Conference Paper2024

Securing Bluetooth Technology Against The BrakTooth Vulnerabilities Through Hybrid Machine Learning Model

Jahanggir Hossain Setu, Md Sazzad Hossain, Nabarun Halder, Ashraful Islam, M Ashraful Amin

3rd International Conference on Computational Modelling, Simulation and Optimization

IEEE, pp. 59-65

DOI: 10.1109/iccmso61761.2024.00027URL

Abstract

BrakTooth, which impacts various versions (including 5.2) of Bluetooth technology, represents a substantial security threat across multiple sectors, including IoT, automotive, healthcare, and financial systems. This study utilizes a hybrid Machine Learning (ML) classifier, combining Extra Trees, Adaptive Boosting (AdaBoost), and Random Forest (RF), to analyze the ISOT BrakTooth Attack dataset which focuses on the baseband layer and Link Manager Protocol (LMP) in the Bluetooth protocol stack. The study proposes a methodological framework that integrates advanced data preprocessing and resampling techniques including the Combined Cleaning and Resampling (CCR) method, Synthetic Minority Over-sampling Technique (SMOTE), and Gaussian SMOTE to address class imbalance issue. Information Gain was employed as a feature selection technique to find the most contributing features in terms of predicting outcomes. The performance of the classifier was evaluated across different distributions of the dataset, with the CCR technique showing remarkable improvement in all metrics. It achieves an accuracy of 99.44%, and precision, recall, and F1-score values above 99.78%. This result demonstrates a substantial enhancement over the original dataset distribution which achieved an accuracy of 90.97% without resampling or feature selection. Moreover, the Area Under Curve (AUC) score of 0.98 confirms the effectiveness of the proposed methodology. The results highlight the importance of resampling techniques in enhancing the detection capabilities of ML classifiers against Bluetooth security threats.

Keywords

  • AdaBoost
  • extra trees
  • information gain feature selection
  • Internet of Things security
  • SMOTE
  • hybrid classifier
  • Bluetooth security
  • class imbalance
  • Random forest
  • attack detection

CCDS Authors

Nabarun Halder
Nabarun Halder

Nabarun Halder

  • Research Assistant, CCDS

Published at

UbiComp, IJCNN, HCII, IEEE Access

Research Interests

Machine Learning, Deep Learning, Natural Language Processing, Health Informatics, Human-Computer Interaction

Ashraful Islam, PhD
Ashraful Islam

Ashraful Islam, PhD

  • Center Director, CCDS
  • Director, HCI Wing

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh (IUB)

Published at

CHI, UIST, UbiComp, IJCNN, ICIP, HCII, ICDMW, BLP, IEEE Internet of Things Journal, JMIR, JMIR Human Factors, JMIR Formative Research, IEEE Access, Smart Agricultural Technology, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Human-Computer Interaction, AI for Social Good, AI for Public Health, AI for Impact

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Professor

+1 more affiliation

Department of Information Sciences and Technology

George Mason University, USA

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

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