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MitM Attack Detection in BLE Networks using Reconstruction and Classification Machine Learning Techniques

Abdelkader Lahmadi 1, 2, 3 Alexis Duque 4, 5 Nathan Heraief 6 Julien Francq 6
1 RESIST - Resilience and Elasticity for Security and ScalabiliTy of dynamic networked systems
Inria Nancy - Grand Est, LORIA - NSS - Department of Networks, Systems and Services
4 AGORA - ALGorithmes et Optimisation pour Réseaux Autonomes
CITI - CITI Centre of Innovation in Telecommunications and Integration of services, Inria Grenoble - Rhône-Alpes
Abstract : Internet of Things (IoT) devices, including smartphones and tablets, are widely deployed in various application domains ranging from smart homes to industrial environments. Many of these devices rely on Bluetooth Low Energy (BLE) as a communication protocol for their control or the transfer of data. Trivial attacks can easily target these devices to compromise them due to their low security features and inherent vul-nerabilities in their software and communication components. In this paper , we firstly demonstrate a Man-in-the-Middle (MitM) attack against BLE devices while collecting datasets of network traffic data exchange with and without the attack. Secondly, we study the use of machine learning to detect this attack by combining unsupervised and supervised techniques. We applied and compared two unsupervised techniques to reconstruct the model of BLE communications and detect suspicious data batches. We then applied a classification method based on Text-CNN technique to classify packets as normal or attack inside each suspicious batch. Our model reconstruction results show that we are able to discriminate normal and attack models with high precision and our classification method achieves high accuracy (≈ 0.99) and low false positive rate (≈ 0.03).
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https://hal.inria.fr/hal-02948407
Contributor : Alexis Duque <>
Submitted on : Thursday, September 24, 2020 - 4:09:36 PM
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  • HAL Id : hal-02948407, version 1

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Abdelkader Lahmadi, Alexis Duque, Nathan Heraief, Julien Francq. MitM Attack Detection in BLE Networks using Reconstruction and Classification Machine Learning Techniques. MLCS 2020 - 2nd Workshop on Machine Learning for Cybersecurity, Sep 2020, Ghent, Belgium. ⟨hal-02948407⟩

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