A Robust Algorithm for Sniffing BLE Long-Lived Connections in Real-Time

Sarkar, Sopan; Liu, Jianqing; Jovanov, Emil

doi:10.1109/globecom38437.2019.9014318

Cited by 16 publications

(12 citation statements)

References 12 publications

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“…Hop interval and hop increment remain the same for an established connection, but access address varies over time. The attacker observes a single channel at a time to determine these three pieces of information and can be aware of an access address change by keeping track of missed packets [97]. With all this information an attacker can even sniff without the initial connection setup information in BLE legacy connections.…”

Section: ) Passive Sniffingmentioning

confidence: 99%

Security and Privacy Threats for Bluetooth Low Energy in IoT and Wearable Devices: A Comprehensive Survey

Barua

Alamin

Hossain

et al. 2022

IEEE Open J. Commun. Soc.

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Bluetooth Low Energy (BLE) has become the de facto communication protocol for the Internet of Things (IoT) and smart wearable devices for its ultra-low energy consumption, ease of development, good enough network coverage, and data transfer speed. Due to the simplified design of this protocol, there have been lots of security and privacy vulnerabilities. As billions of health care, personal fitness wearable, smart lock, industrial automation devices adopt this technology for communication, its vulnerabilities should be dealt with high priority. Some segregated works on BLE were performed focusing on various vulnerabilities, such as the insecure implementation of encryption, device authentication, user privacy, etc. However, there has been no comprehensive survey on the security vulnerabilities of this protocol. In this survey paper, we present a comprehensive taxonomy for the security and privacy issues of BLE. We present possible attack scenarios for different types of vulnerabilities, classify them according to their severity, and list possible mitigation techniques. We also provide case studies regarding how different vulnerabilities can be exploited in real BLE devices.

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Section: ) Passive Sniffingmentioning

confidence: 99%

Security and Privacy Threats for Bluetooth Low Energy in IoT and Wearable Devices: A Comprehensive Survey

Barua

Alamin

Hossain

et al. 2022

IEEE Open J. Commun. Soc.

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show abstract

“…For the 8-classes classification case, Friis and MLP present the worst total APC values (see Table 7), close to 25%. Friis has most of its values at classes [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] and [38-55] km/h, as shown in Figure 8. Similarly, as shown in Table 7, random forest and extra trees present similar accuracy values of less than 40%.…”

Section: Experimentation Using Eight Classesmentioning

confidence: 99%

“…The authors' detector proposition combines both Bluetooth and BLE RSSI sniffing capabilities [8,22], to provide real-time numerical information of people or vehicles inside tunnels. Locating users in space in case of an emergency means estimating the number of people involved in the situation, notifying them, and subsequently giving them instructions on avoiding imminent risks that cannot be spotted due to the conditions.…”

Section: Introductionmentioning

confidence: 99%

Driving Speed Estimation and Trapped Drivers’ Detection inside Tunnels Using Distributed MIMO Bluetooth Devices

et al. 2022

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Accidents in highway tunnels involving trucks carrying flammable cargoes can be dangerous, needing immediate confrontation to detect and safely evacuate the trapped people to lead them to the safety exits. Unfortunately, existing sensing technologies fail to detect and track trapped persons or moving vehicles inside tunnels in such an environment. This paper presents a distributed Bluetooth system architecture that uses detection equipment following a MIMO approach. The proposed equipment uses two long-range Bluetooth and one BLE transponder to locate vehicles and trapped people in motorway tunnels. Moreover, the detector’s parts and distributed architecture are analytically described, along with interfacing with the authors’ resources management system implementation. Furthermore, the authors also propose a speed detection process, based on classifier training, using RSSI input and speed calculations from the tunnel inductive loops as output, instead of the Friis equation with Kalman filtering steps. The proposed detector was experimentally placed at the Votonosi tunnel of the EGNATIA motorway in Greece, and its detection functionality was validated. Finally, the detector classification process accuracy is evaluated using feedback from the existing tunnel inductive loop detectors. According to the evaluation process, classifiers based on decision trees or random forests achieve the highest accuracy.

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“…Especially, as in the previous research efforts [9,13-17], we only consider Bluetooth Classic in this paper. Since Ubertooth One also supports Bluetooth Low Energy (BLE) [24], AFH map prediction in BLE sniffers (e.g., [25]) could be considered as a future work.…”

Section: An Overview Of Bluetooth and Afhmentioning

confidence: 99%

Revisiting Adaptive Frequency Hopping Map Prediction in Bluetooth with Machine Learning Classifiers

2021

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Thanks to the frequency hopping nature of Bluetooth, sniffing Bluetooth traffic with low-cost devices has been considered as a challenging problem. To this end, BlueEar, a state-of-the-art low-cost sniffing system with two Bluetooth radios proposes a set of novel machine learning-based subchannel classification techniques for adaptive frequency hopping (AFH) map prediction by collecting packet statistics and spectrum sensing. However, there is no explicit evaluation results on the accuracy of BlueEar’s AFH map prediction. To this end, in this paper, we revisit the spectrum sensing-based classifier, one of the subchannel classification techniques in BlueEar. At first, we build an independent implementation of the spectrum sensing-based classifier with one Ubertooth sniffing radio. Using the implementation, we conduct a subchannel classification experiment with several machine learning classifiers where spectrum features are utilized. Our results show that higher accuracy can be achieved by choosing an appropriate machine learning classifier and training the classifier with actual AFH maps.Our results show that higher accuracy can be achieved by choosing an appropriate machine learning classifier and training the classifier with actual AFH maps.

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A Robust Algorithm for Sniffing BLE Long-Lived Connections in Real-Time

Cited by 16 publications

References 12 publications

Security and Privacy Threats for Bluetooth Low Energy in IoT and Wearable Devices: A Comprehensive Survey

Security and Privacy Threats for Bluetooth Low Energy in IoT and Wearable Devices: A Comprehensive Survey

Driving Speed Estimation and Trapped Drivers’ Detection inside Tunnels Using Distributed MIMO Bluetooth Devices

Revisiting Adaptive Frequency Hopping Map Prediction in Bluetooth with Machine Learning Classifiers

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