark:/48223/pf0000381137
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 JURAL RISET RUMPUN ILMU TEKNIK
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
_001.jpg)
