Keyless Authentication Research Articles

Keyless Authentication for AWGN Channels

This work establishes that the physical layer can be used to perform information-theoretic authentication in additive white Gaussian noise (AWGN) channels, as long as the adversary is not omniscient. The model considered consists of an encoder, decoder, and adversary, where the adversary knows the message given to the encoder, has a non-causal noisy observation of the encoder’s transmission and may use unlimited transmission power, while the decoder observes a noisy version of the sum of the encoder and adversary’s outputs. A method to modify a generic existing channel code to enable authentication is presented. This method relies on injecting message-dependent noise into the transmission and accepting the transmission as authentic only if the correct noise levels for the decoded message are observed. One drawback to this method is that the encoder must still transmit a low-power signal in the case where there is no message to send. It is shown that this modification costs an asymptotically negligible amount of the coding rate, while still enabling authentication as long as the adversary’s observation is not noiseless. Also notable is that this modification is not (asymptotically) a function of the statistical characterization of the adversary’s channel and no secret key is required. We believe these features will pave the way for a robust practical implementation. Using these results, the channel-authenticated capacity is calculated and shown to be equal to the non-adversarial channel capacity. As our results will show, information-theoretic authentication in AWGN channels is possible without the need for the legitimate party to have a model-based advantage over the adversary. While this modular scheme is designed for use in the given channel model, it is applicable to a wide range of settings.

Keyless Authentication and Authenticated Capacity

We consider the problem of keyless message authentication over noisy channels in the presence of an active adversary. Different from the existing models, in our model, the legitimate users do not have any pre-shared key for authentication. Instead, we use the noisy channel connecting the legitimate users for authentication. The main idea is to utilize the noisy channel connecting the legitimate users to distinguish a legitimate message from a fake message, by generating an output at the receiver that is difficult for the adversary to replicate through its noisy channel. By interpreting the message authentication as a hypothesis testing problem, we investigate the authentication exponent and the authenticated channel capacity of the noisy channel. In the authentication exponent problem, for a given message rate, we investigate the speed at which the optimal successful attack probability can be driven to zero. We fully characterize the authentication exponent for the zero-rate message case and provide both an upper bound and a lower bound on the exponent for the non-zero message rate case. In the authenticated capacity problem, we study the largest data transmission rate under which the attacker’s optimal successful attack probability can still be made arbitrarily small. We establish an all or nothing result. In particular, we show that the authenticated channel capacity is the same as the classic channel capacity if a simulatability condition is not satisfied, while the authenticated capacity will be zero if this condition is satisfied. We also provide efficient algorithms to check this condition. We further show that our results are robust to modeling uncertainties about the eavesdropper’s channels.

Authentication in Millimeter-Wave Body-Centric Networks Through Wireless Channel Characterization

Advent of 5G technologies has ensued in massive growth of body-centric communications (BCCs), especially at millimeter-wave (mm-wave) frequencies. As a result, the portable/handheld terminals are becoming more and more “intelligent” but not without the cost of being less secure. Improved authentication measures need to be explored, as effective identity authentication is the first level of security in these devices. This paper presents a novel keyless authentication method exploiting wireless channel characteristics. Human palm has distinct transmission coefficient (S21) for each of the users and is used for in vivo fingerprint identification in this paper. A detailed channel modeling using data acquisition from real environment and empirical approach is adopted to evaluate the usability of this method. The results show that this method can provide a secure operation for the mm-wave 5G BCCs.

On the Basic Limits of RF-Fingerprint-Based Authentication

RF fingerprinting exploits the variations in the RF chain of radios to uniquely identify transmitters, and distinguish adversarial transmissions from the transmissions of legitimate nodes. We provide a systematic approach rooted from the information theory to evaluate the basic performance limits of RF fingerprinting. We develop a novel channel model for RF fingerprinting, where the imperfections in the RF chain are modeled as a fingerprint channel, cascaded to the actual physical channel. We address the authentication problem in the presence of an adversary, where both the legitimate transmitter and the adversary are equipped with unique fingerprint channels, in addition to a possible secret key available at the legitimate nodes. We provide bounds for the error exponents for reliable communication of the legitimate nodes, and the success exponent for impersonation and substitution attacks of the adversary, as a function of certain parameters based on their RF-fingerprints, and the shared key rate. We illustrate that keyless authentication is possible via RF fingerprints when the legitimate channel is not simulatable. We also show that the probability of these attacks can be reduced significantly by employing additional dedicated authenticated nodes.

On the Optimality of Keyless Authentication in a Noisy Model

We further study the keyless authentication problem in a noisy model in our previous work, where no secret setup is available for sender Alice and receiver Bob while there is DMC $W_1$ from Alice to Bob and a two-way noiseless but insecure channel between them. We propose a construction such that the message length over DMC $W_1$ does not depend on the size of the source space. If the source space is ${\cal S}$ and the number of channel $W_1$ uses is $n$, then our protocol only has a round complexity of $\log^*|{\cal S}|-\log^*n+4.$ In addition, we show that the round complexity of any secure protocol in our model is lower bounded by $\log^*|{\cal S}|-\log^* n-5$. We also obtain a lower bound on the success probability when the message size on DMC $W_1$ is given. Finally, we derive the capacity for a non-interactive authentication protocol under general DMCs, which extends the result under BSCs in our previous work.

Connected Car: Quantified Self becomes Quantified Car

The automotive industry could be facing a situation of profound change and opportunity in the coming decades. There are a number of influencing factors such as increasing urban and aging populations, self-driving cars, 3D parts printing, energy innovation, and new models of transportation service delivery (Zipcar, Uber). The connected car means that vehicles are now part of the connected world, continuously Internet-connected, generating and transmitting data, which on the one hand can be helpfully integrated into applications, like real-time traffic alerts broadcast to smartwatches, but also raises security and privacy concerns. This paper explores the automotive connected world, and describes five killer QS (Quantified Self)-auto sensor applications that link quantified-self sensors (sensors that measure the personal biometrics of individuals like heart rate) and automotive sensors (sensors that measure driver and passenger biometrics or quantitative automotive performance metrics like speed and braking activity). The applications are fatigue detection, real-time assistance for parking and accidents, anger management and stress reduction, keyless authentication and digital identity verification, and DIY diagnostics. These kinds of applications help to demonstrate the benefit of connected world data streams in the automotive industry and beyond where, more fundamentally for human progress, the automation of both physical and now cognitive tasks is underway.

Keyless Authentication in a Noisy Model

We study a keyless authentication problem in a new noisy model, where there is a discrete memoryless channel (DMC) $W_{1}$ from sender Alice to receiver Bob and a DMC $W_{2}$ from adversary Oscar to Bob. In addition, there is an insecure noiseless channel between Alice and Bob. Under this model, we characterize the condition under which an authentication from Alice to Bob is possible. We also construct a secure authentication protocol that has an authentication rate approaching infinity. Finally, we prove that the authentication capacity of a noninteractive authentication over binary symmetric channels is exactly 1. This is an interesting result as Shannon capacity of channel $W_{1}$ is strictly less than 1 while the noiseless channel is completely unreliable.

A Novel Method for Identifying the Keyless Authentication Entry System using Mobile for Auto Mobiles (CAR)

Mobile technology plays a vital role in modern era. In the emerging trend of speech technologies, speech/non-speech detection is an unsolved problem, which affects numerous speech related applications. One of the major reasons for these thefts is the unauthenticated access to the car doors and engine. Recently the Remote keyless system emerged for users to operate their cars without keys. For identifying the speech signal, Automatic speech recognition is used. It identifies the signal from the particular person with the help of Voice Active Detector (VAD). It is widely used within the field of speech communication for achieving high speech coding efficiency and low-bit rate transmission. These identify the signal and prioritize the unwanted noise signal using Hidden Markov Model (HMM). In our system, we propose a novel way for keyless entry enabled through mobile phone. The user can dial a specific number connected to the car and can give voice commands. The system built in the car receives the voice commands, goes for voice recognition & authentication. By employing Mel Frequency Cepstral Coefficients (MFCC) processor, the speech will be recognized. After positive authentication, it obeys the command of the car owner. The normal operation based on voice commands are Car Door Lock/Unlock, Engine ON/OFF etc.