Lightweight gait based authentication technique for IoT using subconscious level activities

Musale, Pratik; Baek, Duin; Choi, Bong Jun

doi:10.1109/wf-iot.2018.8355210

Cited by 13 publications

(7 citation statements)

References 7 publications

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“…Ashibani and Mahmoud [ 22 ] could authenticate users based on network traffic patterns of accessed apps in a accuracy range of 79% to 83% for 10 real users. Musale et al [ 53 ] achieved 97% accuracy using random forest algorithm for 12 real users. Anjomshoa et al [ 91 ] proposed a continuous authentication scheme leveraging five social networking apps usage data from smartphone devices, and could verify genuine users up to 97% success ratio for six real users; however, the architecture is cloud-centric, and it would not be suitable for our proposed edge computing architecture.…”

Section: Discussionmentioning

confidence: 99%

“…The work highlights the possibility of using machine learning models in order to generate user profiles [ 22 ]. The proposed implicit authentication mechanisms for IoT environments can also be found in the literature: some are based only on smartphone data [ 22 , 53 ], while others rely on machine learning applied to WiFi signals [ 54 , 55 ] in order to authenticate IoT devices.…”

Section: Related Workmentioning

confidence: 99%

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Non-Invasive Challenge Response Authentication for Voice Transactions with Smart Home Behavior

Hayashi

Ruggiero

2020

Sensors

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Smart speakers, such as Alexa and Google Home, support daily activities in smart home environments. Even though voice commands enable friction-less interactions, existing financial transaction authorization mechanisms hinder usability. A non-invasive authorization by leveraging presence and light sensors’ data is proposed in order to replace invasive procedure through smartphone notification. The Coloured Petri Net model was created for synthetic data generation, and one month data were collected in test bed with real users. Random Forest machine learning models were used for smart home behavior information retrieval. The LSTM prediction model was evaluated while using test bed data, and an open dataset from CASAS. The proposed authorization mechanism is based on Physical Unclonable Function usage as a random number generator seed in a Challenge Response protocol. The simulations indicate that the proposed scheme with specialized autonomous device could halve the total response time for low value financial transactions triggered by voice, from 7.3 to 3.5 s in a non-invasive manner, maintaining authorization security.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Non-Invasive Challenge Response Authentication for Voice Transactions with Smart Home Behavior

Hayashi

Ruggiero

2020

Sensors

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“…For instance, Chauhan et al observed that microphones can be used to extract breathing acoustics when the user is present in a smart environment [10]. Similarly, the builtin accelerometers in mobile and IoT devices have been used to characterize gait or human body movements to facilitate authentication [5,21,22,27]. These approaches were the first steps taken to explore the full potential of smart environments to turn contextual and behavioral data into biometric traits for seamless authentication.…”

Section: Single-biometric Systemsmentioning

confidence: 99%

BeeHIVE: Behavioral Biometric System based on Object Interactions in Smart Environments

Krawiecka¹,

Birnbach²,

Eberz³

et al. 2022

Preprint

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The lack of standard input interfaces in the Internet of Things (IoT) ecosystems presents a challenge in securing such infrastructures. To tackle this challenge, we introduce a novel behavioral biometric system based on naturally occurring interactions with objects in smart environments. This biometric leverages existing sensors to authenticate users without requiring any hardware modifications of existing smart home devices. The system is designed to reduce the need for phone-based authentication mechanisms, on which smart home systems currently rely. It requires the user to approve transactions on their phone only when the user cannot be authenticated with high confidence through their interactions with the smart environment.We conduct a real-world experiment that involves 13 participants in a company environment, using this experiment to also study mimicry attacks on our proposed system. We show that this system can provide seamless and unobtrusive authentication while still staying highly resistant to zero-effort, video, and in-person observation-based mimicry attacks. Even when at most 1% of the strongest type of mimicry attacks are successful, our system does not require the user to take out their phone to approve legitimate transactions in more than 80% of cases for a single interaction. This increases to 92% of transactions when interactions with more objects are considered.

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“…Gait: The human gait is a spatio-temporal motor-controlled biometric behavior that can be employed for to recognise individuals unobtrusively, using a camera, radar, position-, motion-, or pressure-based sensors. Musale et al [113] proposed a Lightweight Gait Authentication Technique (Li-GAT) that exploits information, such as the subconscious level of user activities, collected from IoT devices having inbuilt motion sensors including an accelerometer. For evaluation, LR using deep-NN, RF, kNN classifiers were selected and achieved an accuracy of 96.69% on a dataset containing 12 subjects.…”

Section: Footstepmentioning

confidence: 99%

A Survey of Human-Computer Interaction (HCI) & Natural Habits-based Behavioral Biometric Modalities for User Recognition Schemes

Gupta¹,

Maple²,

Crispo³

et al. 2022

Preprint

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The proliferation of Internet of Things (IoT) systems is having a profound impact across all aspects of life. Recognising and identifying particular users is central to delivering the personalised experience that citizens want to experience, and that organisations wish to deliver. This article presents a survey of human-computer interaction-based (HCI-based) and natural habits-based behavioral biometrics that can be acquired unobtrusively through smart devices or IoT sensors for user recognition purposes. Robust and usable user recognition is also a security requirement for emerging IoT ecosystems to protect them from adversaries. Typically, it can be specified as a fundamental building block for most types of human-to-things accountability principles and access-control methods. However, end-users are facing numerous security and usability challenges in using currently available knowledge- and token-based recognition (i.e., authentication and identification) schemes. To address the limitations of conventional recognition schemes, biometrics, naturally come as a first choice to supporting sophisticated user recognition solutions. We perform a comprehensive review of touch-stroke, swipe, touch signature, hand-movements, voice, gait and footstep behavioral biometrics modalities. This survey analyzes the recent state-of-the-art research of these behavioral biometrics with a goal to identify their attributes and features for generating unique identification signatures. Finally, we present security, privacy, and usability evaluations that can strengthen the designing of robust and usable user recognition schemes for IoT applications.

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Lightweight gait based authentication technique for IoT using subconscious level activities

Cited by 13 publications

References 7 publications

Non-Invasive Challenge Response Authentication for Voice Transactions with Smart Home Behavior

Non-Invasive Challenge Response Authentication for Voice Transactions with Smart Home Behavior

BeeHIVE: Behavioral Biometric System based on Object Interactions in Smart Environments

A Survey of Human-Computer Interaction (HCI) & Natural Habits-based Behavioral Biometric Modalities for User Recognition Schemes

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