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Register a new userVoltKey: Using Power Line Noise for Zero-Involvement Pairing and Authentication (Demo Abstract)
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We present VoltKey, a method that transparently generates secret keys for colocated devices, leveraging spatiotemporally unique noise contexts observed in commercial power line infrastructure. VoltKey extracts randomness from power line noise and securely converts it into an authentication token. Nearby devices which observe the same noise patterns on the powerline generate identical keys. The unique noise pattern observed only by trusted devices connected to a local power line prevents malicious devices without physical access from obtaining unauthorized access to the network. VoltKey is implemented inside of a standard USB power supply as a platform-agnostic bolt-on addition to any IoT or mobile device or any wireless access point that is connected to the power outlet.
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Context-based authentication is a method for transparently validating another devices legitimacy to join a network based on location. Devices can pair with one another by continuously harvesting environmental noise to generate a random key with no user involvement. However, there are gaps in our understanding of the theoretical limitations of environmental noise harvesting, making it difficult for researchers to build efficient algorithms for sampling environmental noise and distilling keys from that noise. This work explores the information-theoretic capacity of context-based authentication mechanisms to generate random bit strings from environmental noise sources with known properties. Using only mild assumptions about the source processs characteristics, we demonstrate that commonly-used bit extraction algorithms extract only about 10% of the available randomness from a source noise process. We present an efficient algorithm to improve the quality of keys generated by context-based methods and evaluate it on real key extraction hardware. Moonshine is a randomness distiller which is more efficient at extracting bits from an environmental entropy source than existing methods. Our techniques nearly double the quality of keys as measured by the NIST test suite, producing keys that can be used in real-world authentication scenarios.
In wireless OFDM communications systems, pilot tones, due to their publicly known and deterministic characteristic, suffer significant jamming/nulling/spoofing risks. Thus, the convectional channel training protocol using pilot tones could be attacked and paralyzed, which raises the issue of anti-attack channel training authentication (CTA), i.e., verifying the claims of identities of pilot tones and channel estimation samples. In this paper, we consider one-ring scattering scenarios with large-scale uniform linear arrays (ULA) and develop an independence-checking coding (ICC) theory to build a secure and stable CTA protocol, namely, ICC-based CTA (ICC-CTA) protocol. In this protocol, the pilot tones are not only merely randomized and inserted into subcarriers but also encoded as diversified subcarrier activation patterns (SAPs) simultaneously. Those encoded SAPs, though camouflaged by malicious signals, can be identified and decoded into original pilots for high-accuracy channel impulse response (CIR) estimation. The CTA security is first characterized by the error probability of identifying legitimate CIR estimation samples. The CTA instability is formulated as the function of probability of stably estimating CIR against all available diversified SAPs. A realistic tradeoff between the CTA security and instability under the discretely distributed AoA is identified and an optimally stable tradeoff problem is formulated, with the objective of optimizing the code rate to maximize security while maintaining maximum stability for ever. Solving this, we derive the closed-form expression of optimal code rate. Numerical results finally validate the resilience of proposed ICC-CTA protocol.
In this paper, incremental decode-and-forward (IDF) and incremental selective decode-and-forward (ISDF) relaying are proposed to improve the spectral efficiency of power line communication. Contrary to the traditional decode-and-forward (DF) relaying, IDF and ISDF strategies utilize the relay only if the direct link ceases to attain a certain information rate, thereby improving the spectral efficiency. The path gain through the power line is assumed to be log-normally distributed with high distance-dependent attenuation and the additive noise is from a Bernoulli-Gaussian process. Closed-form expressions for the outage probability, and approximate closed-form expressions for the end-to-end average channel capacity and the average bit error rate for binary phase-shift keying are derived. Furthermore, a closed-form expression for the fraction of times the relay is in use is derived as a measure of the spectral efficiency. Comparative analysis of IDF and ISDF with traditional DF relaying is presented. It is shown that IDF is a specific case of ISDF and can obtain optimal spectral efficiency without compromising the outage performance. By employing power allocation to minimize the outage probability, it is realized that the power should be allocated in accordance with the inter-node distances and channel parameters.
Physical layer security (PLS) techniques can help to protect wireless networks from eavesdropper attacks. In this paper, we consider the authentication technique that uses fingerprint embedding to defend 5G cellular networks with unmanned aerial vehicle (UAV) systems from eavesdroppers and intruders. Since the millimeter wave (mmWave) cellular networks use narrow and directional beams, PLS can take further advantage of the 3D spatial dimension for improving the authentication of UAV users. Considering a multi-user mmWave cellular network, we propose a power allocation technique that jointly takes into account splitting of the transmit power between the precoder and the authentication tag, which manages both the secrecy as well as the achievable rate. Our results show that we can obtain optimal achievable rate with expected secrecy.
We propose a method using a long short-term memory (LSTM) network to estimate the noise power spectral density (PSD) of single-channel audio signals represented in the short time Fourier transform (STFT) domain. An LSTM network common to all frequency bands is trained, which processes each frequency band individually by mapping the noisy STFT magnitude sequence to its corresponding noise PSD sequence. Unlike deep-learning-based speech enhancement methods that learn the full-band spectral structure of speech segments, the proposed method exploits the sub-band STFT magnitude evolution of noise with a long time dependency, in the spirit of the unsupervised noise estimators described in the literature. Speaker- and speech-independent experiments with different types of noise show that the proposed method outperforms the unsupervised estimators, and generalizes well to noise types that are not present in the training set.
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