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Register a new userA Novel IoT Sensor Authentication Using HaLo Extraction Method and Memory Chip Variability
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Added by
Fatemeh Tehranipoor
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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In this paper, we propose flash-based hardware security primitives as a viable solution to meet the security challenges of the IoT and specifically telehealth markets. We have created a novel solution, called the High and Low (HaLo) method, that generates physical unclonable function (PUF) signatures based on process variations within flash memory in order to uniquely identify and authenticate remote sensors. The HaLo method consumes 60% less power than conventional authentication schemes, has an average latency of only 39ms for signature generation, and can be readily implemented through firmware on ONFI 2.2 compliant off-the-shelf NAND flash memory chips. The HaLo method generates 512 bit signatures with an average error rate of 5.9 * 10^-4, while also adapting for flash chip aging. Due to its low latency, low error rate, and high power efficiency, the HaLo method could help progress the field of remote patient monitoring by accurately and efficiently authenticating remote health sensors.
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Over the past several years, the electrocardiogram (ECG) has been investigated for its uniqueness and potential to discriminate between individuals. This paper discusses how this discriminatory information can help in continuous user authentication by a wearable chest strap which uses dry electrodes to obtain a single lead ECG signal. To the best of the authors knowledge, this is the first such work which deals with continuous authentication using a genuine wearable device as most prior works have either used medical equipment employing gel electrodes to obtain an ECG signal or have obtained an ECG signal through electrode positions that would not be feasible using a wearable device. Prior works have also mainly dealt with using the ECG signal for identification rather than verification, or dealt with using the ECG signal for discrete authentication. This paper presents a novel algorithm which uses QRS detection, weighted averaging, Discrete Cosine Transform (DCT), and a Support Vector Machine (SVM) classifier to determine whether the wearer of the device should be positively verified or not. Zero intrusion attempts were successful when tested on a database consisting of 33 subjects.
Recurrent neural networks (RNNs) have shown promising results in audio and speech processing applications due to their strong capabilities in modelling sequential data. In many applications, RNNs tend to outperform conventional models based on GMM/UBMs and i-vectors. Increasing popularity of IoT devices makes a strong case for implementing RNN based inferences for applications such as acoustics based authentication, voice commands, and edge analytics for smart homes. Nonetheless, the feasibility and performance of RNN based inferences on resources-constrained IoT devices remain largely unexplored. In this paper, we investigate the feasibility of using RNNs for an end-to-end authentication system based on breathing acoustics. We evaluate the performance of RNN models on three types of devices; smartphone, smartwatch, and Raspberry Pi and show that unlike CNN models, RNN models can be easily ported onto resource-constrained devices without a significant loss in accuracy.
The majority of systems rely on user authentication on passwords, but passwords have so many weaknesses and widespread use that easily raise significant security concerns, regardless of their encrypted form. Users hold the same password for different accounts, administrators never check password files for flaws that might lead to a successful cracking, and the lack of a tight security policy regarding regular password replacement are a few problems that need to be addressed. The proposed research work aims at enhancing this security mechanism, prevent penetrations, password theft, and attempted break-ins towards securing computing systems. The selected solution approach is two-folded; it implements a two-factor authentication scheme to prevent unauthorized access, accompanied by Honeyword principles to detect corrupted or stolen tokens. Both can be integrated into any platform or web application with the use of QR codes and a mobile phone.
Contemporary IoT environments, such as smart buildings, require end-users to trust data-capturing rules published by the systems. There are several reasons why such a trust is misplaced -- IoT systems may violate the rules deliberately or IoT devices may transfer user data to a malicious third-party due to cyberattacks, leading to the loss of individuals privacy or service integrity. To address such concerns, we propose IoT Notary, a framework to ensure trust in IoT systems and applications. IoT Notary provides secure log sealing on live sensor data to produce a verifiable `proof-of-integrity, based on which a verifier can attest that captured sensor data adheres to the published data-capturing rules. IoT Notary is an integral part of TIPPERS, a smart space system that has been deployed at the University of California Irvine to provide various real-time location-based services on the campus. We present extensive experiments over realtime WiFi connectivity data to evaluate IoT Notary, and the results show that IoT Notary imposes nominal overheads. The secure logs only take 21% more storage, while users can verify their one days data in less than two seconds even using a resource-limited device.
Today, two-factor authentication (2FA) is a widely implemented mechanism to counter phishing attacks. Although much effort has been investigated in 2FA, most 2FA systems are still vulnerable to carefully designed phishing attacks, and some even request special hardware, which limits their wide deployment. Recently, real-time phishing (RTP) has made the situation even worse because an adversary can effortlessly establish a phishing website replicating a target website without any background of the web page design technique. Traditional 2FA can be easily bypassed by such RTP attacks. In this work, we propose a novel 2FA system to counter RTP attacks. The main idea is to request a user to take a photo of the web browser with the domain name in the address bar as the 2nd authentication factor. The web server side extracts the domain name information based on Optical Character Recognition (OCR), and then determines if the user is visiting this website or a fake one, thus defeating the RTP attacks where an adversary must set up a fake website with a different domain. We prototyped our system and evaluated its performance in various environments. The results showed that PhotoAuth is an effective technique with good scalability. We also showed that compared to other 2FA systems, PhotoAuth has several advantages, especially no special hardware or software support is needed on the client side except a phone, making it readily deployable.
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