Articles | Open Access | DOI: https://doi.org/10.37547/tajet/Volume06Issue10-10

ADVANCEMENTS IN AIRLINE SECURITY: EVALUATING MACHINE LEARNING MODELS FOR THREAT DETECTION

Fuad Mahmud , Department of Information Assurance and Cybersecurity, Gannon University, USA
Badruddowza , Department of Computer & Info Science, Gannon University, Erie, Pennsylvania, USA
Md Shohail Uddin Sarker , Department of Computer & Info Science, Gannon University, Erie, Pennsylvania, USA
Abdullah Al Mamun , Department of Computer & Info Science, Gannon University, Erie, Pennsylvania, USA
Md Khorshed Alam , Department of Professional Security Studies, New Jersey City University, Jersey City, New Jersey, USA
Md Tarek Hasan , Department of Professional Security Studies, New Jersey City University, Jersey City, New Jersey, USA
Mashaeikh Zaman Md. Eftakhar Choudhury , Master of Social Science in Security Studies, Bangladesh University of Professional (BUP), Dhaka, Bangladesh
Jannatul Ferdous Shorna , College of Engineering and Computer Science, Florida Atlantic University, Boca Raton, Florida, USA

Abstract

This study assessed the performance of four machine learning algorithms—Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN)—for predicting airline security threats using a dataset of 100,000 entries with 30 features. The models were evaluated based on accuracy, precision, recall, F1-Score, and AUC-ROC. The Neural Network achieved the highest performance, with an accuracy of 88%, precision of 86%, recall of 85%, F1-Score of 85.5%, and AUC-ROC of 0.90, demonstrating superior capability in capturing complex, non-linear patterns. The Random Forest model followed, with an accuracy of 85%, precision of 83%, recall of 82%, F1-Score of 82.5%, and AUC-ROC of 0.87, offering a robust and generalizable solution. The SVM model attained an accuracy of 81%, precision of 80%, recall of 78%, F1-Score of 79%, and AUC-ROC of 0.84, showing effective binary classification but with higher computational costs. The Decision Tree model, while interpretable, had the lowest performance metrics: accuracy of 78%, precision of 76%, recall of 72%, F1-Score of 74%, and AUC-ROC of 0.79. The results indicate that Neural Networks and Random Forests are the most effective models for airline security threat detection, with Neural Networks providing the highest overall accuracy and AUC-ROC.

ZENODO DOI:- https://doi.org/10.5281/zenodo.13981482

Keywords

Machine Learning, Decision Tree (DT), Random Forest (RF)

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Fuad Mahmud, Badruddowza, Md Shohail Uddin Sarker, Abdullah Al Mamun, Md Khorshed Alam, Md Tarek Hasan, Mashaeikh Zaman Md. Eftakhar Choudhury, & Jannatul Ferdous Shorna. (2024). ADVANCEMENTS IN AIRLINE SECURITY: EVALUATING MACHINE LEARNING MODELS FOR THREAT DETECTION. The American Journal of Engineering and Technology, 6(10), 86–99. https://doi.org/10.37547/tajet/Volume06Issue10-10